,name,text,source_text,score,input,task,folder_name,model
0,vgg11_bn,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.133450906,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
1,regnet_y_3_2gf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.157051864,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2,regnet_x_3_2gf,regnet_x_3_2gf is used for image classification,regnet_x_3_2gf,1.0,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
3,vgg13_bn,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.121103422,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4,detection.fasterrcnn_mobilenet_v3_large_fpn,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.097638161,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
5,mnasnet0_5,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.224848931,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
6,vit_b_16,Constructs a RegNetY_800MF architecture,regnet_y_800mf,0.15017039,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
7,efficientnet_b2,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.215989122,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
8,mnasnet1_3,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.182201279,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
9,regnet_y_400mf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.103191359,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
10,detection.fasterrcnn_mobilenet_v3_large_fpn,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.21841462,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
11,googlenet,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.191250242,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
12,efficientnet_b6,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.089554714,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
13,regnet_y_16gf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.146781609,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
14,regnet_x_32gf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.131563352,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
15,googlenet,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.125144398,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
16,vit_b_32,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.149077295,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
17,alexnet,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.067953936,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
18,quantization.resnet50,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.154189216,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
19,quantization.shufflenet_v2_x0_5,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.100062514,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
20,video.mc3_18,ssd is a method for detecting objects in images using a single deep neural network,detection.ssd300_vgg16,0.189698087,1x32x32,video classification,Null,TV
21,convnext_large,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.105235099,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
22,mobilenet_v3_large,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.208956752,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
23,video.mc3_18,The key building block is an Inception Module,inception_v3,0.196830356,1x32x32,video classification,Null,TV
24,shufflenet_v2_x1_0,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.086398594,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
25,squeezenet1_0,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.127771215,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
26,vgg16_bn,The required minimum input size of the model is 32x32,vgg16_bn,1.0,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
27,resnet101,It is the simplest of all other vgg versions,vgg11,0.122435415,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
28,quantization.shufflenet_v2_x1_5,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.13325664,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
29,vit_l_32,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.141516287,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
30,quantization.resnet18,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.081846371,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
31,convnext_base,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.099782857,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
32,shufflenet_v2_x1_5,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.15624207,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
33,detection.fasterrcnn_mobilenet_v3_large_fpn,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.091743807,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
34,mnasnet1_3,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.144589156,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
35,vit_l_32,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.134061674,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
36,detection.fasterrcnn_mobilenet_v3_large_320_fpn,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.226771039,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
37,regnet_x_800mf,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.203237017,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
38,mnasnet0_5,mnasnet0_5 is used for image classification,mnasnet0_5,1.0,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
39,regnet_y_16gf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.080651881,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
40,video.r3d_18,r3d_18 is used for video classification,video.r3d_18,1.0,1x32x32,video classification,Null,TV
41,regnet_y_400mf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.187064552,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
42,shufflenet_v2_x1_5,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.176967918,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
43,detection.maskrcnn_resnet50_fpn,based on ResNeXt-50 as its backbone along with the feature Pyramid Network (FPN) for better accuracy and speed,detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
44,regnet_x_1_6gf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_x_1_6gf,1.0,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
45,resnet34,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.241823993,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
46,detection.ssd300_vgg16,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
47,detection.ssdlite320_mobilenet_v3_large,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels",vit_b_16,0.181133641,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
48,detection.fasterrcnn_mobilenet_v3_large_fpn,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.228878404,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
49,vgg13_bn,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.109953312,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
50,densenet121,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.062949178,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
51,vit_b_16,MobileNet v3-Large has 5.4 million parameters,mobilenet_v3_large,0.097987259,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
52,regnet_y_1_6gf,Replacing ReLU with GeLU,convnext_tiny,0.142199549,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
53,segmentation.fcn_resnet50,"Mask R-CNN uses anchor boxes to detect multiple objects, objects of different scales, and overlapping objects in an image",detection.maskrcnn_resnet50_fpn,0.21647215,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
54,regnet_y_400mf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.184895065,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
55,quantization.mobilenet_v2,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.129487135,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
56,shufflenet_v2_x2_0,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.122492008,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
57,vit_b_16,This network is a pretty large network and it has about 138 million parameters,vgg16,0.082941493,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
58,densenet169,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.157890158,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
59,squeezenet1_0,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.216031341,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
60,detection.fasterrcnn_mobilenet_v3_large_320_fpn,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.109880651,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
61,convnext_tiny,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.17926733,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
62,regnet_y_3_2gf,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_3_2gf,1.0,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
63,convnext_tiny,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.237820923,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
64,regnet_y_16gf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.106134031,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
65,densenet161,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.185948563,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
66,vit_l_16,"It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block",googlenet,0.167613388,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
67,regnet_x_3_2gf,adopted from BERT,vit_b_16,0.009857979,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
68,efficientnet_b1,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.172304782,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
69,convnext_base,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.1481133,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
70,quantization.mobilenet_v3_large,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.172604176,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
71,mnasnet1_3,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.028936681,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
72,wide_resnet101_2,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.180211534,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
73,segmentation.fcn_resnet50,It is the simplest of all other vgg versions,vgg11,0.126363774,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
74,efficientnet_b3,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.147813646,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
75,mobilenet_v3_large,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.186418787,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
76,mnasnet1_0,mnasnet1_0 is used for image classification,mnasnet1_0,1.0,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
77,resnext50_32x4d,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.231226359,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
78,efficientnet_b0,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.217790848,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
79,convnext_base,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.095720443,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
80,vit_b_32,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.12670389,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
81,mobilenet_v2,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.138040262,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
82,convnext_tiny,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.129976452,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
83,convnext_small,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.053190841,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
84,squeezenet1_0,"Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet)",squeezenet1_0,1.0,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
85,convnext_base,"Googlenet has 9 Inception modules, 22 layers deep (21 convolutional layers and one fully connected layer) when counting only layers with parameters (or 27 layers if we also count pooling) and 12 times fewer parameters than AlexNet",googlenet,0.1738869,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
86,quantization.googlenet,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.169068001,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
87,quantization.inception_v3,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_400mf,0.156793443,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
88,quantization.shufflenet_v2_x2_0,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",quantization.shufflenet_v2_x2_0,1.0,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
89,vgg16,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.173781253,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
90,video.r2plus1d_18,"In particular, in this section we use the 400 million flops (400MF) regime and train each sampled model for 10 epochs on the ImageNet dataset",regnet_y_400mf,0.178760692,1x32x32,video classification,Null,TV
91,quantization.mobilenet_v3_large,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.180651077,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
92,shufflenet_v2_x0_5,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.164288105,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
93,vit_b_16,MobileNetv3-Large is 27% faster than MobileNetV2 while maintaining similar mAP,mobilenet_v3_large,0.082288419,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
94,vgg11_bn,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.107588005,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
95,convnext_small,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.04634808,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
96,regnet_y_3_2gf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.182241359,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
97,mnasnet1_3,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.045187655,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
98,detection.keypointrcnn_resnet50_fpn,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.151572077,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
99,efficientnet_b3,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.169677338,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
100,mnasnet0_5,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.055576973,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
101,squeezenet1_1,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.210961955,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
102,quantization.shufflenet_v2_x2_0,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.126406914,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
103,segmentation.deeplabv3_mobilenet_v3_large,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.077800067,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
104,resnet34,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.178255984,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
105,segmentation.fcn_resnet50,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.179438236,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
106,regnet_y_8gf,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.168988571,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
107,regnet_x_8gf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.190745032,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
108,quantization.inception_v3,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.171944252,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
109,segmentation.fcn_resnet101,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.124215465,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
110,segmentation.lraspp_mobilenet_v3_large,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.162051302,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
111,vit_l_16,"ssdlite320_mobilenet_v3_large is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.ssdlite320_mobilenet_v3_large,0.197696114,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
112,quantization.shufflenet_v2_x1_0,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.224156983,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
113,detection.ssdlite320_mobilenet_v3_large,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.144089602,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
114,detection.keypointrcnn_resnet50_fpn,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.11691471,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
115,quantization.shufflenet_v2_x1_5,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.128349921,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
116,efficientnet_b3,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.129619543,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
117,resnet101,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.159051794,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
118,detection.fasterrcnn_mobilenet_v3_large_fpn,Constructs a high resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone,detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
119,mobilenet_v2,You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close,vit_b_32,0.151412528,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
120,detection.maskrcnn_resnet50_fpn,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.096379551,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
121,detection.ssdlite320_mobilenet_v3_large,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.157757244,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
122,shufflenet_v2_x2_0,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.181185006,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
123,resnext50_32x4d,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.124450551,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
124,mnasnet0_5,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.204328169,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
125,regnet_x_8gf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.108327187,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
126,shufflenet_v2_x2_0,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.151259169,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
127,shufflenet_v2_x0_5,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.126921,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
128,convnext_small,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.11692498,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
129,vgg19_bn,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.157045301,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
130,quantization.inception_v3,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",quantization.inception_v3,1.0,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
131,efficientnet_b5,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.156403061,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
132,regnet_x_16gf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.119431244,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
133,efficientnet_b0,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.116646837,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
134,detection.keypointrcnn_resnet50_fpn,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.142422582,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
135,resnet152,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.224465387,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
136,mobilenet_v2,MobileNetV2 is a convolutional neural network architecture that seeks to perform well on mobile devices,mobilenet_v2,1.0,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
137,vit_l_32,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.116000424,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
138,squeezenet1_0,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.08649532,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
139,video.r3d_18,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.213172417,1x32x32,video classification,Null,TV
140,detection.keypointrcnn_resnet50_fpn,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,1.0,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
141,vit_b_16,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,shufflenet_v2_x0_5,0.14268428,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
142,regnet_y_400mf,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.171179941,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
143,vit_b_16,"Inception network of googlenet stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid",googlenet,0.155183932,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
144,vgg13,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.123074458,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
145,shufflenet_v2_x1_5,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,shufflenet_v2_x1_5,1.0,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
146,resnet34,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.121393682,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
147,vgg19_bn,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
148,vit_b_32,Constructs a RegNetY_1.6GF architecture,regnet_y_1_6gf,0.170474151,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
149,regnet_y_400mf,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.109045506,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
150,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.094988809,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
151,efficientnet_b1,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.241362688,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
152,regnet_x_1_6gf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.17051769,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
153,regnet_y_1_6gf,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.168482205,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
154,regnet_y_400mf,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.15816875,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
155,segmentation.deeplabv3_mobilenet_v3_large,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.075714425,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
156,regnet_y_1_6gf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.154262464,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
157,squeezenet1_1,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.217410867,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
158,inception_v3,quantization.mobilenet_v3_large is the quantized model of mobilenet_v3_large,quantization.mobilenet_v3_large,0.182053793,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
159,mnasnet1_3,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.199003885,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
160,resnet34,They stack residual blocks ontop of each other to form network: for example  a ResNet-34 has 34 layers using these blocks,resnet34,1.0,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
161,efficientnet_b4,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.182502987,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
162,densenet201,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.122690065,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
163,resnet18,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.083350051,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
164,quantization.googlenet,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.108389283,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
165,mobilenet_v2,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.179915144,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
166,vit_b_16,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.111447285,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
167,vgg13,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.207076583,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
168,convnext_tiny,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.193613595,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
169,resnext101_32x8d,This model has 3.8 billion FLOPs,resnet50,0.133747146,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
170,vit_l_32,"It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block",googlenet,0.162247251,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
171,vgg19,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.166167843,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
172,video.r2plus1d_18,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.176035793,1x32x32,video classification,Null,TV
173,regnet_x_1_6gf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.152989368,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
174,resnet34,Replacing ReLU with GeLU,convnext_tiny,0.186919103,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
175,vit_l_32,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.144660018,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
176,detection.ssdlite320_mobilenet_v3_large,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.152895417,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
177,regnet_x_32gf,Constructs a RegNetX_32GF architecture,regnet_x_32gf,1.0,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
178,shufflenet_v2_x1_5,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.164085442,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
179,mobilenet_v3_small,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.158805192,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
180,vit_l_32,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.173752712,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
181,mobilenet_v3_small,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.228771741,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
182,quantization.shufflenet_v2_x1_5,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.183919465,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
183,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.072082762,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
184,segmentation.fcn_resnet101,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.036112815,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
185,convnext_large,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.172837604,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
186,vgg13_bn,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.174529873,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
187,convnext_small,The key building block is an Inception Module,inception_v3,0.111772086,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
188,efficientnet_b5,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.199891269,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
189,regnet_y_8gf,Constructs a RegNetY_8GF architecture,regnet_y_8gf,1.0,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
190,efficientnet_b3,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.189853877,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
191,mobilenet_v3_large,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.113681097,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
192,segmentation.deeplabv3_resnet101,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.155532666,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
193,shufflenet_v2_x0_5,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.142891543,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
194,mnasnet0_5,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.109915252,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
195,squeezenet1_0,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.165077666,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
196,detection.fasterrcnn_resnet50_fpn,"The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals",detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
197,regnet_y_8gf,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.138371687,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
198,regnet_y_3_2gf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.137627011,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
199,convnext_base,The densenet-161 is much larger at 100MB in size vs the densenet-121 model's roughly 31MB size,densenet161,0.163372215,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
200,resnet18,The required minimum input size of the model is 32x32,vgg11,0.131496474,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
201,efficientnet_b0,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.175413963,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
202,detection.keypointrcnn_resnet50_fpn,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.08691952,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
203,vgg11_bn,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.22297766,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
204,video.r3d_18,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.112362833,1x32x32,video classification,Null,TV
205,convnext_small,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.164742079,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
206,segmentation.fcn_resnet101,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.040361292,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
207,vit_l_16,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.100791475,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
208,convnext_small,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.223641747,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
209,regnet_y_16gf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.115869842,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
210,convnext_large,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.123723095,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
211,regnet_y_1_6gf,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_1_6gf,1.0,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
212,efficientnet_b6,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.176043959,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
213,convnext_base,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.182608676,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
214,efficientnet_b3,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.124326582,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
215,convnext_tiny,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.089191913,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
216,quantization.shufflenet_v2_x0_5,It is the simplest of all other vgg versions,vgg11,0.180333504,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
217,alexnet,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.176893227,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
218,squeezenet1_0,squeezenet1_0 is used for image classification,squeezenet1_0,1.0,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
219,vgg13_bn,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.187365154,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
220,regnet_y_3_2gf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.152869006,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
221,regnet_x_400mf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.120289673,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
222,vit_l_16,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x0_5,0.127401615,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
223,convnext_base,"Each 2-layer block is replaced in the 34-layer net with this 3-layer bottleneck block, resulting in a 50-layer ResNet",resnet50,0.175226055,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
224,mobilenet_v3_small,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.123694883,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
225,quantization.mobilenet_v3_large,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.227036485,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
226,regnet_x_1_6gf,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.153221547,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
227,regnet_x_32gf,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.119085686,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
228,quantization.resnext101_32x8d,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.116250286,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
229,convnext_tiny,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.069145613,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
230,regnet_x_8gf,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.182377067,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
231,segmentation.fcn_resnet101,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.145066247,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
232,regnet_x_400mf,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.170533907,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
233,segmentation.deeplabv3_resnet101,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.100531054,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
234,shufflenet_v2_x1_0,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.175151692,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
235,efficientnet_b4,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.191947153,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
236,detection.retinanet_resnet50_fpn,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.163730875,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
237,quantization.shufflenet_v2_x2_0,ReLU non-linearity as activation functions,vgg11,0.160404,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
238,mobilenet_v3_large,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.149628624,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
239,regnet_y_16gf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.168246464,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
240,regnet_y_16gf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.213280927,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
241,regnet_y_16gf,regnet_y_16gf is used for image classification,regnet_y_16gf,1.0,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
242,mobilenet_v3_large,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.193698487,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
243,mnasnet1_0,MNASNet with depth multiplier of 1.0,mnasnet1_0,1.0,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
244,inception_v3,You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close,vit_b_32,0.125771243,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
245,googlenet,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.145604956,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
246,mobilenet_v3_small,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.159699595,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
247,mnasnet1_3,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.193028495,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
248,mnasnet0_5,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.149889089,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
249,vit_l_16,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.059499273,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
250,vgg13,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.109253482,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
251,segmentation.lraspp_mobilenet_v3_large,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.229553112,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
252,alexnet,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.100419335,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
253,regnet_y_8gf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.162411473,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
254,detection.fasterrcnn_resnet50_fpn,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
255,densenet121,VGG19 has 19.6 billion FLOPs,vgg19,0.138268709,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
256,resnext50_32x4d,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.159276402,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
257,regnet_y_16gf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.108787985,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
258,mnasnet0_75,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.134221941,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
259,detection.keypointrcnn_resnet50_fpn,adopted from BERT,vit_b_16,0.00882221,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
260,quantization.googlenet,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.094563182,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
261,quantization.mobilenet_v2,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.176155406,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
262,segmentation.fcn_resnet101,VGG19 has 19.6 billion FLOPs,vgg19,0.028638212,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
263,mnasnet1_0,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.175921188,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
264,resnet34,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.236113655,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
265,vgg13,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.19686763,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
266,mobilenet_v3_large,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.165970625,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
267,video.r2plus1d_18,The number of parameters is 3.4 million,mobilenet_v2,0.147492777,1x32x32,video classification,Null,TV
268,detection.maskrcnn_resnet50_fpn,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
269,mnasnet1_3,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.038034774,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
270,quantization.mobilenet_v3_large,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.118944257,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
271,shufflenet_v2_x0_5,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.129369106,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
272,segmentation.lraspp_mobilenet_v3_large,resnext50_32x4d is used for image classification,resnext50_32x4d,0.196737714,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
273,quantization.shufflenet_v2_x1_0,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.151998287,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
274,detection.maskrcnn_resnet50_fpn,2D max pooling in between the weight layers,vgg11,0.198697003,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
275,mobilenet_v2,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.139901683,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
276,convnext_small,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.1006793,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
277,vgg11,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.190534993,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
278,vgg16,Constructs a RegNetX_1.6GF architecture,regnet_x_1_6gf,0.234517658,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
279,quantization.googlenet,The number of parameters is 3.4 million,mobilenet_v2,0.123153252,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
280,shufflenet_v2_x0_5,ReLU non-linearity as activation functions,vgg11,0.141871515,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
281,mobilenet_v3_large,MobileNet v3-Large has 5.4 million parameters,mobilenet_v3_large,1.0,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
282,segmentation.lraspp_mobilenet_v3_large,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.169525434,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
283,efficientnet_b7,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.169858697,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
284,detection.retinanet_resnet50_fpn,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
285,detection.keypointrcnn_resnet50_fpn,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.122601426,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
286,vgg13,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.114123058,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
287,efficientnet_b0,Replacing ReLU with GeLU,convnext_tiny,0.088452992,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
288,resnet18,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,1.0,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
289,convnext_base,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.132938975,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
290,efficientnet_b1,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.183665523,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
291,regnet_x_8gf,regnet_x_8gf is used for image classification,regnet_x_8gf,1.0,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
292,segmentation.fcn_resnet50,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.036899075,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
293,video.r3d_18,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.193688863,1x32x32,video classification,Null,TV
294,vit_b_16,The densenet-201 is larger at over 77MB in size vs the densenet-121 model's roughly 31MB size,densenet201,0.110281999,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
295,vgg13_bn,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.167388832,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
296,regnet_y_16gf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.180160625,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
297,efficientnet_b3,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.167734411,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
298,regnet_x_400mf,"The RegNet is composed of a stem, body and head",regnet_x_400mf,1.0,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
299,efficientnet_b2,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.122336703,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
300,regnet_x_16gf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.15262852,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
301,regnet_x_3_2gf,The required minimum input size of the model is 17x17,squeezenet1_1,0.191830696,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
302,regnet_y_16gf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.120769678,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
303,segmentation.fcn_resnet50,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.243353312,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
304,convnext_large,VGG19 has 19.6 billion FLOPs,vgg19,0.142281087,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
305,vgg13_bn,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.192076198,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
306,mobilenet_v3_small,Replacing ReLU with GeLU,convnext_tiny,0.030008913,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
307,inception_v3,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.121632366,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
308,segmentation.deeplabv3_resnet50,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.181337896,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
309,quantization.mobilenet_v2,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.175044765,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
310,convnext_tiny,VGG19 has 19.6 billion FLOPs,vgg19,0.141101778,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
311,regnet_x_800mf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.175836533,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
312,shufflenet_v2_x2_0,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.10135577,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
313,quantization.resnext101_32x8d,adopted from BERT,vit_b_16,0.001566545,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
314,vit_b_16,It works similarly to Faster R-CNN with ResNet-50 FPN backbone,detection.fasterrcnn_mobilenet_v3_large_fpn,0.143293881,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
315,resnet152,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.194759352,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
316,video.mc3_18,adopted from BERT,vit_b_16,0.0,1x32x32,video classification,Null,TV
317,segmentation.deeplabv3_resnet50,vit_b_16 is used for image classification,vit_b_16,0.141892244,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
318,shufflenet_v2_x1_5,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.128673319,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
319,segmentation.fcn_resnet50,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.032848928,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
320,regnet_x_3_2gf,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.229430522,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
321,vgg11,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.097700505,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
322,vgg11,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.181192355,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
323,convnext_tiny,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.153635306,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
324,efficientnet_b7,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.156372673,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
325,convnext_tiny,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.115712645,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
326,convnext_base,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.083401533,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
327,quantization.mobilenet_v3_large,It is the simplest of all other vgg versions,vgg11,0.192180397,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
328,mnasnet1_3,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.119839924,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
329,inception_v3,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.089896298,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
330,video.r2plus1d_18,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.153839464,1x32x32,video classification,Null,TV
331,vgg11_bn,"MnasNet is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency",mnasnet0_5,0.161000046,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
332,vit_b_32,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.128337354,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
333,shufflenet_v2_x1_5,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,1.0,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
334,detection.maskrcnn_resnet50_fpn,"The loss function of Faster R-CNN is updated with the mask loss and as in FPN, it uses 5 anchor boxes with 3 aspect ratio",detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
335,regnet_x_16gf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_x_16gf,1.0,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
336,densenet169,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.047010071,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
337,convnext_small,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.055139542,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
338,regnet_x_400mf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.179861847,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
339,quantization.mobilenet_v2,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.mobilenet_v2,1.0,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
340,mnasnet0_75,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.195258468,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
341,convnext_large,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.137192943,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
342,mnasnet1_0,The key building block is an Inception Module,inception_v3,0.153634311,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
343,detection.retinanet_resnet50_fpn,Constructs a RetinaNet model with a ResNet-50-FPN backbone,detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
344,mnasnet1_3,adopted from BERT,vit_b_16,0.000805243,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
345,efficientnet_b2,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.189197379,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
346,resnet50,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.076671952,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
347,regnet_y_1_6gf,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.146234883,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
348,mobilenet_v3_small,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.194180667,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
349,shufflenet_v2_x0_5,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.109910122,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
350,segmentation.deeplabv3_mobilenet_v3_large,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.168160518,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
351,resnet34,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.166229328,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
352,detection.maskrcnn_resnet50_fpn,The required minimum input size of the model is 17x17,squeezenet1_1,0.18601291,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
353,segmentation.lraspp_mobilenet_v3_large,"60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax",alexnet,0.177021267,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
354,quantization.resnet18,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.198250324,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
355,quantization.inception_v3,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.211876978,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
356,regnet_y_1_6gf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.116662563,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
357,convnext_tiny,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.032596867,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
358,quantization.inception_v3,Avoiding the use of dense layers means less parameters (making the networks faster to train),segmentation.fcn_resnet50,0.154836204,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
359,quantization.resnet50,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.231331238,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
360,vgg13_bn,ResNet-152 can obtain 4.49% error rate,resnet152,0.174467404,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
361,wide_resnet50_2,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.146810369,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
362,vgg11,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.190876455,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
363,quantization.shufflenet_v2_x2_0,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.shufflenet_v2_x2_0,1.0,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
364,vit_l_32,"The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions",detection.fasterrcnn_resnet50_fpn,0.203281674,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
365,quantization.inception_v3,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet18,0.214782067,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
366,regnet_x_3_2gf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.154351589,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
367,wide_resnet101_2,VGG19 has 19.6 billion FLOPs,vgg19,0.105021825,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
368,video.mc3_18,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.145579704,1x32x32,video classification,Null,TV
369,mnasnet1_3,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.144589156,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
370,convnext_small,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.220342015,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
371,vgg16,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.163733451,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
372,vit_l_32,They stack residual blocks ontop of each other to form network: for example  a ResNet-34 has 34 layers using these blocks,resnet34,0.137371199,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
373,resnext50_32x4d,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.193985265,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
374,mnasnet1_0,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.089532546,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
375,regnet_y_16gf,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.155466991,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
376,efficientnet_b6,The number of parameters is 3.4 million,mobilenet_v2,0.145902481,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
377,densenet121,DenseNet-121 has 120 Convolutions and 4 AvgPool,densenet121,1.0,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
378,vit_b_32,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.079815747,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
379,vit_l_16,"As a whole, Faster R-CNN consists of two modules",detection.fasterrcnn_resnet50_fpn,0.202788163,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
380,vit_l_16,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.101356156,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
381,detection.retinanet_resnet50_fpn,The original dataset contained images of size 512x512x1,resnet101,0.148495601,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
382,convnext_base,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,1.0,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
383,convnext_small,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.024913577,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
384,convnext_base,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.071139322,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
385,mnasnet0_75,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.140648571,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
386,shufflenet_v2_x0_5,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.125914547,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
387,detection.ssdlite320_mobilenet_v3_large,VGG19 has 19.6 billion FLOPs,vgg19,0.092079839,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
388,inception_v3,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.062501722,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
389,alexnet,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.131250835,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
390,segmentation.fcn_resnet101,The number of parameters is 3.4 million,mobilenet_v2,0.041758634,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
391,segmentation.lraspp_mobilenet_v3_large,The original dataset contained images of size 512x512x1,resnet101,0.127250898,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
392,regnet_y_32gf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.202036968,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
393,convnext_base,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.153586224,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
394,quantization.resnet50,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",quantization.resnet50,1.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
395,shufflenet_v2_x0_5,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.176150902,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
396,wide_resnet50_2,Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks,wide_resnet50_2,1.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
397,shufflenet_v2_x1_0,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.126794221,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
398,mnasnet1_3,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.122932628,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
399,densenet201,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.127982172,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
400,detection.maskrcnn_resnet50_fpn,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.080767286,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
401,mnasnet1_0,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.199992175,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
402,vgg16_bn,"MnasNet is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency",mnasnet0_5,0.175909241,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
403,mnasnet0_5,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.08947327,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
404,mobilenet_v3_large,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.09477623,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
405,vgg19,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
406,mobilenet_v3_small,ReLU non-linearity as activation functions,vgg11,0.093107269,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
407,efficientnet_b0,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.12049972,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
408,resnext50_32x4d,The required minimum input size of the model is 21x21,squeezenet1_0,0.219076723,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
409,detection.ssd300_vgg16,The key building block is an Inception Module,inception_v3,0.139678822,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
410,densenet121,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.218373778,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
411,shufflenet_v2_x1_5,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.113973787,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
412,quantization.shufflenet_v2_x1_5,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.140584965,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
413,convnext_tiny,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,1.0,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
414,densenet169,Replacing ReLU with GeLU,convnext_tiny,0.074050246,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
415,detection.fasterrcnn_mobilenet_v3_large_fpn,Proposed a new layer called ROI Pooling that extracts equal-length feature vectors from all proposals (  ROIs) in the same image,detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
416,quantization.resnet50,"Each 2-layer block is replaced in the 34-layer net with this 3-layer bottleneck block, resulting in a 50-layer ResNet",quantization.resnet50,1.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
417,convnext_tiny,"It has 11 weight layers in total, so the name VGG11",vgg11,0.211065986,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
418,shufflenet_v2_x2_0,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.103791612,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
419,mnasnet0_75,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.054491771,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
420,shufflenet_v2_x1_5,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.138348221,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
421,convnext_small,"The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks",efficientnet_b0,0.144655999,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
422,resnet50,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.171358999,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
423,vgg11_bn,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.166079076,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
424,regnet_y_32gf,adopted from BERT,vit_b_16,0.016329053,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
425,vit_b_16,Replacing ReLU with GeLU,convnext_tiny,0.126755699,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
426,segmentation.deeplabv3_mobilenet_v3_large,The required minimum input size of the model is 17x17,squeezenet1_1,0.19938118,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
427,regnet_y_1_6gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.197352086,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
428,vgg16,Replacing ReLU with GeLU,convnext_tiny,0.145376983,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
429,detection.keypointrcnn_resnet50_fpn,The squeezing (bottleneck layer) and expansion behavior is common in neural architectures,squeezenet1_1,0.217593229,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
430,detection.keypointrcnn_resnet50_fpn,This model has 3.8 billion FLOPs,resnet50,0.084988367,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
431,mobilenet_v2,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.175875401,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
432,regnet_y_800mf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.178382917,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
433,mnasnet1_3,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.14510108,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
434,quantization.resnext101_32x8d,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.223403114,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
435,inception_v3,Constructs a RegNetY_1.6GF architecture,regnet_y_1_6gf,0.250796768,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
436,convnext_small,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.090247001,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
437,wide_resnet101_2,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.171764739,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
438,vgg19_bn,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
439,quantization.shufflenet_v2_x0_5,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.07376405,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
440,regnet_x_32gf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_x_32gf,1.0,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
441,vit_l_32,adopted from BERT,vit_l_32,1.0,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
442,vgg16,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.185849749,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
443,wide_resnet50_2,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.198226373,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
444,mnasnet0_75,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.062175365,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
445,efficientnet_b3,It is the simplest of all other vgg versions,vgg11,0.173738258,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
446,convnext_tiny,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.17747467,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
447,quantization.googlenet,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.120186814,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
448,segmentation.deeplabv3_resnet101,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.17555308,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
449,quantization.mobilenet_v2,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,1.0,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
450,squeezenet1_1,Constructs a low resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone tuned for mobile use-cases,detection.fasterrcnn_mobilenet_v3_large_320_fpn,0.261480751,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
451,regnet_x_800mf,RegNetX_800MF is used for image classification,regnet_x_800mf,1.0,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
452,detection.ssdlite320_mobilenet_v3_large,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.137440639,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
453,wide_resnet50_2,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.154929645,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
454,quantization.resnet18,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.067971625,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
455,vgg13,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.193961006,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
456,segmentation.deeplabv3_resnet50,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.176804615,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
457,regnet_x_400mf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.159695799,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
458,shufflenet_v2_x0_5,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.137915947,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
459,googlenet,The required minimum input size of the model is 29x29,densenet121,0.133076174,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
460,vit_b_16,Constructs a RegNetY_400MF architecture,regnet_y_400mf,0.160900027,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
461,squeezenet1_1,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.162152233,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
462,resnet18,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.133523006,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
463,vit_l_32,Proposing region proposal network (RPN) which is a fully convolutional network that generates proposals with various scales and aspect ratios,detection.fasterrcnn_resnet50_fpn,0.142127982,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
464,resnext101_32x8d,The required minimum input size of the model is 21x21,squeezenet1_0,0.208565876,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
465,efficientnet_b1,Note that this model does not include any fine-tuned heads,vit_b_16,0.166895512,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
466,video.r3d_18,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.187094605,1x32x32,video classification,Null,TV
467,mnasnet0_5,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.148776874,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
468,vit_b_16,"The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions",detection.fasterrcnn_resnet50_fpn,0.19906065,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
469,vit_b_16,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.108911878,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
470,quantization.resnext101_32x8d,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.145681048,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
471,shufflenet_v2_x0_5,adopted from BERT,vit_b_16,0.0018218,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
472,regnet_y_400mf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,1.0,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
473,squeezenet1_0,VGG19 has 19.6 billion FLOPs,vgg19,0.092299791,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
474,video.r3d_18,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.145177574,1x32x32,video classification,Null,TV
475,detection.keypointrcnn_resnet50_fpn,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.097203073,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
476,quantization.shufflenet_v2_x0_5,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.shufflenet_v2_x0_5,1.0,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
477,convnext_base,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.129228532,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
478,detection.ssdlite320_mobilenet_v3_large,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.122960368,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
479,quantization.shufflenet_v2_x2_0,The main building block is a ShuffleNet v2 Block,quantization.shufflenet_v2_x2_0,1.0,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
480,regnet_x_1_6gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.168675403,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
481,resnet152,They use option 2 for increasing dimensions,resnet50,0.196010296,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
482,segmentation.fcn_resnet50,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.138263704,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
483,segmentation.deeplabv3_resnet101,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.133347182,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
484,vgg16_bn,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.185228157,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
485,vit_l_16,vit_l_16 is used for image classification,vit_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
486,vgg13_bn,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.136371249,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
487,regnet_x_8gf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.202408478,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
488,regnet_x_800mf,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.205933463,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
489,inception_v3,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.107903736,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
490,vit_b_32,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
491,shufflenet_v2_x1_5,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.127288604,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
492,segmentation.deeplabv3_mobilenet_v3_large,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.110921835,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
493,mnasnet0_75,The required minimum input size of the model is 17x17,squeezenet1_1,0.230026205,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
494,quantization.inception_v3,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.138316341,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
495,video.r3d_18,The number of parameters is 3.4 million,mobilenet_v2,0.140219512,1x32x32,video classification,Null,TV
496,regnet_x_1_6gf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.076702464,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
497,video.r2plus1d_18,"ssdlite320_mobilenet_v3_large is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.ssdlite320_mobilenet_v3_large,0.138587604,1x32x32,video classification,Null,TV
498,regnet_y_16gf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.16281233,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
499,inception_v3,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.243298923,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
500,vgg19_bn,"MnasNet is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency",mnasnet0_5,0.185559099,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
501,convnext_large,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.207163119,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
502,resnext101_32x8d,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.115388043,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
503,regnet_x_16gf,The required minimum input size of the model is 32x32,vgg11,0.186964466,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
504,convnext_large,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_large,1.0,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
505,efficientnet_b6,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.07165395,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
506,inception_v3,This network is a pretty large network and it has about 138 million parameters,vgg16,0.115949301,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
507,alexnet,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.147814242,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
508,mnasnet1_3,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.205184221,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
509,googlenet,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.074039237,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
510,segmentation.deeplabv3_resnet50,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.164420539,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
511,inception_v3,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.143219133,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
512,segmentation.fcn_resnet50,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.124989516,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
513,vgg16,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.159752889,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
514,shufflenet_v2_x2_0,VGG19 has 19.6 billion FLOPs,vgg19,0.114050105,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
515,detection.ssdlite320_mobilenet_v3_large,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.18940394,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
516,vit_l_16,"We show that MC ResNets yield roughly a 3-4% gain in clip-level accuracy over 2D ResNets of comparable capacity and they match the performance of 3D ResNets, which have 3 times as many parameters",video.mc3_18,0.083768911,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
517,resnext101_32x8d,11 weight layers (convolutional + fully connected),vgg11,0.262616299,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
518,mobilenet_v2,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.151866498,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
519,googlenet,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.195684083,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
520,quantization.shufflenet_v2_x2_0,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.163403984,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
521,quantization.inception_v3,This issue was later solved by using better backbone architectures like ResNet and other small fixes,detection.ssd300_vgg16,0.163293121,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
522,quantization.mobilenet_v3_large,2D max pooling in between the weight layers,vgg11,0.223230002,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
523,vgg16,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.165205552,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
524,convnext_small,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.090815767,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
525,quantization.shufflenet_v2_x2_0,"It builds upon ShuffleNet v1, which utilised pointwise group convolutions, bottleneck-like structures, and a channel shuffle operation",quantization.shufflenet_v2_x2_0,1.0,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
526,vit_b_16,Constructs a DeepLabV3 model with a MobileNetV3-Large backbone,segmentation.deeplabv3_mobilenet_v3_large,0.134219867,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
527,shufflenet_v2_x1_0,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.105678,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
528,convnext_large,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.114138941,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
529,squeezenet1_1,based on ResNeXt-50 as its backbone along with the feature Pyramid Network (FPN) for better accuracy and speed,detection.maskrcnn_resnet50_fpn,0.230170076,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
530,regnet_y_1_6gf,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_1_6gf,1.0,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
531,regnet_y_800mf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.128189802,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
532,resnet152,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.168774892,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
533,regnet_y_1_6gf,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.046257791,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
534,resnet101,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.146985195,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
535,mnasnet1_3,The key building block is an Inception Module,inception_v3,0.184142898,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
536,inception_v3,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,1.0,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
537,convnext_tiny,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.225931248,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
538,mobilenet_v2,It is the simplest of all other vgg versions,vgg11,0.167983264,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
539,resnext50_32x4d,ResNeXt is a homogeneous neural network which reduces the number of hyper parameters required by conventional ResNet,resnext50_32x4d,1.0,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
540,regnet_y_8gf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.12385917,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
541,regnet_x_3_2gf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.152591115,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
542,segmentation.deeplabv3_resnet50,The required minimum input size of the model is 21x21,squeezenet1_0,0.175404574,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
543,densenet201,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.196571765,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
544,detection.ssdlite320_mobilenet_v3_large,"The number of channels in outer 1x1 convolutions is the same, Wide ResNet-50-2 has 2048-1024-2048 channels",wide_resnet50_2,0.157755826,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
545,efficientnet_b2,efficientnet_b2 is used for image classification,efficientnet_b2,1.0,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
546,detection.keypointrcnn_resnet50_fpn,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.138620444,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
547,regnet_y_1_6gf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.163070403,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
548,shufflenet_v2_x2_0,It is the simplest of all other vgg versions,vgg11,0.169006659,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
549,inception_v3,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.206425739,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
550,regnet_x_8gf,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.177398536,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
551,quantization.shufflenet_v2_x1_5,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.112602441,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
552,vgg19,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.169095135,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
553,resnet152,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.154283135,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
554,wide_resnet50_2,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.146335799,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
555,segmentation.deeplabv3_resnet50,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.085594165,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
556,vit_b_16,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.139434272,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
557,resnet50,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.173256488,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
558,segmentation.deeplabv3_resnet50,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels",vit_b_16,0.171286033,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
559,regnet_y_16gf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.061447185,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
560,regnet_x_1_6gf,regnet_x_1_6gf is used for image classification,regnet_x_1_6gf,1.0,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
561,resnet152,The required minimum input size of the model is 32x32,vgg11,0.158558237,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
562,detection.maskrcnn_resnet50_fpn,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.12086407,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
563,vgg13_bn,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.133503928,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
564,shufflenet_v2_x0_5,"It builds upon ShuffleNet v1, which utilised pointwise group convolutions, bottleneck-like structures, and a channel shuffle operation",shufflenet_v2_x0_5,1.0,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
565,quantization.mobilenet_v3_large,"(2+1)D  convolutional block, which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution",video.r2plus1d_18,0.191979272,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
566,regnet_y_8gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.184868833,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
567,regnet_x_16gf,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.171845961,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
568,efficientnet_b2,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.171035777,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
569,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.072487621,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
570,shufflenet_v2_x1_5,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.130823303,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
571,convnext_base,VGG19 has 19.6 billion FLOPs,vgg19,0.125879984,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
572,regnet_y_32gf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.178347746,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
573,mobilenet_v3_small,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.099130824,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
574,detection.keypointrcnn_resnet50_fpn,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.170529622,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
575,vgg11_bn,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.135321843,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
576,mnasnet0_75,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.157792995,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
577,segmentation.lraspp_mobilenet_v3_large,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.19246808,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
578,vgg11,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
579,mnasnet0_75,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.076912324,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
580,mobilenet_v3_large,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,1.0,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
581,vgg19_bn,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.145510821,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
582,mnasnet1_0,Note that this model does not include any fine-tuned heads,vit_b_16,0.156312349,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
583,resnet18,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.087252992,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
584,vgg13,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.162467461,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
585,regnet_y_16gf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.153661681,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
586,regnet_x_3_2gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.216606213,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
587,mnasnet1_3,ReLU non-linearity as activation functions,vgg11,0.102400879,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
588,mnasnet1_3,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.2392981,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
589,quantization.mobilenet_v3_large,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.126615717,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
590,resnet34,They use option 2 for increasing dimensions,resnet50,0.199785694,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
591,convnext_base,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.087754215,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
592,regnet_y_400mf,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.133006872,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
593,resnet34,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.193090055,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
594,resnet18,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.112190648,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
595,vit_b_16,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.192794277,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
596,vgg19,The key building block is an Inception Module,inception_v3,0.107029773,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
597,shufflenet_v2_x0_5,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.146304628,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
598,inception_v3,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.123122022,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
599,regnet_x_400mf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.163555622,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
600,wide_resnet50_2,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.116952899,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
601,alexnet,Alexnet is used for image classification,alexnet,1.0,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
602,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.118630768,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
603,vit_l_16,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.105728955,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
604,vgg13_bn,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.133281808,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
605,regnet_x_16gf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.156502001,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
606,regnet_x_32gf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.195533443,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
607,efficientnet_b2,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.16092231,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
608,quantization.inception_v3,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.194503061,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
609,inception_v3,adopted from BERT,vit_b_16,0.011728815,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
610,quantization.shufflenet_v2_x1_5,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.187793137,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
611,regnet_x_400mf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.172492045,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
612,vgg16_bn,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.224931089,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
613,detection.ssdlite320_mobilenet_v3_large,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.132596938,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
614,mnasnet1_3,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.116022338,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
615,resnet18,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.098177549,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
616,densenet201,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.18045187,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
617,detection.ssdlite320_mobilenet_v3_large,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.062049404,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
618,inception_v3,VGG19 has 19.6 billion FLOPs,vgg19,0.114609404,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
619,densenet201,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.141147238,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
620,quantization.shufflenet_v2_x0_5,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.171664199,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
621,mobilenet_v3_small,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.234356443,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
622,video.r3d_18,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.154340797,1x32x32,video classification,Null,TV
623,regnet_y_3_2gf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.117072035,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
624,convnext_large,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.070824304,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
625,shufflenet_v2_x0_5,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.172581531,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
626,shufflenet_v2_x1_5,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.138341715,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
627,shufflenet_v2_x0_5,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.126085712,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
628,densenet169,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.173201696,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
629,regnet_x_400mf,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.159007766,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
630,regnet_x_1_6gf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.133347715,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
631,video.mc3_18,"In particular, in this section we use the 400 million flops (400MF) regime and train each sampled model for 10 epochs on the ImageNet dataset",regnet_y_400mf,0.193423835,1x32x32,video classification,Null,TV
632,vgg11,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.161568464,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
633,quantization.mobilenet_v2,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.169604721,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
634,video.r2plus1d_18,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.185796928,1x32x32,video classification,Null,TV
635,regnet_x_32gf,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.163284058,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
636,efficientnet_b1,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.171337342,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
637,convnext_small,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.063016712,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
638,squeezenet1_1,The required minimum input size of the model is 17x17,squeezenet1_1,1.0,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
639,regnet_y_400mf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.18302151,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
640,segmentation.lraspp_mobilenet_v3_large,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.159960774,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
641,segmentation.fcn_resnet101,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.068932974,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
642,vgg11_bn,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.143229265,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
643,mnasnet0_75,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.162737561,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
644,regnet_y_32gf,"The RegNet is composed of a stem, body and head",regnet_y_32gf,1.0,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
645,vit_l_16,"Each layer from the FPN is passed to the subnets, enabling it to detect objects as various scales",detection.retinanet_resnet50_fpn,0.200198952,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
646,regnet_x_800mf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.077945879,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
647,quantization.resnext101_32x8d,Replacing ReLU with GeLU,convnext_tiny,0.193438015,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
648,video.r2plus1d_18,r2plus1d_18 is used for video classification,video.r2plus1d_18,1.0,1x32x32,video classification,Null,TV
649,densenet121,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,0.231494906,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
650,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.156415477,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
651,regnet_x_32gf,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.165670638,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
652,quantization.googlenet,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.110767758,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
653,detection.maskrcnn_resnet50_fpn,VGG19 has 19.6 billion FLOPs,vgg19,0.087172298,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
654,resnet101,The original dataset contained images of size 512x512x1,resnet101,1.0,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
655,convnext_large,The key building block is an Inception Module,inception_v3,0.108678452,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
656,quantization.shufflenet_v2_x1_0,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.224489713,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
657,resnext50_32x4d,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.143461104,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
658,detection.keypointrcnn_resnet50_fpn,"1x1 Convolution, Batch Normalization, Convolution, Depth wise Separable Convolution, Dropout, Global Average Pooling, Inverted Residual Block, Residual Connection, ReLU, Max Pooling, Softmax, Squeeze-and-Excitation Block",mnasnet1_0,0.132165502,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
659,densenet161,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.196674968,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
660,mobilenet_v3_large,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.111095237,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
661,resnet101,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.190630938,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
662,alexnet,"Two GPUs, Rectified Linear Units (ReLUs), Local Response Normalization (LRN), Overlapping Pooling, Data augmentation, DropoutAlexNet was a deep neural network",alexnet,1.0,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
663,vgg11,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.170393825,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
664,convnext_tiny,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.232567676,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
665,segmentation.deeplabv3_resnet50,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.17557227,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
666,vgg13,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.132829681,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
667,regnet_y_16gf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.159864377,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
668,resnext50_32x4d,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.19555838,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
669,segmentation.deeplabv3_resnet50,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.10124372,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
670,quantization.resnext101_32x8d,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.216095693,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
671,convnext_base,MNASNet with depth multiplier of 1.3,mnasnet1_3,0.217039041,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
672,detection.ssdlite320_mobilenet_v3_large,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.21239312,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
673,detection.fasterrcnn_mobilenet_v3_large_fpn,"Dense blocks, Batch Normalization(BN)",densenet121,0.137392553,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
674,vit_l_32,This issue was later solved by using better backbone architectures like ResNet and other small fixes,detection.ssd300_vgg16,0.118829775,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
675,googlenet,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.087664685,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
676,detection.ssd300_vgg16,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.132194471,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
677,segmentation.fcn_resnet50,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.038112952,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
678,quantization.shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,1.0,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
679,vit_b_32,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.09999399,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
680,mnasnet1_3,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.203639751,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
681,convnext_small,SqueezeNet 1.0 achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters,squeezenet1_0,0.13847726,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
682,regnet_y_400mf,Replacing ReLU with GeLU,convnext_tiny,0.146920541,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
683,convnext_small,Wide ResNet-50-2 Wide Residual Networks,wide_resnet50_2,0.196633648,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
684,efficientnet_b5,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.111330978,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
685,vgg13_bn,Replacing ReLU with GeLU,convnext_tiny,0.14490472,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
686,quantization.shufflenet_v2_x0_5,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.116258848,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
687,detection.retinanet_resnet50_fpn,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.097106099,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
688,quantization.mobilenet_v3_large,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.15593362,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
689,regnet_x_1_6gf,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_x_1_6gf,1.0,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
690,detection.ssdlite320_mobilenet_v3_large,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,1.0,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
691,vgg16_bn,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.138394569,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
692,regnet_y_400mf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.148679556,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
693,regnet_x_1_6gf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.172018227,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
694,resnext101_32x8d,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.223635595,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
695,detection.ssd300_vgg16,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.112004865,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
696,convnext_small,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.241709364,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
697,efficientnet_b0,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,1.0,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
698,convnext_large,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.054997125,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
699,vgg19_bn,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.174600461,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
700,segmentation.fcn_resnet101,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.145611513,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
701,quantization.shufflenet_v2_x0_5,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.179396609,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
702,segmentation.fcn_resnet101,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.176617253,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
703,mnasnet0_75,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.210960731,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
704,vit_b_16,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.105961906,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
705,detection.ssdlite320_mobilenet_v3_large,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.083056426,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
706,regnet_y_32gf,Constructs a RegNetY_32GF architecture,regnet_y_32gf,1.0,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
707,regnet_y_400mf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.202732664,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
708,video.mc3_18,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.11809993,1x32x32,video classification,Null,TV
709,convnext_small,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.112693526,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
710,shufflenet_v2_x1_0,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.124433435,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
711,wide_resnet50_2,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.147384682,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
712,regnet_y_3_2gf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_3_2gf,1.0,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
713,vgg11_bn,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.149309477,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
714,vit_l_32,The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block,wide_resnet50_2,0.171334695,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
715,vit_l_32,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.118621789,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
716,regnet_x_800mf,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.168308774,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
717,detection.retinanet_resnet50_fpn,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.192987402,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
718,convnext_small,quantization.mobilenet_v3_large is the quantized model of mobilenet_v3_large,quantization.mobilenet_v3_large,0.151565329,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
719,quantization.mobilenet_v3_large,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.167553976,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
720,resnet34,Resnet34 is a 34 layer convolutional neural network that can be utilized as a state-of-the-art image classification model,resnet34,1.0,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
721,convnext_base,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.12002584,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
722,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection",detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
723,convnext_large,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.178390903,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
724,densenet169,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.153185819,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
725,shufflenet_v2_x2_0,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.174077318,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
726,mobilenet_v3_large,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.208970922,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
727,mobilenet_v3_large,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.161222023,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
728,segmentation.fcn_resnet101,Constructs a Fully-Convolutional Network model with a ResNet-101 backbone,segmentation.fcn_resnet101,1.0,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
729,detection.ssdlite320_mobilenet_v3_large,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.162615627,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
730,regnet_x_8gf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.1764684,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
731,vit_l_32,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.17283812,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
732,efficientnet_b1,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.188612295,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
733,efficientnet_b1,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.148932067,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
734,segmentation.deeplabv3_mobilenet_v3_large,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.180511625,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
735,vgg11,adopted from BERT,vit_b_16,0.002671752,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
736,vit_b_16,The key building block is an Inception Module,inception_v3,0.156901132,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
737,segmentation.deeplabv3_resnet50,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.170011493,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
738,regnet_y_32gf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.192954314,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
739,quantization.inception_v3,ResNeXt is a homogeneous neural network which reduces the number of hyper parameters required by conventional ResNet,resnext50_32x4d,0.178634271,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
740,mnasnet1_3,Relative fast due to no fully connected layers or a refinement block,segmentation.fcn_resnet50,0.174211587,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
741,vit_l_32,densenet169 is More acurate than DenseNet-121,densenet169,0.133632021,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
742,squeezenet1_0,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.197154918,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
743,regnet_x_3_2gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.165107512,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
744,inception_v3,MNASNet with depth multiplier of 0.75,mnasnet0_75,0.253161232,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
745,densenet169,It is the simplest of all other vgg versions,vgg11,0.179080315,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
746,segmentation.deeplabv3_resnet101,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.154390355,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
747,vit_l_16,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.101704408,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
748,detection.keypointrcnn_resnet50_fpn,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.066561967,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
749,alexnet,The required minimum input size of the model is 32x32,vgg11,0.212312857,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
750,vit_b_32,"Inception v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using Label Smoothing, Factorized 7 x 7 convolutions, and the use of an auxiliary classifier to propagate label information lower down the network (along with the use of batch normalization for layers in the side head)",inception_v3,0.184119633,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
751,quantization.mobilenet_v3_large,Replacing ReLU with GeLU,convnext_tiny,0.013605048,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
752,vit_b_16,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.115460483,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
753,regnet_x_32gf,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.215895384,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
754,vit_l_16,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels",vit_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
755,vit_b_32,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.176046103,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
756,video.r3d_18,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.184140802,1x32x32,video classification,Null,TV
757,quantization.resnext101_32x8d,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.139795269,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
758,vgg16_bn,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.219635598,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
759,mnasnet0_5,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.209434956,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
760,convnext_tiny,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.218242801,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
761,inception_v3,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.143918718,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
762,shufflenet_v2_x2_0,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.167471862,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
763,densenet201,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.120033852,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
764,quantization.shufflenet_v2_x2_0,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.146512737,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
765,segmentation.fcn_resnet101,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.142249492,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
766,googlenet,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.111732706,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
767,mnasnet0_75,The squeezing (bottleneck layer) and expansion behavior is common in neural architectures,squeezenet1_1,0.227308805,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
768,convnext_small,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.166741914,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
769,resnext50_32x4d,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.177674655,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
770,quantization.shufflenet_v2_x0_5,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.101491225,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
771,efficientnet_b3,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.174634504,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
772,regnet_x_800mf,2D max pooling in between the weight layers,vgg11,0.145925756,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
773,quantization.shufflenet_v2_x2_0,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.159971321,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
774,efficientnet_b6,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.219984844,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
775,convnext_tiny,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.160659574,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
776,mnasnet0_75,It is the simplest of all other vgg versions,vgg11,0.164879752,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
777,quantization.resnext101_32x8d,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.199790455,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
778,regnet_y_800mf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.164448209,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
779,quantization.shufflenet_v2_x1_0,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",quantization.shufflenet_v2_x1_0,1.0,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
780,segmentation.deeplabv3_resnet101,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.08187772,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
781,video.r3d_18,Constructor for 18 layer Mixed Convolution network,video.r3d_18,1.0,1x32x32,video classification,Null,TV
782,wide_resnet50_2,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.1256363,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
783,detection.maskrcnn_resnet50_fpn,The branch is a fully connected network applied on RoIs to classify each pixel into segments with little overall computation cost,detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
784,regnet_x_400mf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.085977602,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
785,mnasnet1_3,"1x1 Convolution, Batch Normalization, Convolution, Depth wise Separable Convolution, Dropout, Global Average Pooling, Inverted Residual Block, Residual Connection, ReLU, Max Pooling, Softmax, Squeeze-and-Excitation Block",mnasnet1_0,0.140878854,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
786,video.mc3_18,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.117472256,1x32x32,video classification,Null,TV
787,regnet_y_32gf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.185578547,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
788,video.r3d_18,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.199685772,1x32x32,video classification,Null,TV
789,convnext_large,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.035195267,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
790,regnet_x_3_2gf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.099518669,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
791,vgg11_bn,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.162884905,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
792,detection.ssdlite320_mobilenet_v3_large,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.179018574,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
793,alexnet,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.113775209,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
794,shufflenet_v2_x1_0,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.155112504,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
795,efficientnet_b5,EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient,efficientnet_b5,1.0,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
796,convnext_tiny,Shallower network means: Number of layers can be reduced and Training time can be shorter as well,wide_resnet50_2,0.206594883,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
797,efficientnet_b1,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.163295986,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
798,quantization.inception_v3,ResNet-152 can obtain 4.49% error rate,resnet152,0.134976567,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
799,detection.fasterrcnn_mobilenet_v3_large_fpn,"The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection",detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
800,densenet121,The key building block is an Inception Module,inception_v3,0.223103208,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
801,convnext_base,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.157393614,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
802,detection.ssdlite320_mobilenet_v3_large,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.125428571,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
803,vit_b_32,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.097055603,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
804,quantization.mobilenet_v2,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.128897317,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
805,vgg13_bn,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.199148102,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
806,quantization.inception_v3,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.122731167,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
807,segmentation.deeplabv3_mobilenet_v3_large,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384",vit_l_32,0.176269089,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
808,vgg13,The required minimum input size of the model is 32x32,vgg13,1.0,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
809,mnasnet1_3,"It has 11 weight layers in total, so the name VGG11",vgg11,0.2217411,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
810,squeezenet1_0,"Fully Convolutional Networks, or FCNs, are an architecture used mainly for semantic segmentation",segmentation.fcn_resnet50,0.216368745,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
811,shufflenet_v2_x1_5,ReLU non-linearity as activation functions,vgg11,0.145572677,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
812,detection.retinanet_resnet50_fpn,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.057348307,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
813,quantization.shufflenet_v2_x2_0,VGG19 has 19.6 billion FLOPs,vgg19,0.110981011,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
814,detection.fasterrcnn_mobilenet_v3_large_fpn,It works similarly to Faster R-CNN with ResNet-50 FPN backbone,detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
815,convnext_small,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.057655312,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
816,vgg11_bn,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.116051115,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
817,quantization.shufflenet_v2_x1_5,The main building block is a ShuffleNet v2 Block,quantization.shufflenet_v2_x1_5,1.0,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
818,detection.ssd300_vgg16,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.135022514,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
819,efficientnet_b5,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.176408559,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
820,inception_v3,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.099486234,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
821,quantization.googlenet,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.128385972,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
822,mnasnet0_5,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.160818582,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
823,convnext_tiny,"In particular, in this section we use the 400 million flops (400MF) regime and train each sampled model for 10 epochs on the ImageNet dataset",regnet_y_400mf,0.152179212,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
824,shufflenet_v2_x1_0,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.105127011,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
825,resnet18,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.194189196,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
826,regnet_x_400mf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.134668675,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
827,densenet201,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.189539606,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
828,regnet_y_800mf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.092816979,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
829,quantization.mobilenet_v3_large,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.166644228,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
830,detection.fasterrcnn_resnet50_fpn,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.136988903,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
831,video.mc3_18,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.172151815,1x32x32,video classification,Null,TV
832,mnasnet1_0,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.176279116,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
833,segmentation.deeplabv3_resnet101,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.191618374,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
834,convnext_base,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.054394472,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
835,resnet18,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.222494539,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
836,densenet161,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.188263555,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
837,mnasnet1_3,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.122588081,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
838,regnet_y_400mf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.178456724,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
839,mobilenet_v3_large,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.15458272,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
840,convnext_tiny,Constructs a DeepLabV3 model with a ResNet-101 backbone,segmentation.deeplabv3_resnet101,0.205299567,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
841,segmentation.fcn_resnet101,ssd is a method for detecting objects in images using a single deep neural network,detection.ssd300_vgg16,0.205867658,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
842,convnext_small,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.029139509,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
843,vit_b_32,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.107590305,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
844,regnet_x_1_6gf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.171711998,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
845,regnet_x_400mf,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.226784446,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
846,resnext101_32x8d,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.243353356,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
847,densenet121,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.196514077,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
848,convnext_base,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.096311214,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
849,regnet_x_32gf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.107640883,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
850,video.r2plus1d_18,You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close,vit_b_32,0.112988735,1x32x32,video classification,Null,TV
851,mobilenet_v3_large,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.208987078,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
852,wide_resnet101_2,The key building block is an Inception Module,inception_v3,0.164838847,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
853,regnet_y_8gf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.059344877,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
854,resnet34,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.153151651,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
855,efficientnet_b6,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.142075897,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
856,segmentation.fcn_resnet101,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.087309552,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
857,mobilenet_v3_small,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.197860758,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
858,quantization.shufflenet_v2_x0_5,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.167379792,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
859,regnet_x_8gf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.154349054,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
860,quantization.shufflenet_v2_x2_0,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.136158779,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
861,inception_v3,"We show that MC ResNets yield roughly a 3-4% gain in clip-level accuracy over 2D ResNets of comparable capacity and they match the performance of 3D ResNets, which have 3 times as many parameters",video.mc3_18,0.193998831,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
862,segmentation.fcn_resnet101,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.156320812,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
863,mnasnet1_3,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.206639633,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
864,regnet_y_3_2gf,It is the simplest of all other vgg versions,vgg11,0.189208625,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
865,quantization.shufflenet_v2_x2_0,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.154345507,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
866,convnext_base,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.154856881,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
867,vgg11,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.125258276,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
868,convnext_base,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.193304447,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
869,efficientnet_b1,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.15146844,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
870,detection.retinanet_resnet50_fpn,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.195120412,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
871,regnet_y_8gf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.127508416,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
872,efficientnet_b7,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.164253784,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
873,vgg13,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.128036373,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
874,regnet_y_32gf,Replacing ReLU with GeLU,convnext_tiny,0.134912531,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
875,convnext_tiny,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.148425148,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
876,segmentation.deeplabv3_resnet50,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.11059827,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
877,quantization.inception_v3,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.21338404,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
878,mnasnet0_5,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,0.228735457,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
879,shufflenet_v2_x0_5,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.139369556,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
880,convnext_large,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet18,0.194178322,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
881,wide_resnet50_2,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.149224657,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
882,squeezenet1_0,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.142133995,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
883,segmentation.deeplabv3_mobilenet_v3_large,The number of parameters is 3.4 million,mobilenet_v2,0.122392625,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
884,detection.ssdlite320_mobilenet_v3_large,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.123792388,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
885,mnasnet0_75,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.163519092,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
886,efficientnet_b4,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.199365203,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
887,vgg13_bn,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.090979323,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
888,segmentation.lraspp_mobilenet_v3_large,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.176759389,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
889,convnext_small,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.120100722,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
890,convnext_large,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.173663962,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
891,regnet_x_8gf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.065153938,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
892,segmentation.fcn_resnet50,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.215691871,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
893,quantization.mobilenet_v2,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.111031117,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
894,mnasnet0_75,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.23992686,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
895,mobilenet_v3_large,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.096214545,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
896,vit_b_16,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.123619227,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
897,regnet_x_1_6gf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.103671444,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
898,convnext_base,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.188842302,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
899,inception_v3,It works similarly to Faster R-CNN with ResNet-50 FPN backbone,detection.fasterrcnn_mobilenet_v3_large_fpn,0.177823416,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
900,segmentation.fcn_resnet50,"Fully Convolutional Networks, or FCNs, are an architecture used mainly for semantic segmentation",segmentation.fcn_resnet50,1.0,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
901,detection.fasterrcnn_mobilenet_v3_large_320_fpn,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.088012259,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
902,detection.ssdlite320_mobilenet_v3_large,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.247139508,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
903,quantization.inception_v3,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.10999956,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
904,inception_v3,It follows this arrangement of convolution and max pool layers consistently throughout the whole architecture,vgg16,0.2138668,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
905,video.r2plus1d_18,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.104075396,1x32x32,video classification,Null,TV
906,efficientnet_b4,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.165962178,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
907,convnext_tiny,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.152187207,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
908,regnet_y_3_2gf,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.184985057,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
909,regnet_x_1_6gf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.154221953,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
910,densenet169,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.197785782,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
911,mnasnet0_75,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.194177687,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
912,vit_b_16,"resnext50_32x4d is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",resnext50_32x4d,0.118556308,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
913,segmentation.deeplabv3_resnet50,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.113600367,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
914,convnext_base,The number of parameters is 3.4 million,mobilenet_v2,0.059787545,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
915,convnext_large,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_large,1.0,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
916,mnasnet1_3,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.024979656,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
917,segmentation.lraspp_mobilenet_v3_large,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.186952719,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
918,vit_b_32,vit_b_32 is used for image classification,vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
919,vit_l_16,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.138769865,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
920,vit_b_16,"As a whole, Faster R-CNN consists of two modules",detection.fasterrcnn_resnet50_fpn,0.199828377,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
921,alexnet,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.140695838,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
922,regnet_y_1_6gf,"The RegNet is composed of a stem, body and head",regnet_y_1_6gf,1.0,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
923,shufflenet_v2_x1_0,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.128092309,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
924,segmentation.fcn_resnet101,The required minimum input size of the model is 21x21,squeezenet1_0,0.163853628,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
925,regnet_y_800mf,Replacing ReLU with GeLU,convnext_tiny,0.13344705,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
926,googlenet,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.053836897,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
927,resnext50_32x4d,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.119519339,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
928,vgg16_bn,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.148257053,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
929,quantization.shufflenet_v2_x1_0,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.11209464,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
930,resnet50,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.111940359,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
931,squeezenet1_1,ReLU non-linearity as activation functions,vgg11,0.083326611,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
932,quantization.resnext101_32x8d,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.206436495,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
933,detection.keypointrcnn_resnet50_fpn,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.197220211,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
934,convnext_large,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.18761219,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
935,efficientnet_b6,EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient,efficientnet_b6,1.0,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
936,vgg11_bn,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.130833803,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
937,resnext50_32x4d,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.205870673,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
938,vit_b_32,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
939,densenet201,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.216373665,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
940,efficientnet_b7,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.154773134,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
941,vgg11_bn,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.177112972,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
942,video.mc3_18,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.15520417,1x32x32,video classification,Null,TV
943,segmentation.lraspp_mobilenet_v3_large,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.13526566,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
944,vit_b_32,"MnasNet is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency",mnasnet0_5,0.139939122,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
945,quantization.googlenet,"A fixed size of (224 * 224) RGB image was given as input to this network which means that the matrix was of shape (224,224,3)",vgg19,0.157341047,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
946,convnext_large,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.173248121,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
947,mobilenet_v3_large,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.066412566,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
948,quantization.shufflenet_v2_x1_5,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.126171851,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
949,resnet152,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.071267139,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
950,vgg16_bn,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.168357489,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
951,vgg19,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.126696877,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
952,convnext_tiny,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.115239286,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
953,quantization.mobilenet_v3_large,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.170695026,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
954,vgg13,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.126093911,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
955,squeezenet1_1,Constructs a high resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone,detection.fasterrcnn_mobilenet_v3_large_fpn,0.266499939,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
956,quantization.inception_v3,"resnext50_32x4d is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",resnext50_32x4d,0.222753607,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
957,detection.ssdlite320_mobilenet_v3_large,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.161923548,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
958,shufflenet_v2_x2_0,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.1756704,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
959,efficientnet_b0,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.153416894,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
960,mnasnet0_5,This model has 3.8 billion FLOPs,resnet50,0.137675231,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
961,inception_v3,MobileNetv3-Large is 27% faster than MobileNetV2 while maintaining similar mAP,mobilenet_v3_large,0.16761678,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
962,squeezenet1_0,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.235212028,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
963,mnasnet0_75,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.209138473,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
964,shufflenet_v2_x1_0,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x1_0,1.0,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
965,segmentation.deeplabv3_resnet101,The key building block is an Inception Module,inception_v3,0.117279711,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
966,regnet_x_800mf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.132630471,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
967,detection.fasterrcnn_resnet50_fpn,This model has 3.8 billion FLOPs,resnet50,0.154534507,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
968,regnet_x_1_6gf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.162658508,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
969,regnet_y_400mf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.186313552,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
970,quantization.shufflenet_v2_x0_5,The main building block is a ShuffleNet v2 Block,quantization.shufflenet_v2_x0_5,1.0,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
971,vit_l_16,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.117011448,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
972,regnet_x_16gf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.158029141,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
973,shufflenet_v2_x0_5,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.169022962,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
974,regnet_x_400mf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.152877621,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
975,mnasnet0_75,The required minimum input size of the model is 32x32,vgg11,0.161779756,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
976,regnet_y_3_2gf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.10572237,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
977,regnet_y_8gf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.158869697,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
978,convnext_small,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.147006638,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
979,alexnet,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.113333558,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
980,quantization.shufflenet_v2_x1_0,"It has 11 weight layers in total, so the name VGG11",vgg11,0.17664497,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
981,efficientnet_b6,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.148227086,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
982,densenet121,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.103372098,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
983,regnet_x_400mf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.202501724,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
984,resnet34,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.221668622,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
985,resnext101_32x8d,The number of parameters is 3.4 million,mobilenet_v2,0.159126712,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
986,segmentation.deeplabv3_mobilenet_v3_large,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.095854957,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
987,segmentation.lraspp_mobilenet_v3_large,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.149525601,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
988,efficientnet_b0,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.201068492,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
989,resnext50_32x4d,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.226084743,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
990,convnext_small,The densenet-201 is larger at over 77MB in size vs the densenet-121 model's roughly 31MB size,densenet201,0.166226722,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
991,vgg13,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.132158918,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
992,squeezenet1_0,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.150592993,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
993,segmentation.fcn_resnet101,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.136490541,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
994,resnext101_32x8d,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.164685914,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
995,vit_b_16,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.134165229,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
996,vgg16,vgg16 is used for image classification,vgg16,1.0,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
997,detection.fasterrcnn_resnet50_fpn,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
998,vit_b_32,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.166889145,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
999,regnet_x_400mf,Constructs a RegNetX_400MF architecture,regnet_x_400mf,1.0,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1000,quantization.mobilenet_v2,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.18145312,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
1001,quantization.shufflenet_v2_x2_0,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.17432262,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1002,regnet_x_32gf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.165657893,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1003,regnet_y_3_2gf,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.049788639,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1004,efficientnet_b2,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.129170469,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
1005,vit_b_16,MobileNetV2 is a convolutional neural network architecture that seeks to perform well on mobile devices,mobilenet_v2,0.152356603,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1006,mnasnet0_5,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.192710072,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1007,vit_b_16,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.07702728,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1008,quantization.shufflenet_v2_x1_5,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.162112758,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1009,efficientnet_b3,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.158687256,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1010,vgg16,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.140198977,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1011,quantization.resnext101_32x8d,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.239479785,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1012,segmentation.fcn_resnet101,"Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384",vit_l_32,0.200243594,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1013,vit_b_16,obtained by scaling up the baseline network,efficientnet_b1,0.151346121,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1014,efficientnet_b0,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.164343151,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1015,googlenet,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.102580731,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1016,segmentation.fcn_resnet101,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.091385088,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1017,convnext_base,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.226142516,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1018,vgg16_bn,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg16_bn,1.0,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1019,regnet_y_800mf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.082430572,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1020,regnet_x_16gf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.1098327,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1021,video.r2plus1d_18,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.078547078,1x32x32,video classification,Null,TV
1022,regnet_x_800mf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.149073411,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1023,quantization.resnext101_32x8d,The number of parameters is 3.4 million,mobilenet_v2,0.171110703,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1024,segmentation.fcn_resnet101,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.231250333,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1025,resnet50,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.134690952,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
1026,squeezenet1_1,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.155951247,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1027,regnet_y_32gf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.104740362,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1028,convnext_tiny,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.110846281,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1029,quantization.inception_v3,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.216436069,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1030,quantization.googlenet,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.187294185,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1031,quantization.resnet18,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.186085077,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1032,densenet161,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.144582858,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
1033,mnasnet0_5,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.074588887,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1034,quantization.shufflenet_v2_x1_0,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.204350432,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1035,efficientnet_b2,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.122511698,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
1036,detection.keypointrcnn_resnet50_fpn,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.157895962,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1037,resnet18,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.072524581,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1038,quantization.resnet18,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.134701581,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1039,squeezenet1_1,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.094964226,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1040,mobilenet_v3_small,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.126914969,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1041,regnet_y_3_2gf,Replacing ReLU with GeLU,convnext_tiny,0.140102576,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1042,vgg16_bn,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.211843597,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1043,wide_resnet50_2,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.057978208,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1044,regnet_x_16gf,Replacing ReLU with GeLU,convnext_tiny,0.144070665,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1045,quantization.resnet18,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.123322482,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1046,detection.retinanet_resnet50_fpn,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.139057926,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1047,vgg13,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.203256445,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1048,mobilenet_v2,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.172644592,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1049,regnet_x_8gf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_x_8gf,1.0,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1050,segmentation.fcn_resnet101,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.174999041,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1051,regnet_x_3_2gf,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.163702455,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1052,detection.fasterrcnn_resnet50_fpn,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1053,shufflenet_v2_x2_0,shufflenet_v2_x2_0 is used for image classification,shufflenet_v2_x2_0,1.0,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1054,detection.maskrcnn_resnet50_fpn,adopted from BERT,vit_b_16,0.000955426,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1055,mnasnet1_3,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.210108131,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1056,resnet152,VGG19 has 19.6 billion FLOPs,vgg19,0.104329402,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1057,mobilenet_v3_large,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.164818606,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1058,convnext_large,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.097852914,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1059,mnasnet0_75,"1x1 Convolution, Batch Normalization, Convolution, Depth wise Separable Convolution, Dropout, Global Average Pooling, Inverted Residual Block, Residual Connection, ReLU, Max Pooling, Softmax, Squeeze-and-Excitation Block",mnasnet1_0,0.136062344,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1060,quantization.resnet18,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.188191348,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1061,regnet_y_800mf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.158913659,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1062,video.r3d_18,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.170920088,1x32x32,video classification,Null,TV
1063,shufflenet_v2_x1_0,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.187287219,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1064,mnasnet0_75,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.203875514,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1065,efficientnet_b0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.186830723,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1066,shufflenet_v2_x1_5,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.194268059,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1067,segmentation.lraspp_mobilenet_v3_large,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.198969817,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1068,densenet201,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.168986102,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1069,vit_l_16,Constructs a DeepLabV3 model with a ResNet-50 backbone,segmentation.deeplabv3_resnet50,0.144995164,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1070,googlenet,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.194857391,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1071,regnet_y_800mf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.122811593,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1072,mnasnet1_3,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.042758721,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1073,segmentation.lraspp_mobilenet_v3_large,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.250007677,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1074,convnext_base,This network is a pretty large network and it has about 138 million parameters,vgg16,0.112149902,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1075,mnasnet0_5,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.209859714,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1076,efficientnet_b1,efficientnet_b1 is used for image classification,efficientnet_b1,1.0,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
1077,alexnet,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.186434887,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1078,wide_resnet101_2,wide_resnet101_2 is used for image classification,wide_resnet101_2,1.0,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1079,quantization.resnext101_32x8d,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,1.0,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1080,shufflenet_v2_x2_0,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.168054987,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1081,vit_b_16,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.105197937,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1082,quantization.shufflenet_v2_x1_0,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.127703634,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1083,vit_l_16,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.102929608,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1084,efficientnet_b5,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.154018018,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1085,regnet_x_1_6gf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_x_1_6gf,1.0,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1086,vgg16,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.17216627,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1087,quantization.googlenet,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",quantization.googlenet,1.0,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1088,squeezenet1_0,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.229193389,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1089,video.r2plus1d_18,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.195732418,1x32x32,video classification,Null,TV
1090,detection.maskrcnn_resnet50_fpn,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.161465678,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1091,detection.fasterrcnn_mobilenet_v3_large_fpn,Faster R-CNN builds a network that has only a single stage,detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
1092,regnet_y_32gf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.099976304,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1093,quantization.googlenet,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.062336364,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1094,regnet_y_8gf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_8gf,1.0,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1095,resnext50_32x4d,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.119903162,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1096,convnext_tiny,The required minimum input size of the model is 32x32,vgg11,0.138871618,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1097,quantization.inception_v3,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.203036804,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1098,resnext101_32x8d,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.245101128,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1099,video.r2plus1d_18,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.110089954,1x32x32,video classification,Null,TV
1100,regnet_y_32gf,ReLU non-linearity as activation functions,vgg11,0.160136604,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1101,detection.keypointrcnn_resnet50_fpn,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.19307364,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1102,quantization.resnet18,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.193931246,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1103,convnext_tiny,Wide ResNet-50-2 Wide Residual Networks,wide_resnet50_2,0.190989643,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1104,regnet_x_1_6gf,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.153486805,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1105,efficientnet_b5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.226653375,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1106,regnet_y_1_6gf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.171894732,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1107,wide_resnet50_2,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.249317078,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1108,detection.fasterrcnn_resnet50_fpn,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.128772054,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1109,shufflenet_v2_x2_0,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.168131309,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1110,regnet_x_8gf,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.111302633,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1111,segmentation.fcn_resnet50,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.029312742,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1112,detection.fasterrcnn_resnet50_fpn,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.088539867,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1113,mnasnet0_75,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.173870926,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1114,vit_b_16,"The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks",efficientnet_b0,0.076937962,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1115,regnet_x_800mf,The required minimum input size of the model is 21x21,squeezenet1_0,0.222179242,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1116,vit_b_32,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.132728972,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1117,regnet_y_32gf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.154830533,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1118,shufflenet_v2_x2_0,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.105367,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1119,regnet_x_32gf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.086596186,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1120,vgg16_bn,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16_bn,1.0,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1121,segmentation.fcn_resnet50,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.18560132,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1122,vit_l_32,Constructs a DeepLabV3 model with a MobileNetV3-Large backbone,segmentation.deeplabv3_mobilenet_v3_large,0.141248805,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1123,squeezenet1_1,adopted from BERT,vit_b_16,0.022181168,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1124,vit_l_32,"Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks",vit_l_32,1.0,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1125,vit_l_32,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.107136828,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1126,resnext101_32x8d,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.156164626,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1127,quantization.shufflenet_v2_x2_0,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.105848627,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1128,detection.maskrcnn_resnet50_fpn,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.138055759,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1129,mnasnet0_5,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.117237095,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1130,vit_l_16,Constructor for 18 layer Mixed Convolution network,video.r3d_18,0.167667507,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1131,googlenet,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.123466171,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1132,segmentation.lraspp_mobilenet_v3_large,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.207264572,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1133,regnet_y_400mf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.09046561,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1134,vit_b_32,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.126918313,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1135,segmentation.deeplabv3_resnet101,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet101,1.0,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1136,detection.maskrcnn_resnet50_fpn,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.146906298,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1137,segmentation.deeplabv3_resnet101,The original dataset contained images of size 512x512x1,resnet101,0.122313404,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1138,vgg13_bn,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.143542644,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1139,squeezenet1_0,"VGG13 has the same architecture as VGG11, but with a different network size",vgg13,0.164622222,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1140,convnext_base,Constructs a DeepLabV3 model with a ResNet-101 backbone,segmentation.deeplabv3_resnet101,0.218788616,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1141,efficientnet_b5,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.218162902,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1142,resnet152,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.212568817,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1143,detection.keypointrcnn_resnet50_fpn,"(keypointrcnn_resnet50_fpn) Keypoint R-CNN is an algorithm for finding unique points on the human body, also called keypoints on images containing a human",detection.keypointrcnn_resnet50_fpn,1.0,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1144,regnet_y_16gf,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.131925066,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1145,mnasnet0_75,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.228019178,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1146,shufflenet_v2_x1_0,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.169862633,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1147,resnet18,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.139849949,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1148,video.r3d_18,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.155528836,1x32x32,video classification,Null,TV
1149,quantization.resnext101_32x8d,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.232753473,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1150,shufflenet_v2_x0_5,"It has 11 weight layers in total, so the name VGG11",vgg11,0.184624795,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1151,quantization.shufflenet_v2_x0_5,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.114653253,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1152,squeezenet1_0,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.150228862,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1153,shufflenet_v2_x1_0,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.153164987,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1154,convnext_tiny,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.107366344,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1155,vit_b_16,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.138346677,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1156,resnet34,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.198482158,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1157,convnext_small,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.136826109,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1158,efficientnet_b4,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.169406886,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1159,efficientnet_b3,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.196357965,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1160,inception_v3,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.224078774,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1161,quantization.mobilenet_v2,mobilenet_v2 is used for image classification,quantization.mobilenet_v2,1.0,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
1162,detection.ssdlite320_mobilenet_v3_large,Relative fast due to no fully connected layers or a refinement block,segmentation.fcn_resnet50,0.16220975,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1163,detection.ssdlite320_mobilenet_v3_large,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.199846549,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1164,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals",detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1165,efficientnet_b2,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.123067097,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
1166,mobilenet_v3_small,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.164884184,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1167,regnet_y_400mf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.200989555,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1168,regnet_y_16gf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.095707733,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1169,resnext101_32x8d,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.144187594,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1170,regnet_x_1_6gf,The required minimum input size of the model is 32x32,vgg11,0.167354379,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1171,detection.retinanet_resnet50_fpn,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.053449329,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1172,shufflenet_v2_x2_0,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.164585747,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1173,detection.ssd300_vgg16,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.072783894,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1174,regnet_y_3_2gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.171169058,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1175,quantization.resnext101_32x8d,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.225216908,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1176,resnet50,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.090298857,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
1177,quantization.googlenet,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.199731787,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1178,regnet_x_400mf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.176408846,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1179,alexnet,Note that this model does not include any fine-tuned heads,vit_b_16,0.220841587,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1180,regnet_y_32gf,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.165687268,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1181,detection.fasterrcnn_mobilenet_v3_large_fpn,RPN and Fast R-CNN are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look,detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
1182,regnet_x_400mf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.113864159,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1183,segmentation.fcn_resnet101,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels",vit_b_16,0.200208712,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1184,vit_b_16,Constructs a DeepLabV3 model with a ResNet-50 backbone,segmentation.deeplabv3_resnet50,0.143147984,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1185,regnet_x_800mf,Replacing ReLU with GeLU,convnext_tiny,0.131369122,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1186,densenet169,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.159145885,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
1187,densenet161,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.189631796,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
1188,regnet_y_400mf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.18633353,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1189,efficientnet_b4,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.149886105,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1190,convnext_small,Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone,segmentation.lraspp_mobilenet_v3_large,0.13820215,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1191,detection.keypointrcnn_resnet50_fpn,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.060194447,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1192,vgg11_bn,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.176580468,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
1193,convnext_base,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.132456079,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1194,detection.ssdlite320_mobilenet_v3_large,The dense prediction is achieved by simply up-sampling the output of the last convolution layer and computing pixel-wise loss,segmentation.deeplabv3_resnet50,0.215598193,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1195,quantization.resnet50,"VGG13 has the same architecture as VGG11, but with a different network size",vgg13,0.239801012,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
1196,regnet_x_1_6gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.211521207,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1197,resnext101_32x8d,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.185395982,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1198,detection.ssdlite320_mobilenet_v3_large,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.149114127,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1199,resnext101_32x8d,ResNeXt is a homogeneous neural network which reduces the number of hyper parameters required by conventional ResNet,resnext101_32x8d,1.0,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1200,vgg11,MobileNetv3-Large is 27% faster than MobileNetV2 while maintaining similar mAP,mobilenet_v3_large,0.166785676,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1201,segmentation.deeplabv3_mobilenet_v3_large,"Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded",vit_b_16,0.178909593,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1202,resnext101_32x8d,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.156024311,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1203,quantization.googlenet,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.101199779,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1204,regnet_y_8gf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.166660108,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1205,quantization.shufflenet_v2_x0_5,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.181688338,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1206,vit_b_16,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1207,vgg11,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.139778282,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1208,resnet101,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.217894601,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
1209,quantization.googlenet,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.088379333,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1210,detection.maskrcnn_resnet50_fpn,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.190458795,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1211,mnasnet0_75,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.129769546,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1212,quantization.mobilenet_v2,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.16210869,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
1213,vgg13_bn,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.100844527,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1214,efficientnet_b6,This model has 3.8 billion FLOPs,resnet50,0.176877966,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1215,quantization.mobilenet_v3_large,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.16775515,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1216,regnet_y_16gf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.171045133,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1217,detection.retinanet_resnet50_fpn,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.059047944,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1218,segmentation.lraspp_mobilenet_v3_large,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.15486344,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1219,resnext50_32x4d,2D max pooling in between the weight layers,vgg11,0.218424544,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1220,regnet_x_3_2gf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.16833787,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1221,vit_b_16,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.067859707,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1222,efficientnet_b7,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.149406455,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
1223,vit_l_16,MobileNet v3-Small has 2.5 million parameters,mobilenet_v3_small,0.11894306,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1224,wide_resnet50_2,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.122138802,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1225,regnet_y_800mf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.148681725,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1226,vit_b_32,They stack residual blocks ontop of each other to form network,vgg19_bn,0.056256618,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1227,vgg11,"MnasNet is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency",mnasnet0_5,0.156519943,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1228,regnet_y_3_2gf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.17757424,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1229,convnext_small,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.212714427,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1230,vgg11_bn,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.077161257,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
1231,resnet152,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.171933308,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1232,efficientnet_b7,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.203124411,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
1233,efficientnet_b5,obtained by scaling up the baseline network,efficientnet_b5,1.0,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1234,mobilenet_v3_large,ReLU non-linearity as activation functions,vgg11,0.084153229,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1235,detection.fasterrcnn_mobilenet_v3_large_320_fpn,Constructs a low resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone tuned for mobile use-cases,detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1236,squeezenet1_1,"VGG13 has the same architecture as VGG11, but with a different network size",vgg13,0.16630849,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1237,wide_resnet101_2,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.129764472,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1238,quantization.inception_v3,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.191045392,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1239,googlenet,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.204018598,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1240,segmentation.deeplabv3_resnet50,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.192389631,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1241,quantization.shufflenet_v2_x0_5,"It has 11 weight layers in total, so the name VGG11",vgg11,0.182994357,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1242,regnet_y_3_2gf,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.210575403,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1243,convnext_small,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.074353637,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1244,wide_resnet101_2,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.11875315,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1245,quantization.resnet18,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.060248531,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1246,video.mc3_18,The number of parameters is 3.4 million,mobilenet_v2,0.13531555,1x32x32,video classification,Null,TV
1247,convnext_tiny,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.136188253,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1248,resnext50_32x4d,Mask R-CNN extends on the Faster R-CNN by adding another branch in parallel for pixel-level object instance segmentation,detection.maskrcnn_resnet50_fpn,0.225005951,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1249,mnasnet0_5,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.208237141,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1250,mnasnet0_75,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.232987657,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1251,vgg19,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.121995727,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1252,efficientnet_b6,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.120625,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1253,vit_l_32,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.181917306,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1254,video.mc3_18,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.075486487,1x32x32,video classification,Null,TV
1255,mnasnet0_5,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.186407397,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1256,detection.retinanet_resnet50_fpn,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.05504726,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1257,wide_resnet50_2,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.166756911,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1258,quantization.shufflenet_v2_x1_5,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.101252767,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1259,resnext50_32x4d,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.238564759,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1260,convnext_tiny,The densenet-169 is larger at just about 55MB in size vs the densenet-121 model's roughly 31MB size,densenet169,0.168089383,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1261,regnet_y_32gf,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_32gf,1.0,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1262,vgg11_bn,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.126276611,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
1263,googlenet,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,0.188496684,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1264,detection.keypointrcnn_resnet50_fpn,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.217450242,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1265,shufflenet_v2_x0_5,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.105177085,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1266,efficientnet_b3,Note that this model does not include any fine-tuned heads,vit_b_16,0.172858466,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1267,regnet_y_800mf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.099558073,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1268,squeezenet1_0,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.171474681,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1269,detection.fasterrcnn_mobilenet_v3_large_320_fpn,The required minimum input size of the model is 17x17,squeezenet1_1,0.197930672,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1270,alexnet,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.195148913,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1271,detection.fasterrcnn_resnet50_fpn,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.105163557,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1272,densenet201,The key building block is an Inception Module,inception_v3,0.180699063,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1273,segmentation.deeplabv3_resnet101,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.182579193,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1274,resnet18,resnet18  is used for image classification,resnet18,1.0,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1275,video.r2plus1d_18,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.114084238,1x32x32,video classification,Null,TV
1276,detection.fasterrcnn_resnet50_fpn,"The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions",detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1277,shufflenet_v2_x1_5,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.171348962,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1278,vit_l_16,"resnext50_32x4d is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",resnext50_32x4d,0.121772925,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1279,alexnet,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.152138516,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1280,vit_b_16,MobileNet v3-Small has 2.5 million parameters,mobilenet_v3_small,0.117713322,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1281,squeezenet1_0,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.140462975,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1282,shufflenet_v2_x1_5,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.206747277,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1283,resnet50,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.157357133,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
1284,regnet_x_1_6gf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.150739783,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1285,vgg19,VGG19 has 19.6 billion FLOPs,vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1286,segmentation.lraspp_mobilenet_v3_large,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.078216496,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1287,resnet101,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.151240215,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
1288,regnet_y_400mf,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.161378851,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1289,regnet_y_800mf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.168482267,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1290,convnext_base,densenet169 is More acurate than DenseNet-121,densenet169,0.18249657,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1291,regnet_x_16gf,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.162930412,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1292,vgg11,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.126682756,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1293,quantization.resnet50,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.1900947,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
1294,convnext_tiny,The key building block is an Inception Module,inception_v3,0.119515619,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1295,vgg13,"VGG13 has the same architecture as VGG11, but with a different network size",vgg13,1.0,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1296,convnext_large,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.155696888,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1297,wide_resnet50_2,The key building block is an Inception Module,inception_v3,0.156447559,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1298,quantization.resnext101_32x8d,Mask R-CNN extends on the Faster R-CNN by adding another branch in parallel for pixel-level object instance segmentation,detection.maskrcnn_resnet50_fpn,0.233796169,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1299,segmentation.deeplabv3_resnet50,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.209721637,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1300,quantization.resnext101_32x8d,2D max pooling in between the weight layers,vgg11,0.208991935,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1301,regnet_y_8gf,The required minimum input size of the model is 29x29,densenet121,0.192151703,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1302,shufflenet_v2_x2_0,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.228501689,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1303,regnet_y_32gf,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.176134176,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1304,detection.retinanet_resnet50_fpn,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.066936589,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1305,video.mc3_18,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.182453398,1x32x32,video classification,Null,TV
1306,convnext_tiny,The required minimum input size of the model is 21x21,squeezenet1_0,0.170826317,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1307,regnet_x_800mf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.156794152,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1308,detection.retinanet_resnet50_fpn,"RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks",detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1309,detection.fasterrcnn_mobilenet_v3_large_320_fpn,Proposing region proposal network (RPN) which is a fully convolutional network that generates proposals with various scales and aspect ratios,detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1310,vgg13,"EfficientNet-B7 achieves new state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy, while being 8.4x smaller than the best existing CNN",efficientnet_b7,0.247001864,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1311,regnet_y_3_2gf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.164315691,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1312,convnext_base,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.220219893,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1313,segmentation.lraspp_mobilenet_v3_large,Relative fast due to no fully connected layers or a refinement block,segmentation.fcn_resnet50,0.177246419,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1314,segmentation.lraspp_mobilenet_v3_large,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.203638616,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1315,vgg11_bn,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_400mf,0.189128103,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
1316,quantization.shufflenet_v2_x2_0,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.090665753,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1317,efficientnet_b0,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.167003679,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1318,mnasnet0_5,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.043117118,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1319,efficientnet_b1,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.144289106,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
1320,segmentation.lraspp_mobilenet_v3_large,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.208236128,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1321,quantization.inception_v3,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.165777009,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1322,mnasnet1_3,The required minimum input size of the model is 29x29,densenet121,0.13544824,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1323,vgg13_bn,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.133478743,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1324,efficientnet_b3,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.127433512,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1325,regnet_y_16gf,"The RegNet is composed of a stem, body and head",regnet_y_16gf,1.0,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1326,efficientnet_b7,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.152819677,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
1327,resnet34,"This is a model that has been pre-trained on the ImageNet dataset--a dataset that has 100,000+ images across 200 different classes",resnet34,1.0,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1328,detection.fasterrcnn_mobilenet_v3_large_320_fpn,The original dataset contained images of size 512x512x1,resnet101,0.156821438,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1329,convnext_large,"It has 11 weight layers in total, so the name VGG11",vgg11,0.211898282,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1330,vit_l_32,Constructs a Fully-Convolutional Network model with a ResNet-50 backbone,segmentation.fcn_resnet50,0.104133092,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1331,regnet_y_8gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.183533105,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1332,quantization.resnext101_32x8d,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.1265655,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1333,regnet_x_16gf,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_16gf,1.0,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1334,vit_b_16,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.131691724,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1335,quantization.shufflenet_v2_x1_0,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.182964972,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1336,quantization.mobilenet_v3_large,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.191814944,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1337,googlenet,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.091312023,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1338,video.r3d_18,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.141431236,1x32x32,video classification,Null,TV
1339,efficientnet_b3,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.176582506,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1340,segmentation.deeplabv3_mobilenet_v3_large,The key building block is an Inception Module,inception_v3,0.118911292,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1341,mnasnet0_5,VGG19 has 19.6 billion FLOPs,vgg19,0.136947403,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1342,shufflenet_v2_x2_0,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.138742669,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1343,vit_l_32,Constructs a RegNetY_800MF architecture,regnet_y_800mf,0.166911401,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1344,efficientnet_b7,"It has 11 weight layers in total, so the name VGG11",vgg11,0.222292456,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
1345,wide_resnet50_2,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.090041205,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1346,quantization.shufflenet_v2_x1_0,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.110229725,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1347,googlenet,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.070167625,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1348,quantization.mobilenet_v3_large,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.166097437,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1349,quantization.shufflenet_v2_x1_5,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.163208189,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1350,mnasnet1_0,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.123318564,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
1351,squeezenet1_0,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.164272911,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1352,efficientnet_b7,adopted from BERT,vit_b_16,0.0,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
1353,resnet152,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.158470401,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1354,mnasnet1_3,VGG19 has 19.6 billion FLOPs,vgg19,0.119698487,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1355,quantization.shufflenet_v2_x0_5,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.17938346,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1356,efficientnet_b5,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.146585671,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1357,quantization.shufflenet_v2_x1_0,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.042505765,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1358,mobilenet_v3_small,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.128714436,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1359,quantization.shufflenet_v2_x2_0,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.182568715,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1360,quantization.inception_v3,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.151209413,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1361,regnet_x_8gf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.16922128,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1362,squeezenet1_0,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.08360257,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1363,vit_l_16,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.1089936,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1364,video.r3d_18,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.148136394,1x32x32,video classification,Null,TV
1365,quantization.shufflenet_v2_x1_0,"It builds upon ShuffleNet v1, which utilised pointwise group convolutions, bottleneck-like structures, and a channel shuffle operation",quantization.shufflenet_v2_x1_0,1.0,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1366,googlenet,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.144329533,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1367,quantization.googlenet,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.055027587,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1368,vgg19_bn,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.139944372,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
1369,regnet_x_3_2gf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.172208639,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1370,convnext_large,The required minimum input size of the model is 32x32,vgg11,0.140243065,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1371,shufflenet_v2_x1_5,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.179465707,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1372,efficientnet_b4,efficientnet_b4 is used for image classification,efficientnet_b4,1.0,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1373,video.r2plus1d_18,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.151826385,1x32x32,video classification,Null,TV
1374,regnet_x_3_2gf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.102058764,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1375,vgg13,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.211819235,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1376,quantization.shufflenet_v2_x1_5,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.164946212,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1377,efficientnet_b7,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.164027326,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
1378,convnext_large,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.199593335,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1379,resnext50_32x4d,The required minimum input size of the model is 29x29,densenet121,0.217344031,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1380,mobilenet_v2,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.17731707,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1381,convnext_large,They stack residual blocks on top of each other to form network: for example  a ResNet-50 has fifty layers using these blocks,resnet50,0.167321197,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1382,quantization.inception_v3,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.207587667,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1383,resnext101_32x8d,2D max pooling in between the weight layers,vgg11,0.212555375,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1384,quantization.shufflenet_v2_x2_0,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.229530296,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1385,regnet_y_400mf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.172124118,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1386,mobilenet_v2,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.130707961,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1387,vgg19,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.190800807,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1388,detection.keypointrcnn_resnet50_fpn,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.160967534,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1389,resnext50_32x4d,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.068916637,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1390,efficientnet_b1,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,1.0,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
1391,convnext_large,"Googlenet has 9 Inception modules, 22 layers deep (21 convolutional layers and one fully connected layer) when counting only layers with parameters (or 27 layers if we also count pooling) and 12 times fewer parameters than AlexNet",googlenet,0.16784847,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1392,regnet_x_8gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.178133541,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1393,mobilenet_v3_large,Replacing ReLU with GeLU,convnext_tiny,0.012541346,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1394,quantization.shufflenet_v2_x1_5,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.102340034,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1395,quantization.mobilenet_v3_large,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.031602012,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1396,video.r2plus1d_18,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.105215297,1x32x32,video classification,Null,TV
1397,detection.ssdlite320_mobilenet_v3_large,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.188554186,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1398,vgg13_bn,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.154955005,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1399,shufflenet_v2_x0_5,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.207050761,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1400,quantization.mobilenet_v3_large,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.193288107,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1401,convnext_base,The network design includes the use of a hard swish activation and squeeze-and-excitation modules in the MBConv blocks,mobilenet_v3_small,0.179428535,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1402,regnet_x_3_2gf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_x_3_2gf,1.0,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1403,mnasnet0_5,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.205409179,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1404,resnext101_32x8d,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.224468008,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1405,resnext101_32x8d,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.209299956,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1406,inception_v3,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.151257598,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1407,segmentation.lraspp_mobilenet_v3_large,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.122132892,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1408,vgg16,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.192400851,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1409,efficientnet_b3,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.238373578,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1410,resnext50_32x4d,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.147876304,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1411,squeezenet1_0,The key building block is an Inception Module,inception_v3,0.218945969,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1412,convnext_tiny,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.100036846,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1413,regnet_x_16gf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.173064671,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1414,regnet_y_1_6gf,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,1.0,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1415,squeezenet1_0,"It has 11 weight layers in total, so the name VGG11",vgg11,0.205745136,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1416,efficientnet_b1,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.131003254,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
1417,inception_v3,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.147546759,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1418,detection.retinanet_resnet50_fpn,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.187265757,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1419,mobilenet_v3_small,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.114855818,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1420,regnet_y_800mf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.116412592,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1421,regnet_y_1_6gf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.178930597,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1422,segmentation.deeplabv3_mobilenet_v3_large,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.151936813,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1423,regnet_y_8gf,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,1.0,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1424,regnet_y_8gf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.162031105,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1425,resnet50,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.144174454,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
1426,vgg16_bn,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.194372156,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1427,mobilenet_v3_large,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.191179849,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1428,segmentation.fcn_resnet101,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.099538377,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1429,segmentation.deeplabv3_resnet101,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.111956279,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1430,detection.maskrcnn_resnet50_fpn,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1431,efficientnet_b6,The key building block is an Inception Module,inception_v3,0.200459599,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1432,shufflenet_v2_x1_0,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.156537902,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1433,densenet121,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.140198402,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1434,densenet201,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.188913524,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1435,mnasnet0_5,The required minimum input size of the model is 32x32,vgg11,0.147252078,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1436,quantization.resnext101_32x8d,It is the simplest of all other vgg versions,vgg11,0.164700513,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1437,quantization.shufflenet_v2_x1_5,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.16356248,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1438,detection.fasterrcnn_mobilenet_v3_large_fpn,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.181510539,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
1439,convnext_large,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.080677594,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1440,detection.retinanet_resnet50_fpn,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.196689087,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1441,densenet201,Densenet201 is used for image classification,densenet201,1.0,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1442,mnasnet0_75,"This effectively doubles the number of nonlinearities compared to a network using full 3D convolutions for the same number of parameters, thus rendering the model capable of representing more complex functions",video.r2plus1d_18,0.223053118,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1443,efficientnet_b3,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.195850952,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1444,video.mc3_18,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.088570791,1x32x32,video classification,Null,TV
1445,densenet161,"Dense blocks, Batch Normalization(BN)",densenet161,1.0,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
1446,shufflenet_v2_x0_5,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.088786834,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1447,efficientnet_b5,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.240488283,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1448,convnext_large,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.147674792,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1449,vgg16,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.153486202,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1450,vgg11,The required minimum input size of the model is 32x32,vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1451,regnet_y_1_6gf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.187458283,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1452,inception_v3,"The authors demonstrate its efficacy with the help of a simple, single stage detector, called RetinaNet, which predicts objects by dense sampling of the input image in location, scale and aspect ratio",detection.retinanet_resnet50_fpn,0.210427554,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1453,video.mc3_18,"We show that MC ResNets yield roughly a 3-4% gain in clip-level accuracy over 2D ResNets of comparable capacity and they match the performance of 3D ResNets, which have 3 times as many parameters",video.mc3_18,1.0,1x32x32,video classification,Null,TV
1454,regnet_x_32gf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.174329858,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1455,squeezenet1_1,It is the simplest of all other vgg versions,vgg11,0.15216334,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1456,mnasnet0_5,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.204747707,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1457,vit_b_16,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.129922303,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1458,regnet_y_800mf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.139032944,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1459,segmentation.fcn_resnet101,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.043254172,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1460,efficientnet_b0,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.176497304,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1461,squeezenet1_1,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_1,1.0,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1462,vit_l_32,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.112298397,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1463,quantization.resnext101_32x8d,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.116133414,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1464,regnet_x_3_2gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.193008723,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1465,vit_b_16,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.102769553,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1466,vit_l_32,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.064900788,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1467,efficientnet_b0,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.162486827,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1468,segmentation.deeplabv3_resnet101,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,1.0,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1469,convnext_small,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.034078602,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1470,resnext50_32x4d,VGG19 has 19.6 billion FLOPs,vgg19,0.139315831,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1471,efficientnet_b5,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.139716831,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1472,vit_l_32,lraspp_mobilenet_v3_large is used for semantic segmentation,segmentation.lraspp_mobilenet_v3_large,0.218498432,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1473,regnet_y_16gf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.200039934,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1474,segmentation.deeplabv3_mobilenet_v3_large,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.162776886,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1475,segmentation.deeplabv3_mobilenet_v3_large,Note that this model does not include any fine-tuned heads,vit_b_16,0.13954159,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1476,mobilenet_v3_small,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.128256827,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1477,mobilenet_v3_large,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.232947069,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1478,convnext_tiny,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.152187207,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1479,segmentation.fcn_resnet50,This model has 3.8 billion FLOPs,resnet50,0.043165905,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1480,wide_resnet50_2,adopted from BERT,vit_b_16,0.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1481,vgg19,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.142511498,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1482,resnet152,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.173827196,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1483,vit_l_32,They stack residual blocks ontop of each other to form network,vgg19_bn,0.097913794,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1484,segmentation.lraspp_mobilenet_v3_large,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.139565165,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1485,segmentation.fcn_resnet50,The number of parameters is 3.4 million,mobilenet_v2,0.044069796,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1486,resnet34,It is the simplest of all other vgg versions,vgg11,0.134498189,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1487,segmentation.deeplabv3_mobilenet_v3_large,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.082215893,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1488,regnet_y_1_6gf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.153040987,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1489,alexnet,"60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax",alexnet,1.0,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1490,shufflenet_v2_x2_0,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.232494254,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1491,regnet_x_32gf,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.162210858,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1492,shufflenet_v2_x0_5,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.121702876,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1493,densenet161,"A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other",densenet161,1.0,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
1494,inception_v3,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x0_5,0.194150505,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1495,efficientnet_b5,efficientnet_b5 is used for image classification,efficientnet_b5,1.0,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1496,convnext_base,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.125826272,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1497,vit_l_16,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.086896699,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1498,regnet_y_800mf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.157629744,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1499,vit_b_16,They stack residual blocks ontop of each other to form network: for example  a ResNet-34 has 34 layers using these blocks,resnet34,0.124982556,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1500,resnet34,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.117054103,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1501,convnext_tiny,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.123670109,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1502,convnext_tiny,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.127665401,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1503,resnet34,resnet34 is used for image classification,resnet34,1.0,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1504,alexnet,"It consists of convolutions, max pooling and dense layers as the basic building blocks",alexnet,1.0,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1505,regnet_x_1_6gf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.102952632,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1506,regnet_x_400mf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.16958908,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1507,mnasnet0_5,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.244395887,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1508,regnet_y_400mf,adopted from BERT,vit_b_16,0.010435705,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1509,resnet18,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.131645854,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1510,vgg16_bn,The key building block is an Inception Module,inception_v3,0.183624748,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1511,convnext_large,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.143479826,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1512,convnext_small,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.178633843,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1513,detection.ssdlite320_mobilenet_v3_large,"A ConvNet which has the design simplicity of Convolutional Neural Networks while having better accuracy, performance and scalability than Vision Transformers.",convnext_small,0.174485996,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1514,detection.fasterrcnn_resnet50_fpn,"As a whole, Faster R-CNN consists of two modules",detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1515,regnet_y_400mf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.07800011,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1516,segmentation.lraspp_mobilenet_v3_large,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.13820072,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1517,inception_v3,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.223376659,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1518,resnet34,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.1043041,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1519,detection.keypointrcnn_resnet50_fpn,Constructs a Keypoint R-CNN model with a ResNet-50-FPN backbone,detection.keypointrcnn_resnet50_fpn,1.0,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1520,detection.keypointrcnn_resnet50_fpn,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.094145401,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1521,mobilenet_v3_small,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.17378898,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1522,efficientnet_b4,Note that this model does not include any fine-tuned heads,vit_b_16,0.199528983,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1523,resnext101_32x8d,The key building block is an Inception Module,inception_v3,0.1652775,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1524,video.r2plus1d_18,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.081448761,1x32x32,video classification,Null,TV
1525,mnasnet0_5,The number of parameters is 3.4 million,mobilenet_v2,0.118531098,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1526,segmentation.deeplabv3_resnet50,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.176794425,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1527,resnet18,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.107656942,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1528,regnet_y_16gf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.152213409,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1529,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.080170448,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1530,efficientnet_b0,They use option 2 for increasing dimensions,resnet50,0.218936519,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1531,vit_l_32,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.125865874,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1532,quantization.resnet18,quantization.resnet18 is the quantized model of resnet18,quantization.resnet18,1.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1533,inception_v3,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.157520652,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1534,quantization.inception_v3,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.187350287,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1535,segmentation.deeplabv3_mobilenet_v3_large,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.091837297,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1536,squeezenet1_0,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,1.0,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1537,mnasnet1_3,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.126970911,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1538,quantization.googlenet,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.139131196,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1539,inception_v3,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.15522032,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1540,video.mc3_18,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.228681707,1x32x32,video classification,Null,TV
1541,inception_v3,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.125697481,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1542,detection.fasterrcnn_resnet50_fpn,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.093663448,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1543,vit_l_16,Network architecture of the 3D ResNet,video.r3d_18,0.14529947,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1544,vgg16,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.142627193,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1545,convnext_small,"This is a model that has been pre-trained on the ImageNet dataset--a dataset that has 100,000+ images across 200 different classes",resnet34,0.147546361,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1546,vit_b_32,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.080940331,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1547,mnasnet1_3,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.159074984,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1548,regnet_x_8gf,ReLU non-linearity as activation functions,vgg11,0.165753297,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1549,mnasnet0_75,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.183078036,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1550,resnet152,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.157258086,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1551,alexnet,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.172943408,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1552,quantization.googlenet,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.109193336,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1553,resnet152,ResNet-152 can obtain 4.49% error rate,resnet152,1.0,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1554,densenet161,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.206438931,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
1555,squeezenet1_1,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.186378931,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1556,segmentation.lraspp_mobilenet_v3_large,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.127662631,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1557,segmentation.fcn_resnet50,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.145069405,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1558,regnet_y_32gf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.169289333,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1559,quantization.shufflenet_v2_x2_0,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.184902751,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1560,resnext50_32x4d,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.142777858,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1561,efficientnet_b4,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.177950053,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1562,efficientnet_b1,It is the simplest of all other vgg versions,vgg11,0.189396198,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
1563,shufflenet_v2_x1_5,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.145275423,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1564,quantization.shufflenet_v2_x1_0,It is the simplest of all other vgg versions,vgg11,0.179348333,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1565,mnasnet0_75,This model has 3.8 billion FLOPs,resnet50,0.119948662,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1566,resnet34,The number of parameters is 3.4 million,mobilenet_v2,0.075386362,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1567,regnet_y_400mf,2D max pooling in between the weight layers,vgg11,0.16806569,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1568,efficientnet_b5,Note that this model does not include any fine-tuned heads,vit_b_16,0.193397257,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1569,quantization.mobilenet_v3_large,mobilenet_v3_large is used for image classification,quantization.mobilenet_v3_large,1.0,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1570,wide_resnet50_2,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.233660898,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1571,detection.maskrcnn_resnet50_fpn,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.065725852,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1572,googlenet,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.072542867,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1573,resnext101_32x8d,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.170645114,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1574,segmentation.deeplabv3_resnet101,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet101,1.0,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1575,regnet_x_8gf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.099073046,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1576,vgg13,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.164961882,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1577,video.r2plus1d_18,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.096615481,1x32x32,video classification,Null,TV
1578,vit_b_16,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.081392586,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1579,vit_b_32,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.153014071,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1580,regnet_x_8gf,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.127328917,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1581,quantization.shufflenet_v2_x0_5,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.144343328,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1582,shufflenet_v2_x0_5,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.164125584,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1583,segmentation.fcn_resnet101,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.023705618,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1584,wide_resnet101_2,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.179093386,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1585,convnext_tiny,MobileNet v3-Large has 5.4 million parameters,mobilenet_v3_large,0.137272012,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1586,shufflenet_v2_x1_0,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.126760284,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1587,regnet_x_8gf,in this section we use the 8 billion flops (8GF) regime,regnet_x_8gf,1.0,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1588,wide_resnet50_2,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.201249062,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1589,regnet_y_16gf,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.121611134,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1590,vit_l_16,The number of parameters is 3.4 million,mobilenet_v2,0.093206382,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1591,quantization.shufflenet_v2_x2_0,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.124689829,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1592,regnet_y_3_2gf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.153682791,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1593,detection.fasterrcnn_mobilenet_v3_large_320_fpn,The required minimum input size of the model is 21x21,squeezenet1_0,0.178179267,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1594,quantization.resnet18,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",quantization.resnet18,1.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1595,regnet_x_1_6gf,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.196645992,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1596,regnet_x_800mf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.117220997,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1597,detection.maskrcnn_resnet50_fpn,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.120043034,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1598,alexnet,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.163452292,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1599,regnet_x_8gf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.187737748,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1600,vit_l_16,Constructs a Fully-Convolutional Network model with a ResNet-101 backbone,segmentation.fcn_resnet101,0.106212369,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1601,regnet_x_16gf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.111898778,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1602,resnext101_32x8d,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.236719683,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1603,convnext_tiny,"Googlenet has 9 Inception modules, 22 layers deep (21 convolutional layers and one fully connected layer) when counting only layers with parameters (or 27 layers if we also count pooling) and 12 times fewer parameters than AlexNet",googlenet,0.168998878,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1604,mobilenet_v3_large,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.090910292,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1605,convnext_tiny,Constructs a DeepLabV3 model with a MobileNetV3-Large backbone,segmentation.deeplabv3_mobilenet_v3_large,0.138770656,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1606,regnet_y_1_6gf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.131929769,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1607,densenet169,Densenet is used for image classification,densenet169,1.0,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
1608,mobilenet_v3_large,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.160395128,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1609,video.mc3_18,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.043456051,1x32x32,video classification,Null,TV
1610,mnasnet0_5,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.220807555,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1611,mnasnet0_5,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.153691098,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1612,vgg13,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.166120937,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1613,regnet_x_32gf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.079272518,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1614,regnet_x_400mf,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.158180119,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1615,quantization.inception_v3,densenet169 is More acurate than DenseNet-121,densenet169,0.139273079,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1616,quantization.googlenet,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.130267879,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1617,vit_l_16,"The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks",efficientnet_b0,0.075608262,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1618,convnext_tiny,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.044038803,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1619,quantization.resnext101_32x8d,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.179384157,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1620,efficientnet_b4,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.171123482,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1621,detection.fasterrcnn_mobilenet_v3_large_320_fpn,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.180927891,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1622,video.r2plus1d_18,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.169490542,1x32x32,video classification,Null,TV
1623,efficientnet_b5,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.12299172,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1624,detection.ssd300_vgg16,ReLU non-linearity as activation functions,vgg11,0.144217368,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1625,inception_v3,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.117187387,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1626,regnet_y_800mf,ReLU non-linearity as activation functions,vgg11,0.156749997,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1627,quantization.shufflenet_v2_x0_5,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,quantization.shufflenet_v2_x0_5,1.0,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1628,efficientnet_b4,It is the simplest of all other vgg versions,vgg11,0.184741871,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1629,segmentation.fcn_resnet101,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.247022976,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1630,quantization.inception_v3,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.113719464,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1631,detection.ssdlite320_mobilenet_v3_large,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.177275637,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1632,detection.retinanet_resnet50_fpn,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.192876266,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1633,vit_l_16,Constructs a RegNetX_8GF architecture,regnet_x_8gf,0.17756005,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1634,efficientnet_b7,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.17985369,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
1635,vgg13_bn,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.121315954,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1636,resnet50,adopted from BERT,vit_b_16,0.0,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
1637,shufflenet_v2_x1_0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.209108075,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1638,detection.ssd300_vgg16,ssd is a method for detecting objects in images using a single deep neural network,detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1639,vit_l_32,"resnext50_32x4d is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",resnext50_32x4d,0.135959024,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1640,quantization.shufflenet_v2_x1_0,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,quantization.shufflenet_v2_x1_0,1.0,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1641,vit_l_16,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.159892781,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1642,mnasnet1_3,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.20027715,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1643,convnext_base,Constructs an SSDlite model with input size 320x320 and a MobileNetV3 Large backbone,detection.ssdlite320_mobilenet_v3_large,0.108988225,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1644,regnet_x_3_2gf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.170816893,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1645,regnet_y_800mf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.174714549,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1646,resnet101,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.198451377,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
1647,quantization.shufflenet_v2_x1_5,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.14669442,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1648,densenet169,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.127448851,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
1649,detection.retinanet_resnet50_fpn,The required minimum input size of the model is 21x21,squeezenet1_0,0.147129762,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1650,mobilenet_v3_large,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.173735583,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1651,vit_b_32,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.087703126,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1652,regnet_x_3_2gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.163492824,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1653,inception_v3,"Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet)",squeezenet1_0,0.182997896,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1654,shufflenet_v2_x1_5,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.158128658,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1655,quantization.inception_v3,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.133592191,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1656,mnasnet1_0,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.216418888,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
1657,mobilenet_v3_small,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.085925775,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1658,convnext_tiny,They stack residual blocks ontop of each other to form network: for example  a ResNet-152 has 152 layers using these blocks,resnet152,0.156072745,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1659,vgg11_bn,SqueezeNet 1.0 achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters,squeezenet1_0,0.186014554,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
1660,regnet_y_3_2gf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_3_2gf,1.0,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1661,densenet201,VGG19 has 19.6 billion FLOPs,vgg19,0.134110773,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1662,vgg11,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.149136447,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1663,convnext_large,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.082970146,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1664,quantization.inception_v3,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.127659461,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1665,efficientnet_b6,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.111246526,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1666,quantization.shufflenet_v2_x1_5,Replacing ReLU with GeLU,convnext_tiny,0.093926795,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1667,vit_b_16,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.129948285,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1668,vgg16,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,0.227675047,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1669,efficientnet_b6,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.18766379,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1670,quantization.shufflenet_v2_x0_5,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.195851936,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1671,regnet_x_800mf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.167238785,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1672,densenet161,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.052866626,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
1673,segmentation.fcn_resnet50,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.178234969,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1674,quantization.inception_v3,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.133007794,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1675,convnext_large,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.059721293,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1676,quantization.shufflenet_v2_x1_5,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.100257049,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1677,efficientnet_b0,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.134367633,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1678,quantization.mobilenet_v2,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.062140581,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
1679,vgg11,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.214460657,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1680,shufflenet_v2_x2_0,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.10697477,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1681,video.mc3_18,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.144796377,1x32x32,video classification,Null,TV
1682,mnasnet0_75,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.138987403,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1683,efficientnet_b4,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.217959836,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1684,vgg11_bn,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.175751667,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
1685,segmentation.deeplabv3_mobilenet_v3_large,"Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384",vit_l_32,0.156262292,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1686,vgg13_bn,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.136709306,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1687,regnet_x_32gf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.14291655,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1688,efficientnet_b0,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.200282407,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1689,resnext50_32x4d,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.193827035,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1690,shufflenet_v2_x1_5,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.173840555,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1691,vit_b_16,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.143872675,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1692,segmentation.fcn_resnet101,vit_l_16 is used for image classification,vit_l_16,0.139840012,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1693,densenet169,ReLU non-linearity as activation functions,vgg11,0.113700032,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
1694,vgg13,ReLU non-linearity as activation functions,vgg11,0.130556295,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1695,shufflenet_v2_x1_0,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.105361831,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1696,inception_v3,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.155194068,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1697,vit_b_32,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.121856661,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1698,shufflenet_v2_x0_5,Note that this model does not include any fine-tuned heads,vit_b_16,0.219055592,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1699,inception_v3,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.108528238,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1700,quantization.shufflenet_v2_x1_0,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.132948428,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1701,googlenet,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.056684456,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1702,vgg19_bn,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.132995434,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
1703,wide_resnet101_2,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.168932637,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1704,detection.fasterrcnn_resnet50_fpn,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.077231847,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1705,regnet_x_8gf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.168628431,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1706,regnet_y_8gf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.10388319,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1707,video.r3d_18,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.0559184,1x32x32,video classification,Null,TV
1708,vit_l_32,This model has 3.8 billion FLOPs,resnet50,0.132191017,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1709,resnet18,This model has 3.8 billion FLOPs,resnet50,0.090840106,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1710,vgg11,11 weight layers (convolutional + fully connected),vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1711,detection.ssdlite320_mobilenet_v3_large,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.126379077,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1712,densenet161,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.146827192,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
1713,detection.maskrcnn_resnet50_fpn,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.061705281,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1714,detection.ssd300_vgg16,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.147495434,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1715,segmentation.deeplabv3_resnet50,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.075436669,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1716,resnet152,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.119554476,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1717,quantization.resnext101_32x8d,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.200245855,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1718,mnasnet0_5,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.178818971,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1719,segmentation.fcn_resnet50,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.183155508,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1720,efficientnet_b3,EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient,efficientnet_b3,1.0,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1721,vgg13,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.226446047,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1722,convnext_tiny,This model has 3.8 billion FLOPs,resnet50,0.10966067,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1723,efficientnet_b6,Note that this model does not include any fine-tuned heads,vit_b_16,0.193818219,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1724,segmentation.deeplabv3_resnet101,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384",vit_l_32,0.181652726,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1725,efficientnet_b6,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.179625098,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1726,quantization.resnext101_32x8d,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.230849485,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1727,regnet_y_32gf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.17182439,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1728,mnasnet0_5,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.113048236,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1729,shufflenet_v2_x2_0,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.146642356,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1730,regnet_x_8gf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.169955843,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1731,efficientnet_b7,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.21694693,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
1732,mnasnet0_75,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.159275504,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1733,segmentation.deeplabv3_resnet101,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.074278471,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1734,convnext_small,This model has 3.8 billion FLOPs,resnet50,0.100097085,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1735,resnet152,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.134681263,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1736,shufflenet_v2_x0_5,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.228454353,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1737,inception_v3,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.118811926,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1738,detection.ssdlite320_mobilenet_v3_large,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x0_5,0.181857975,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1739,quantization.shufflenet_v2_x0_5,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.04272165,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1740,shufflenet_v2_x1_5,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.167982588,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1741,mnasnet1_3,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.243071998,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1742,quantization.resnext101_32x8d,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.14295354,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1743,vgg16_bn,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.13944996,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1744,video.r3d_18,You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close,vit_b_32,0.171508948,1x32x32,video classification,Null,TV
1745,regnet_x_3_2gf,2D max pooling in between the weight layers,vgg11,0.158312794,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1746,googlenet,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.080882405,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1747,regnet_x_1_6gf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.175249571,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1748,resnet50,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.129179964,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
1749,regnet_x_8gf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.081242318,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1750,vgg11,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.154192799,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1751,detection.ssd300_vgg16,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.081031244,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1752,vgg19,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1753,vgg11,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1754,regnet_y_32gf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.080243695,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1755,video.mc3_18,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.075704335,1x32x32,video classification,Null,TV
1756,segmentation.lraspp_mobilenet_v3_large,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.155713216,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1757,quantization.inception_v3,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.113752479,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1758,regnet_y_800mf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.095135593,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1759,regnet_x_8gf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.176167437,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1760,mnasnet1_3,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.169555642,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1761,vit_b_32,This model has been created using Multilingual Knowledge Distillation,vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1762,detection.keypointrcnn_resnet50_fpn,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.129399733,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1763,vgg13_bn,quantization.resnet50 is the quantized model of resnet50,quantization.resnet50,0.188196509,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1764,quantization.googlenet,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,1.0,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1765,regnet_y_16gf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.170198838,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1766,resnext50_32x4d,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.139521932,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1767,quantization.inception_v3,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.086771557,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1768,vgg13,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.155753411,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1769,vit_l_32,Constructs a DeepLabV3 model with a ResNet-50 backbone,segmentation.deeplabv3_resnet50,0.157505317,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1770,densenet121,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.166469983,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1771,mobilenet_v3_small,MobileNet v3-Small has 2.5 million parameters,mobilenet_v3_small,1.0,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1772,vgg16,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.2091617,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1773,mobilenet_v2,ResNet-152 can obtain 4.49% error rate,resnet152,0.25450179,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1774,quantization.resnet18,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.123846055,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1775,mnasnet1_3,The required minimum input size of the model is 21x21,squeezenet1_0,0.177273144,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1776,shufflenet_v2_x0_5,VGG19 has 19.6 billion FLOPs,vgg19,0.1186549,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1777,resnext101_32x8d,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.256991193,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1778,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.181770247,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1779,mobilenet_v3_large,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.159461467,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1780,convnext_small,Constructs a DeepLabV3 model with a MobileNetV3-Large backbone,segmentation.deeplabv3_mobilenet_v3_large,0.143776288,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1781,segmentation.deeplabv3_resnet50,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.132805959,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1782,regnet_y_16gf,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.170862954,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1783,densenet121,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.052640085,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1784,vit_b_32,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.180125102,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1785,regnet_x_3_2gf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.138724769,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1786,quantization.shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,1.0,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1787,vgg16_bn,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.220502145,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1788,vgg13,The key building block is an Inception Module,inception_v3,0.179349944,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1789,convnext_base,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.066510237,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1790,resnet18,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.180219915,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1791,squeezenet1_0,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,1.0,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1792,segmentation.deeplabv3_resnet50,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.071162582,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1793,convnext_base,"The number of channels in outer 1x1 convolutions is the same, Wide ResNet-50-2 has 2048-1024-2048 channels",wide_resnet50_2,0.225102782,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1794,efficientnet_b6,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.152492743,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1795,regnet_x_3_2gf,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.124149963,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1796,vit_b_32,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.139465243,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1797,segmentation.fcn_resnet50,adopted from BERT,vit_b_16,0.001218396,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1798,convnext_large,adopted from BERT,vit_b_16,0.017651962,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1799,quantization.resnext101_32x8d,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.166622341,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1800,vit_l_32,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.12418847,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1801,regnet_x_16gf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.170915069,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1802,convnext_base,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.068921183,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1803,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"fasterrcnn_mobilenet_v3_large_320_fpn is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1804,quantization.shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,1.0,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1805,segmentation.lraspp_mobilenet_v3_large,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.191892381,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1806,efficientnet_b6,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.245232359,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1807,shufflenet_v2_x2_0,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.048324595,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1808,detection.keypointrcnn_resnet50_fpn,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.143655467,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1809,densenet161,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.058793694,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
1810,vit_b_32,"This effectively doubles the number of nonlinearities compared to a network using full 3D convolutions for the same number of parameters, thus rendering the model capable of representing more complex functions",video.r2plus1d_18,0.163591652,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1811,segmentation.fcn_resnet101,"Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded",vit_b_16,0.16606961,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1812,convnext_base,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.187313872,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1813,vit_l_16,Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks,wide_resnet50_2,0.118684782,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1814,detection.fasterrcnn_mobilenet_v3_large_320_fpn,The required minimum input size of the model is 29x29,densenet121,0.19113763,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1815,vgg19,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.094272559,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1816,mnasnet1_3,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.224779487,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1817,regnet_y_16gf,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.145916854,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1818,detection.retinanet_resnet50_fpn,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.157578673,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1819,shufflenet_v2_x1_0,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.165624056,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1820,squeezenet1_1,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.231560635,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1821,vit_b_16,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.169654639,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1822,efficientnet_b4,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.207919011,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1823,video.r2plus1d_18,ResNet (2+1)D  is a variation of ResNet 3D is a type of model for video that employs 3D convolutions,video.r2plus1d_18,1.0,1x32x32,video classification,Null,TV
1824,densenet161,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.184949378,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
1825,inception_v3,Constructs a RegNetX_400MF architecture,regnet_x_400mf,0.210222488,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1826,quantization.resnext101_32x8d,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.16370006,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1827,quantization.mobilenet_v3_large,quantization.mobilenet_v3_large is the quantized model of mobilenet_v3_large,quantization.mobilenet_v3_large,1.0,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1828,convnext_tiny,2D max pooling in between the weight layers,vgg11,0.098815908,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1829,vgg19_bn,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.165479377,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
1830,quantization.shufflenet_v2_x1_0,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.148006669,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1831,mobilenet_v2,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.120169369,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1832,shufflenet_v2_x1_5,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.202924234,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1833,densenet201,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.19779332,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1834,regnet_x_16gf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.172850663,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1835,regnet_x_8gf,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.205719385,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1836,quantization.resnext101_32x8d,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.158322815,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1837,inception_v3,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.163350277,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1838,regnet_x_3_2gf,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.153180517,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1839,quantization.resnet18,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.175381436,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1840,efficientnet_b3,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.123531447,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1841,segmentation.fcn_resnet50,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.03301068,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1842,efficientnet_b2,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.231628698,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
1843,segmentation.deeplabv3_resnet50,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.141286132,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1844,efficientnet_b5,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.256038502,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1845,regnet_y_16gf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.167230324,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1846,resnet18,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.198911244,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1847,mobilenet_v2,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.159289083,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1848,resnet152,The number of parameters is 3.4 million,mobilenet_v2,0.084935757,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1849,convnext_base,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.152831413,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1850,vit_l_32,They stack residual blocks ontop of each other to form network: for example  a ResNet-152 has 152 layers using these blocks,resnet152,0.125772325,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1851,regnet_x_1_6gf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.107776284,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1852,quantization.inception_v3,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.110156164,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1853,mobilenet_v3_large,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.180465329,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1854,quantization.shufflenet_v2_x1_5,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.206471089,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1855,regnet_y_400mf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.19483785,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1856,inception_v3,"ViT-B-32 is slightly faster than ResNet50 and it performs much worse on the 9M subset, but better on 90M+ subsets",vit_b_32,0.183651446,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1857,vgg13,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.18361805,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1858,vit_b_32,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.136254008,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1859,detection.keypointrcnn_resnet50_fpn,VGG19 has 19.6 billion FLOPs,vgg19,0.079294054,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1860,densenet121,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.218471251,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1861,densenet121,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.187284184,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1862,shufflenet_v2_x0_5,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.15427556,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1863,regnet_y_3_2gf,The required minimum input size of the model is 21x21,squeezenet1_0,0.19932919,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1864,quantization.googlenet,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.191313619,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1865,vgg11,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.089912107,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1866,quantization.shufflenet_v2_x1_0,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.124893896,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1867,regnet_y_32gf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.16009078,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1868,quantization.shufflenet_v2_x2_0,Constructs a ShuffleNetV2 with 2.0x output channels,quantization.shufflenet_v2_x2_0,1.0,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1869,vgg13,adopted from BERT,vit_b_16,0.00735986,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1870,video.r3d_18,VGG19 has 19.6 billion FLOPs,vgg19,0.130551439,1x32x32,video classification,Null,TV
1871,convnext_base,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.109465551,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1872,segmentation.lraspp_mobilenet_v3_large,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.223439476,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1873,densenet201,ReLU non-linearity as activation functions,vgg11,0.122803647,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1874,mnasnet0_5,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.110822176,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1875,regnet_x_32gf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.098864668,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1876,regnet_y_32gf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_32gf,1.0,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1877,video.r3d_18,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.1445924,1x32x32,video classification,Null,TV
1878,quantization.resnet18,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.12999847,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1879,efficientnet_b2,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.153104623,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
1880,efficientnet_b3,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.17664111,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1881,regnet_y_8gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.197669954,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1882,quantization.mobilenet_v3_large,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.141161008,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1883,vgg13,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.183840368,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1884,mobilenet_v3_small,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.162796409,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1885,resnet34,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.100306579,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1886,regnet_x_400mf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.151828419,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1887,efficientnet_b4,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.182474932,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1888,quantization.shufflenet_v2_x2_0,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.101870389,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1889,convnext_tiny,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.138875111,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1890,segmentation.fcn_resnet101,The required minimum input size of the model is 17x17,squeezenet1_1,0.179764269,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1891,shufflenet_v2_x1_5,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.235324364,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1892,vit_b_16,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,0.161424484,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1893,video.r2plus1d_18,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.097030837,1x32x32,video classification,Null,TV
1894,convnext_small,"Each 2-layer block is replaced in the 34-layer net with this 3-layer bottleneck block, resulting in a 50-layer ResNet",resnet50,0.167777302,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1895,resnet34,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.217341574,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1896,segmentation.deeplabv3_mobilenet_v3_large,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.161582208,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1897,quantization.shufflenet_v2_x1_5,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",quantization.shufflenet_v2_x1_5,1.0,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1898,densenet121,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.157092268,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1899,vit_l_16,"RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks",detection.retinanet_resnet50_fpn,0.115112594,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1900,vgg19,vgg19 is used for image classification,vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1901,regnet_x_800mf,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.123583848,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1902,quantization.shufflenet_v2_x0_5,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.131883065,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1903,regnet_y_8gf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.150733943,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1904,video.r3d_18,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.142089571,1x32x32,video classification,Null,TV
1905,mnasnet0_75,Note that this model does not include any fine-tuned heads,vit_b_16,0.158958594,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1906,vgg16,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.180175557,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1907,quantization.resnet18,ResNet-18 is a convolutional neural network that is 18 layers deep,quantization.resnet18,1.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1908,resnet18,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.164687112,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1909,regnet_y_800mf,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_800mf,1.0,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1910,regnet_x_8gf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.154980644,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1911,shufflenet_v2_x0_5,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.171306369,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1912,vgg16_bn,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.170061359,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1913,quantization.resnext101_32x8d,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.186914393,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1914,regnet_x_32gf,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.203316311,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1915,convnext_large,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.166945646,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1916,video.mc3_18,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.142444487,1x32x32,video classification,Null,TV
1917,quantization.shufflenet_v2_x1_5,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.177247334,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1918,detection.retinanet_resnet50_fpn,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.057288789,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1919,detection.fasterrcnn_resnet50_fpn,RPN and Fast R-CNN are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look,detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1920,vgg16_bn,ReLU non-linearity as activation functions,vgg11,0.160842802,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1921,googlenet,It is the simplest of all other vgg versions,vgg11,0.143193948,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1922,segmentation.deeplabv3_resnet50,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.122889269,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1923,vgg13_bn,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,1.0,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1924,detection.keypointrcnn_resnet50_fpn,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.147343755,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1925,quantization.googlenet,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,1.0,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1926,quantization.resnet50,adopted from BERT,vit_b_16,0.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
1927,mnasnet0_5,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.11146458,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1928,vit_b_16,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.080707046,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1929,convnext_base,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.146513788,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1930,detection.fasterrcnn_mobilenet_v3_large_320_fpn,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.143760813,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1931,convnext_base,obtained by scaling up the baseline network,efficientnet_b1,0.139089509,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1932,convnext_base,They stack residual blocks ontop of each other to form network,vgg19_bn,0.135418468,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1933,mnasnet1_3,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.066024862,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1934,regnet_x_8gf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.213564258,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1935,video.r2plus1d_18,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.184377572,1x32x32,video classification,Null,TV
1936,resnet50,Replacing ReLU with GeLU,convnext_tiny,0.13211692,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
1937,efficientnet_b6,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.253211662,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1938,squeezenet1_0,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.086957081,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1939,regnet_x_800mf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.150851127,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1940,inception_v3,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.148677424,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1941,resnext101_32x8d,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.15825405,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1942,convnext_base,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.111298885,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1943,segmentation.fcn_resnet50,Replacing ReLU with GeLU,convnext_tiny,0.139393152,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1944,regnet_y_800mf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.190284262,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1945,vit_b_16,They stack residual blocks on top of each other to form network: for example  a ResNet-50 has fifty layers using these blocks,resnet50,0.110977287,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1946,regnet_y_400mf,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.142408149,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1947,regnet_y_800mf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.113073814,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1948,convnext_tiny,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.207095925,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1949,regnet_y_16gf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.082549361,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1950,quantization.resnext101_32x8d,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.210796732,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1951,mnasnet0_75,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.044343658,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1952,efficientnet_b1,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.129001586,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
1953,regnet_y_8gf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.177294613,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1954,vit_b_16,Faster R-CNN builds a network that has only a single stage,detection.fasterrcnn_resnet50_fpn,0.155645059,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1955,quantization.inception_v3,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.09827387,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1956,quantization.resnext101_32x8d,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.176479927,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
1957,efficientnet_b5,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.140891705,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1958,vit_l_16,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.107357881,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1959,vit_b_16,Constructs a Fully-Convolutional Network model with a ResNet-101 backbone,segmentation.fcn_resnet101,0.104469107,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1960,vgg13_bn,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.104801493,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1961,regnet_y_1_6gf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.146212219,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1962,regnet_y_32gf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.144460451,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1963,efficientnet_b5,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.073287729,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1964,shufflenet_v2_x0_5,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.153006862,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1965,vgg11,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.133504088,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1966,mnasnet1_3,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.048019746,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1967,regnet_x_800mf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_x_800mf,1.0,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1968,efficientnet_b2,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.18186506,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
1969,video.r2plus1d_18,It is the simplest of all other vgg versions,vgg11,0.140368213,1x32x32,video classification,Null,TV
1970,video.r2plus1d_18,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.100353404,1x32x32,video classification,Null,TV
1971,vit_b_16,"Using the DenseNet-121 architecture to understand the table, we can see that every dense block has varying number of layers (repetitions) featuring two convolutions each; a 1x1 sized kernel as the bottleneck layer and 3x3 kernel to perform the convolution operation",densenet121,0.157165144,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1972,efficientnet_b3,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.174145994,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1973,mnasnet0_5,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.137618499,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1974,shufflenet_v2_x0_5,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.192884926,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1975,mnasnet0_75,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.111672475,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1976,convnext_tiny,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.242160312,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1977,regnet_y_8gf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.177954003,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1978,shufflenet_v2_x2_0,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.160529354,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1979,regnet_x_32gf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.119725281,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1980,quantization.mobilenet_v2,"Inverted residual structure, Linear Bottleneck",quantization.mobilenet_v2,1.0,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
1981,regnet_x_800mf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.167931048,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1982,regnet_x_800mf,They use option 2 for increasing dimensions,resnet50,0.178168596,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1983,regnet_x_3_2gf,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.141536494,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1984,detection.ssdlite320_mobilenet_v3_large,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.166584882,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1985,quantization.resnet18,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.103119774,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1986,convnext_base,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.107007562,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1987,mnasnet1_3,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.084207805,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1988,resnext101_32x8d,"In particular, in this section we use the 400 million flops (400MF) regime and train each sampled model for 10 epochs on the ImageNet dataset",regnet_y_400mf,0.276703402,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1989,vgg11,Constructs a RegNetX_400MF architecture,regnet_x_400mf,0.187357993,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1990,regnet_y_3_2gf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.182110687,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1991,segmentation.fcn_resnet50,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.165571517,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1992,alexnet,Replacing ReLU with GeLU,convnext_tiny,0.12715559,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1993,regnet_x_3_2gf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.151300492,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1994,detection.ssd300_vgg16,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1995,segmentation.deeplabv3_resnet101,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.199656862,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1996,densenet169,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.187782639,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
1997,detection.ssdlite320_mobilenet_v3_large,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.128605431,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1998,resnext50_32x4d,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.167563818,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1999,vit_b_16,Constructs a RegNetX_8GF architecture,regnet_x_8gf,0.17929918,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2000,shufflenet_v2_x1_5,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.105846605,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
2001,shufflenet_v2_x2_0,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.196513287,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2002,resnext50_32x4d,resnext50_32x4d is used for image classification,resnext50_32x4d,1.0,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
2003,vgg16_bn,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.170873517,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
2004,regnet_x_400mf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.110482118,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
2005,regnet_x_8gf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.157958304,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
2006,regnet_x_8gf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.13824917,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
2007,convnext_small,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.039551214,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2008,regnet_y_800mf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.203446779,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
2009,convnext_base,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.231046125,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2010,segmentation.deeplabv3_resnet50,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.174291688,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2011,vgg16_bn,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.213751357,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
2012,detection.maskrcnn_resnet50_fpn,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
2013,vit_b_16,"By widening Residual Network (ResNet), the network can be shallower with the same accuracy or improved accuracy",wide_resnet50_2,0.135423132,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2014,quantization.mobilenet_v3_large,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.155824215,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
2015,shufflenet_v2_x1_5,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.142544456,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
2016,vit_b_16,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.057582295,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2017,vit_b_16,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.196719613,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2018,googlenet,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.077524999,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2019,resnet18,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.141586566,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
2020,quantization.resnet50,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.089427288,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
2021,segmentation.deeplabv3_resnet50,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.172121354,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2022,mobilenet_v3_small,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.173385099,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2023,resnet101,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.207084024,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2024,regnet_x_3_2gf,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.180927779,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2025,detection.keypointrcnn_resnet50_fpn,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.047960016,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2026,regnet_y_3_2gf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.174422753,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2027,vit_l_16,Constructs a RegNetX_1.6GF architecture,regnet_x_1_6gf,0.171409196,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2028,quantization.googlenet,googlenet is used for image classification,quantization.googlenet,1.0,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2029,mnasnet1_0,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.043287461,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
2030,mobilenet_v2,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.202881634,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
2031,efficientnet_b2,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.176350339,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
2032,vit_l_32,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.118023131,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2033,detection.keypointrcnn_resnet50_fpn,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.138160913,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2034,regnet_x_1_6gf,Constructs a RegNetX_1.6GF architecture,regnet_x_1_6gf,1.0,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
2035,convnext_tiny,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.213267435,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2036,regnet_x_3_2gf,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_3_2gf,1.0,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2037,convnext_large,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.070909917,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2038,detection.ssdlite320_mobilenet_v3_large,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet18,0.202940514,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
2039,segmentation.deeplabv3_resnet101,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.213457948,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2040,vit_b_16,The number of parameters is 3.4 million,mobilenet_v2,0.089027797,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2041,convnext_small,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.13522703,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2042,vit_b_32,Constructs a DeepLabV3 model with a MobileNetV3-Large backbone,segmentation.deeplabv3_mobilenet_v3_large,0.154389051,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2043,convnext_base,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.113178654,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2044,detection.maskrcnn_resnet50_fpn,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
2045,vit_l_32,"Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384",vit_l_32,1.0,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2046,quantization.shufflenet_v2_x1_0,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.150779666,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
2047,quantization.shufflenet_v2_x0_5,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.116395274,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
2048,efficientnet_b2,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.124132365,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
2049,regnet_y_8gf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.169641311,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2050,vgg16,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.168700253,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2051,regnet_y_32gf,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.161991694,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
2052,vgg13_bn,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.179348648,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
2053,vit_l_32,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.122675867,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2054,regnet_y_1_6gf,adopted from BERT,vit_b_16,0.014839992,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2055,efficientnet_b6,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.211121254,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
2056,quantization.resnet50,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.155508061,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
2057,vgg11,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.135916172,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
2058,segmentation.lraspp_mobilenet_v3_large,"Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded",vit_b_16,0.211082432,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2059,vgg11_bn,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.081174432,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
2060,efficientnet_b1,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.168722324,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
2061,regnet_x_400mf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.134114133,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
2062,resnet34,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.127591922,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
2063,regnet_y_32gf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.187879594,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
2064,wide_resnet101_2,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.217890354,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2065,segmentation.fcn_resnet101,vit_b_32 is used for image classification,vit_b_32,0.128746344,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
2066,quantization.shufflenet_v2_x2_0,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.178763963,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2067,vgg16,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.145756723,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2068,resnet101,They stack residual blocks on top of each other to form network: for example  a ResNet-101 has 101 layers using these blocks,resnet101,1.0,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2069,wide_resnet101_2,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.137055712,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2070,googlenet,"It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block",googlenet,1.0,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2071,mobilenet_v3_large,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.141772158,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2072,shufflenet_v2_x1_0,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.164591335,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2073,mnasnet0_5,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.203591642,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
2074,convnext_base,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.045948707,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2075,convnext_tiny,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.12521597,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2076,quantization.inception_v3,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.160695671,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2077,efficientnet_b3,ReLU non-linearity as activation functions,vgg11,0.159188741,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
2078,resnet18,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.202043975,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
2079,detection.fasterrcnn_resnet50_fpn,VGG19 has 19.6 billion FLOPs,vgg19,0.147797093,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
2080,quantization.resnet18,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.093558587,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2081,shufflenet_v2_x1_0,The number of parameters is 3.4 million,mobilenet_v2,0.093274738,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2082,mnasnet0_75,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.177228163,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2083,quantization.shufflenet_v2_x1_5,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.190046748,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2084,resnet101,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.105456962,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2085,segmentation.fcn_resnet101,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.009973932,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
2086,detection.maskrcnn_resnet50_fpn,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.118653267,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
2087,regnet_x_3_2gf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.109757853,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2088,resnet152,They stack residual blocks ontop of each other to form network: for example  a ResNet-152 has 152 layers using these blocks,resnet152,1.0,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2089,densenet161,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.174368301,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
2090,vit_b_32,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,0.144913071,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2091,regnet_x_800mf,"The RegNet is composed of a stem, body and head",regnet_x_800mf,1.0,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2092,resnet152,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.135545579,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2093,vit_l_32,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.131967671,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2094,detection.keypointrcnn_resnet50_fpn,It is the simplest of all other vgg versions,vgg11,0.104588828,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2095,mobilenet_v3_large,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.121094037,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2096,quantization.inception_v3,The key building block is an Inception Module,quantization.inception_v3,1.0,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2097,detection.retinanet_resnet50_fpn,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.228412742,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
2098,quantization.shufflenet_v2_x1_5,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.211588021,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2099,shufflenet_v2_x2_0,"It builds upon ShuffleNet v1, which utilised pointwise group convolutions, bottleneck-like structures, and a channel shuffle operation",shufflenet_v2_x2_0,1.0,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2100,regnet_x_3_2gf,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.168370641,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2101,vgg19_bn,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
2102,regnet_y_800mf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.171701019,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
2103,segmentation.deeplabv3_mobilenet_v3_large,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.144346356,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2104,mnasnet0_75,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.157222008,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2105,efficientnet_b5,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.247756794,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2106,convnext_base,"Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet)",squeezenet1_0,0.119137129,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2107,shufflenet_v2_x1_5,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.126806425,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
2108,regnet_x_8gf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.145706226,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
2109,regnet_y_400mf,"The RegNet is composed of a stem, body and head",regnet_y_400mf,1.0,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
2110,densenet169,densenet169 is More acurate than DenseNet-121,densenet169,1.0,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
2111,regnet_y_1_6gf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.179388185,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2112,resnet34,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.214806972,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
2113,densenet201,The model output for densenet-201 is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database,densenet201,1.0,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
2114,detection.retinanet_resnet50_fpn,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.106844206,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
2115,resnext50_32x4d,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.036196049,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
2116,convnext_tiny,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.118723841,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2117,quantization.inception_v3,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.119050611,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2118,mobilenet_v3_small,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.192665723,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2119,vit_b_16,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.083547318,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2120,vgg11_bn,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.066143338,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
2121,regnet_y_3_2gf,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.154342247,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2122,mnasnet0_75,The key building block is an Inception Module,inception_v3,0.178709244,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2123,segmentation.fcn_resnet101,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.03036876,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
2124,efficientnet_b7,ReLU non-linearity as activation functions,vgg11,0.19478866,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
2125,wide_resnet50_2,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.148656562,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
2126,efficientnet_b7,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.208422353,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
2127,regnet_y_32gf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.099976304,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
2128,efficientnet_b6,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.142139988,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
2129,resnet18,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.194651319,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
2130,resnet34,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.051174291,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
2131,quantization.resnet18,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.08931433,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2132,detection.fasterrcnn_mobilenet_v3_large_fpn,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.18313022,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2133,regnet_y_16gf,Replacing ReLU with GeLU,convnext_tiny,0.146869949,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
2134,resnet101,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.071834187,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2135,detection.keypointrcnn_resnet50_fpn,The required minimum input size of the model is 32x32,vgg11,0.180690286,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2136,regnet_x_3_2gf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.148298873,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2137,vgg11_bn,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.165219384,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
2138,regnet_x_400mf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.160842575,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
2139,quantization.inception_v3,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.161677662,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2140,inception_v3,Constructs a RegNetX_800MF architecture,regnet_x_800mf,0.217391176,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2141,convnext_small,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.161797554,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2142,squeezenet1_1,The squeezing (bottleneck layer) and expansion behavior is common in neural architectures,squeezenet1_1,1.0,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2143,vit_b_16,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.159764173,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2144,video.r3d_18,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.115401444,1x32x32,video classification,Null,TV
2145,resnet18,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.084964531,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
2146,resnet152,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.18167199,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2147,mobilenet_v3_small,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.106577595,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2148,detection.fasterrcnn_mobilenet_v3_large_fpn,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.158304027,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2149,quantization.shufflenet_v2_x2_0,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.154268496,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2150,resnet18,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.037560412,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
2151,shufflenet_v2_x1_0,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.179445277,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2152,shufflenet_v2_x2_0,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.126777768,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2153,convnext_base,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.127546407,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2154,detection.fasterrcnn_mobilenet_v3_large_fpn,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.111694319,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2155,quantization.shufflenet_v2_x1_5,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.189551625,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2156,quantization.shufflenet_v2_x1_5,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.155281885,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2157,efficientnet_b0,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.061494115,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
2158,vgg19,The required minimum input size of the model is 32x32,vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
2159,video.mc3_18,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.07006982,1x32x32,video classification,Null,TV
2160,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.099354504,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
2161,mnasnet0_5,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.04211481,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
2162,regnet_y_8gf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.164885321,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2163,detection.keypointrcnn_resnet50_fpn,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.11707393,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2164,inception_v3,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.09781176,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2165,vgg11_bn,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.146524292,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
2166,mobilenet_v3_large,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.198966673,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2167,quantization.shufflenet_v2_x1_0,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.038659676,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
2168,segmentation.lraspp_mobilenet_v3_large,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.175800788,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2169,vit_l_16,densenet169 is More acurate than DenseNet-121,densenet169,0.103932126,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2170,efficientnet_b7,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.196760658,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
2171,convnext_tiny,EfficientNet-B7 only has 66 million parameters,efficientnet_b7,0.200151559,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2172,regnet_y_3_2gf,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.158354931,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2173,regnet_y_400mf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.16665774,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
2174,video.mc3_18,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.239161259,1x32x32,video classification,Null,TV
2175,video.r2plus1d_18,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,1.0,1x32x32,video classification,Null,TV
2176,mnasnet1_3,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.095986813,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2177,regnet_x_800mf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_x_800mf,1.0,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2178,efficientnet_b3,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.232221612,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
2179,segmentation.fcn_resnet50,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.043783801,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2180,mnasnet1_3,The squeezing (bottleneck layer) and expansion behavior is common in neural architectures,squeezenet1_1,0.239050016,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2181,shufflenet_v2_x2_0,The number of parameters is 3.4 million,mobilenet_v2,0.09326451,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2182,regnet_x_3_2gf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.16546309,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2183,wide_resnet101_2,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.155581599,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2184,quantization.resnet50,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.064212322,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
2185,regnet_y_3_2gf,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.14020735,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2186,shufflenet_v2_x2_0,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.123455652,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2187,mobilenet_v3_large,There are 4 convolutional layers in each module (excluding the 1x1 convolutional layer),resnet18,0.150329517,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2188,segmentation.deeplabv3_mobilenet_v3_large,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_mobilenet_v3_large,1.0,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2189,regnet_y_3_2gf,"ssd300_vgg16 is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.ssd300_vgg16,0.07862337,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2190,detection.fasterrcnn_mobilenet_v3_large_fpn,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.132464812,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2191,quantization.resnext101_32x8d,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.204292727,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
2192,regnet_y_8gf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.183170771,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2193,convnext_tiny,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet18,0.180817621,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2194,video.r3d_18,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.114546139,1x32x32,video classification,Null,TV
2195,segmentation.fcn_resnet101,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.188004332,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
2196,detection.ssdlite320_mobilenet_v3_large,"Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384",vit_l_32,0.185335669,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
2197,inception_v3,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.110180929,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2198,quantization.mobilenet_v2,"ViT-B-32 is slightly faster than ResNet50 and it performs much worse on the 9M subset, but better on 90M+ subsets",vit_b_32,0.205824422,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
2199,detection.fasterrcnn_mobilenet_v3_large_fpn,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.162206558,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2200,convnext_base,"Compared with ResNet and PolyNet, ResNeXt has 3.03% Top-5 error rate, which is a large relative improvement of about 15%",resnext50_32x4d,0.240540425,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2201,resnext101_32x8d,The required minimum input size of the model is 29x29,densenet121,0.212448761,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
2202,detection.fasterrcnn_resnet50_fpn,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.061953443,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
2203,vit_b_16,"1x1 Convolution, Batch Normalization, Convolution, Depth wise Separable Convolution, Dropout, Global Average Pooling, Inverted Residual Block, Residual Connection, ReLU, Max Pooling, Softmax, Squeeze-and-Excitation Block",mnasnet1_0,0.148625806,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2204,efficientnet_b4,The required minimum input size of the model is 32x32,vgg11,0.207063279,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
2205,vgg16,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.142089645,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2206,quantization.mobilenet_v3_large,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.181905385,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
2207,video.r2plus1d_18,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.196911024,1x32x32,video classification,Null,TV
2208,segmentation.deeplabv3_resnet50,The required minimum input size of the model is 17x17,squeezenet1_1,0.202762777,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2209,shufflenet_v2_x1_0,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.169359718,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2210,detection.maskrcnn_resnet50_fpn,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
2211,regnet_y_3_2gf,regnet_y_3_2gf is used for image classification,regnet_y_3_2gf,1.0,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2212,squeezenet1_0,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.07569062,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
2213,regnet_y_8gf,2D max pooling in between the weight layers,vgg11,0.16625973,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2214,segmentation.deeplabv3_resnet101,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.113286152,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2215,quantization.shufflenet_v2_x0_5,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.132130196,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
2216,mobilenet_v3_small,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.230922167,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2217,quantization.resnext101_32x8d,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.138420978,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
2218,detection.keypointrcnn_resnet50_fpn,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.059904945,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2219,googlenet,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.142400472,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2220,convnext_large,They stack residual blocks on top of each other to form network: for example  a ResNet-18 has 18 layers using these blocks,resnet18,0.191582145,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2221,vit_b_32,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.075556342,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2222,quantization.shufflenet_v2_x1_5,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.113405918,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2223,googlenet,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.112230745,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2224,vgg13,Constructs a RegNetX_1.6GF architecture,regnet_x_1_6gf,0.239603054,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
2225,convnext_small,densenet169 is More acurate than DenseNet-121,densenet169,0.189226529,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2226,squeezenet1_1,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.182723641,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2227,shufflenet_v2_x2_0,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.172290569,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2228,densenet121,It is the simplest of all other vgg versions,vgg11,0.214743598,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
2229,segmentation.deeplabv3_mobilenet_v3_large,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.164160217,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2230,densenet201,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.203734768,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
2231,resnet101,The number of parameters is 3.4 million,mobilenet_v2,0.069218268,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2232,vgg16,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.189047484,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2233,regnet_y_3_2gf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.166603158,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2234,efficientnet_b6,efficientnet_b6 is used for image classification,efficientnet_b6,1.0,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
2235,vgg16_bn,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.183559875,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
2236,resnext50_32x4d,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.253358699,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
2237,mnasnet1_3,Replacing ReLU with GeLU,convnext_tiny,0.085688734,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2238,densenet161,The required minimum input size of the model is 29x29,densenet161,1.0,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
2239,mobilenet_v3_small,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.138972381,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2240,vgg16,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.18279373,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2241,quantization.inception_v3,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.173608178,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2242,quantization.resnet18,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.066026839,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2243,convnext_small,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.22520504,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2244,vit_b_32,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.071397856,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2245,densenet161,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.119021354,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
2246,resnext101_32x8d,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.140438238,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
2247,vit_l_32,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.072227003,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2248,densenet121,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.219187632,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
2249,quantization.shufflenet_v2_x2_0,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.169262625,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2250,mnasnet1_0,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.196253667,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
2251,resnet101,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.110576277,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2252,quantization.shufflenet_v2_x1_5,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.209395631,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2253,inception_v3,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.19173861,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2254,squeezenet1_1,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.152370267,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2255,segmentation.deeplabv3_resnet50,vit_b_32 is used for image classification,vit_b_32,0.136221969,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2256,regnet_x_1_6gf,"The RegNet is composed of a stem, body and head",regnet_x_1_6gf,1.0,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
2257,video.r2plus1d_18,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.16689638,1x32x32,video classification,Null,TV
2258,vgg16,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,1.0,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2259,convnext_large,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.066346463,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2260,regnet_y_400mf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.176756489,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
2261,convnext_small,The number of parameters is 3.4 million,mobilenet_v2,0.066182375,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2262,regnet_x_3_2gf,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.198117278,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2263,shufflenet_v2_x1_5,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.169098814,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
2264,segmentation.deeplabv3_mobilenet_v3_large,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.113338888,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2265,convnext_base,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.152062651,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2266,regnet_y_32gf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.116826442,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
2267,regnet_y_400mf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.200139749,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
2268,efficientnet_b6,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.174488126,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
2269,regnet_y_1_6gf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_1_6gf,1.0,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2270,mobilenet_v3_large,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.104089302,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2271,quantization.inception_v3,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.119243243,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2272,inception_v3,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.129686785,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2273,quantization.mobilenet_v2,MobileNetV2 is a convolutional neural network architecture that seeks to perform well on mobile devices,quantization.mobilenet_v2,1.0,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
2274,efficientnet_b3,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b3,1.0,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
2275,quantization.mobilenet_v3_large,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.193314667,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
2276,convnext_tiny,adopted from BERT,vit_b_16,0.026412734,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2277,regnet_y_8gf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_8gf,1.0,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2278,vgg19,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
2279,video.r2plus1d_18,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.163254037,1x32x32,video classification,Null,TV
2280,convnext_tiny,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.067803086,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2281,quantization.shufflenet_v2_x2_0,It is the simplest of all other vgg versions,vgg11,0.179001045,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2282,vit_l_32,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.090104096,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2283,efficientnet_b0,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.116497991,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
2284,regnet_y_32gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.187327524,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
2285,resnet101,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.071365893,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2286,detection.fasterrcnn_mobilenet_v3_large_fpn,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.047966386,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2287,vgg11_bn,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.181330348,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
2288,regnet_y_8gf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.190616919,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2289,resnet101,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.106410198,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2290,detection.ssd300_vgg16,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.078003303,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
2291,vit_b_16,"ssdlite320_mobilenet_v3_large is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.ssdlite320_mobilenet_v3_large,0.194700978,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2292,vit_b_32,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2293,shufflenet_v2_x0_5,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.175366506,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2294,quantization.googlenet,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.177206776,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2295,detection.fasterrcnn_mobilenet_v3_large_320_fpn,Replacing ReLU with GeLU,convnext_tiny,0.132445589,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
2296,mnasnet1_3,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.223210772,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2297,detection.keypointrcnn_resnet50_fpn,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.05779917,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2298,mobilenet_v3_large,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.2008591,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2299,detection.ssdlite320_mobilenet_v3_large,ResNet-18 is a convolutional neural network that is 18 layers deep,resnet18,0.217723385,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
2300,vit_b_16,Constructs a RegNetY_3.2GF architecture,regnet_y_3_2gf,0.173118901,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2301,segmentation.lraspp_mobilenet_v3_large,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,1.0,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2302,regnet_y_8gf,The required minimum input size of the model is 21x21,squeezenet1_0,0.216244429,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2303,wide_resnet101_2,It is the simplest of all other vgg versions,vgg11,0.151923562,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2304,regnet_x_16gf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.153547411,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
2305,shufflenet_v2_x2_0,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x2_0,1.0,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2306,segmentation.fcn_resnet50,The required minimum input size of the model is 29x29,densenet121,0.188512344,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2307,shufflenet_v2_x1_0,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.147979722,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2308,detection.maskrcnn_resnet50_fpn,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.071414682,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
2309,mobilenet_v3_large,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.044242816,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2310,regnet_x_3_2gf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.163614933,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2311,vit_b_32,This model can be used for image search (users search through a large collection of images) and for multi-lingual zero-shot image classification (image labels are defined as text),vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2312,video.r3d_18,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.159339957,1x32x32,video classification,Null,TV
2313,segmentation.deeplabv3_resnet101,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.124357385,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2314,wide_resnet50_2,The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block,wide_resnet50_2,1.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
2315,detection.retinanet_resnet50_fpn,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.208467277,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
2316,densenet161,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.197817254,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
2317,regnet_x_32gf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_x_32gf,1.0,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
2318,quantization.mobilenet_v2,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.072245815,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
2319,convnext_base,2D max pooling in between the weight layers,vgg11,0.091227549,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2320,efficientnet_b1,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.062079391,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
2321,convnext_base,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.149182541,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2322,vit_l_32,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.115364537,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2323,resnet50,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.195141538,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
2324,vgg13_bn,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.095166932,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
2325,video.r3d_18,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.165917284,1x32x32,video classification,Null,TV
2326,shufflenet_v2_x1_5,"It builds upon ShuffleNet v1, which utilised pointwise group convolutions, bottleneck-like structures, and a channel shuffle operation",shufflenet_v2_x1_5,1.0,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
2327,efficientnet_b0,adopted from BERT,vit_b_16,0.0,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
2328,vit_b_16,Constructs a Fully-Convolutional Network model with a ResNet-50 backbone,segmentation.fcn_resnet50,0.10128904,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2329,vgg16,This network is a pretty large network and it has about 138 million parameters,vgg16,1.0,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2330,squeezenet1_1,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,0.165492562,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2331,vgg16_bn,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.183952533,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
2332,regnet_y_3_2gf,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.159539419,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2333,segmentation.deeplabv3_resnet50,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.181373386,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2334,detection.keypointrcnn_resnet50_fpn,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.095902614,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2335,segmentation.deeplabv3_resnet101,The required minimum input size of the model is 21x21,squeezenet1_0,0.169749694,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2336,shufflenet_v2_x0_5,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.165797015,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2337,quantization.resnet18,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.094402625,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2338,detection.fasterrcnn_resnet50_fpn,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.168016538,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
2339,convnext_small,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.17587002,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2340,googlenet,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.111852512,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2341,densenet169,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.059433531,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
2342,mnasnet0_5,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.204711229,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
2343,convnext_large,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.175222677,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2344,wide_resnet101_2,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.242100619,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2345,regnet_y_1_6gf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.167155519,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2346,regnet_x_800mf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.167874293,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2347,convnext_tiny,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",vgg19,0.22872274,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2348,resnext101_32x8d,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.186208658,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
2349,detection.ssdlite320_mobilenet_v3_large,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.122924756,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
2350,mnasnet1_3,Note that this model does not include any fine-tuned heads,vit_b_16,0.14989012,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2351,mnasnet0_5,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.152895865,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
2352,video.r3d_18,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.149768018,1x32x32,video classification,Null,TV
2353,shufflenet_v2_x1_0,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.121331449,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2354,densenet121,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.041119983,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
2355,detection.retinanet_resnet50_fpn,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.140897896,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
2356,vit_l_32,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.126381107,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2357,mnasnet0_75,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.175680476,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2358,vgg13,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.146799544,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
2359,segmentation.fcn_resnet50,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.073813275,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2360,detection.keypointrcnn_resnet50_fpn,It follows this arrangement of convolution and max pool layers consistently throughout the whole architecture,vgg16,0.185009596,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2361,quantization.mobilenet_v3_large,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.142642618,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
2362,convnext_large,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.111128877,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2363,squeezenet1_0,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.122303578,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
2364,quantization.shufflenet_v2_x0_5,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.128271173,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
2365,mnasnet0_75,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.071661594,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2366,convnext_small,Replacing ReLU with GeLU,convnext_small,1.0,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2367,quantization.shufflenet_v2_x1_0,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.154366516,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
2368,densenet169,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.218128937,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
2369,regnet_x_1_6gf,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.167302782,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
2370,resnet34,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.214740794,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
2371,regnet_x_800mf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.106777481,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2372,regnet_x_400mf,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.125728773,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
2373,mnasnet0_5,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.241532758,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
2374,quantization.resnet18,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.18133198,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2375,regnet_y_8gf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.082126258,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2376,regnet_x_32gf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.158973561,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
2377,googlenet,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.128593507,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2378,resnext50_32x4d,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.25479535,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
2379,resnet50,"Each 2-layer block is replaced in the 34-layer net with this 3-layer bottleneck block, resulting in a 50-layer ResNet",resnet50,1.0,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
2380,efficientnet_b3,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.208086072,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
2381,regnet_y_32gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.18160029,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
2382,quantization.shufflenet_v2_x0_5,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",quantization.shufflenet_v2_x0_5,1.0,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
2383,mnasnet1_3,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.102367195,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2384,regnet_y_800mf,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.169667555,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
2385,mnasnet1_0,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.234584513,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
2386,mnasnet0_75,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.106255497,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2387,vgg11_bn,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
2388,convnext_tiny,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.107647552,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2389,quantization.shufflenet_v2_x2_0,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.111628948,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2390,video.r3d_18,Note that this model does not include any fine-tuned heads,vit_b_16,0.200228084,1x32x32,video classification,Null,TV
2391,inception_v3,Wide ResNet-50-2 Wide Residual Networks,wide_resnet50_2,0.146688207,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2392,vit_b_32,They use option 2 for increasing dimensions,resnet50,0.155025849,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2393,resnet101,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.17960033,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2394,regnet_x_400mf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.164596974,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
2395,vgg16,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,1.0,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2396,regnet_y_400mf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.146355421,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
2397,segmentation.deeplabv3_resnet101,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet101,1.0,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2398,inception_v3,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.078747847,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2399,segmentation.lraspp_mobilenet_v3_large,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.190325454,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2400,detection.ssdlite320_mobilenet_v3_large,"It has 11 weight layers in total, so the name VGG11",vgg11,0.130940652,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
2401,segmentation.deeplabv3_resnet101,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.079630481,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2402,regnet_y_1_6gf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.171933327,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2403,convnext_large,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.157809152,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2404,convnext_base,The densenet-169 is larger at just about 55MB in size vs the densenet-121 model's roughly 31MB size,densenet169,0.138413144,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2405,shufflenet_v2_x1_0,The required minimum input size of the model is 21x21,squeezenet1_0,0.181960125,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2406,detection.fasterrcnn_mobilenet_v3_large_fpn,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.166497015,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2407,alexnet,The number of parameters is 3.4 million,mobilenet_v2,0.128320973,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
2408,vgg11,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.139804343,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
2409,vit_b_16,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2410,detection.fasterrcnn_resnet50_fpn,It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position,detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
2411,video.mc3_18,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.12096376,1x32x32,video classification,Null,TV
2412,mobilenet_v3_small,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.181498013,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2413,shufflenet_v2_x0_5,shufflenet_v2_x0_5 is used for image classification,shufflenet_v2_x0_5,1.0,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2414,mnasnet1_0,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.14242231,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
2415,segmentation.fcn_resnet50,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384",vit_l_32,0.2112226,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2416,regnet_y_8gf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.162536108,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2417,vgg16_bn,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,1.0,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
2418,densenet161,It is the simplest of all other vgg versions,vgg11,0.189378707,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
2419,vgg13_bn,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.154223874,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
2420,detection.keypointrcnn_resnet50_fpn,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.148643236,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2421,convnext_base,convnext_base is used for image classification,convnext_base,1.0,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2422,vit_b_32,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.177759414,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2423,efficientnet_b3,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.179310691,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
2424,efficientnet_b1,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.189700063,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
2425,regnet_x_3_2gf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.110356936,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2426,regnet_y_1_6gf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.104321668,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2427,regnet_y_1_6gf,regnet_y_1_6gf is used for image classification,regnet_y_1_6gf,1.0,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2428,inception_v3,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,1.0,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2429,segmentation.deeplabv3_resnet50,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.184054971,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2430,densenet161,The key building block is an Inception Module,inception_v3,0.175913545,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
2431,convnext_tiny,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.078011344,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2432,vit_l_32,RPN and Fast R-CNN are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look,detection.fasterrcnn_resnet50_fpn,0.145971402,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2433,mnasnet1_0,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.120257738,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
2434,quantization.mobilenet_v2,The number of parameters is 3.4 million,quantization.mobilenet_v2,1.0,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
2435,resnet152,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.177480501,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2436,mobilenet_v3_small,mobilenet_v3_small is used for image classification,mobilenet_v3_small,1.0,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2437,vgg19,"A fixed size of (224 * 224) RGB image was given as input to this network which means that the matrix was of shape (224,224,3)",vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
2438,convnext_tiny,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.216447219,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2439,resnext50_32x4d,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.173651695,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
2440,convnext_tiny,densenet169 is More acurate than DenseNet-121,densenet169,0.183718371,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2441,shufflenet_v2_x0_5,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.092386144,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2442,efficientnet_b3,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.18189385,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
2443,googlenet,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.120510649,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2444,quantization.resnext101_32x8d,resnext101_32x8d is used for image classification,quantization.resnext101_32x8d,1.0,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
2445,vgg13_bn,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.173469737,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
2446,shufflenet_v2_x2_0,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.154266244,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2447,vit_b_32,Constructs a RegNetY_3.2GF architecture,regnet_y_3_2gf,0.160627224,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2448,detection.fasterrcnn_mobilenet_v3_large_fpn,Proposing region proposal network (RPN) which is a fully convolutional network that generates proposals with various scales and aspect ratios,detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2449,convnext_tiny,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.086036037,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2450,shufflenet_v2_x0_5,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.207882762,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2451,resnext50_32x4d,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.107163859,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
2452,mnasnet0_75,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.228803466,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2453,squeezenet1_1,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.079777791,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2454,segmentation.deeplabv3_resnet50,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,1.0,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2455,efficientnet_b4,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.204016377,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
2456,segmentation.fcn_resnet50,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.12983661,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2457,quantization.googlenet,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.149154988,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2458,quantization.resnet18,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.10491586,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2459,segmentation.fcn_resnet50,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.150344188,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2460,vit_b_32,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2461,shufflenet_v2_x0_5,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,1.0,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2462,resnext101_32x8d,The required minimum input size of the model is 32x32,vgg11,0.169616554,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
2463,regnet_x_3_2gf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.062609088,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2464,detection.fasterrcnn_resnet50_fpn,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.20860668,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
2465,segmentation.deeplabv3_mobilenet_v3_large,ResNet-152 can obtain 4.49% error rate,resnet152,0.208842576,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2466,vit_l_16,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.148768336,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2467,quantization.googlenet,"Googlenet has 9 Inception modules, 22 layers deep (21 convolutional layers and one fully connected layer) when counting only layers with parameters (or 27 layers if we also count pooling) and 12 times fewer parameters than AlexNet",quantization.googlenet,1.0,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2468,vit_l_32,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,1.0,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2469,video.r2plus1d_18,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.047952212,1x32x32,video classification,Null,TV
2470,resnet152,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.155681188,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2471,efficientnet_b7,Note that this model does not include any fine-tuned heads,vit_b_16,0.205302812,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
2472,vit_b_16,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.109021658,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2473,detection.maskrcnn_resnet50_fpn,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.186289453,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
2474,segmentation.deeplabv3_mobilenet_v3_large,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.127860542,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2475,mnasnet0_75,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.133238887,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2476,quantization.resnext101_32x8d,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.209072216,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
2477,quantization.shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,1.0,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
2478,mnasnet0_5,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.242484525,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
2479,mnasnet0_75,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.207642327,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2480,quantization.resnet18,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.158534462,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2481,efficientnet_b3,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,1.0,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
2482,detection.fasterrcnn_resnet50_fpn,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.127229638,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
2483,video.mc3_18,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.037757927,1x32x32,video classification,Null,TV
2484,quantization.googlenet,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.195768188,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2485,convnext_small,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.07343209,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2486,detection.keypointrcnn_resnet50_fpn,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.116903549,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2487,convnext_large,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.087690151,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2488,shufflenet_v2_x2_0,"It has 11 weight layers in total, so the name VGG11",vgg11,0.178557102,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2489,segmentation.fcn_resnet101,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.035703661,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
2490,squeezenet1_1,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.153099205,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2491,segmentation.fcn_resnet50,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.093908397,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2492,vgg13,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.164972808,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
2493,segmentation.deeplabv3_resnet101,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.161374016,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2494,resnet50,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.149153618,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
2495,segmentation.deeplabv3_resnet101,vit_l_16 is used for image classification,vit_l_16,0.147877714,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2496,regnet_x_800mf,The required minimum input size of the model is 29x29,densenet121,0.214064206,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2497,wide_resnet101_2,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.140836287,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2498,squeezenet1_0,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.165285331,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
2499,regnet_x_16gf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_x_16gf,1.0,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
2500,quantization.shufflenet_v2_x1_5,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.133126002,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2501,regnet_x_1_6gf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.145670261,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
2502,shufflenet_v2_x1_5,It is the simplest of all other vgg versions,vgg11,0.171008937,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
2503,segmentation.fcn_resnet101,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.12216003,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
2504,mnasnet1_3,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.209266665,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2505,regnet_x_800mf,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.217417179,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2506,resnext101_32x8d,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.221455108,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
2507,detection.maskrcnn_resnet50_fpn,Mask R-CNN extends on the Faster R-CNN by adding another branch in parallel for pixel-level object instance segmentation,detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
2508,inception_v3,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.106310039,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2509,mnasnet0_75,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.229370837,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2510,quantization.shufflenet_v2_x1_5,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.127015126,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2511,vgg16,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.097057608,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2512,resnet34,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.109498891,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
2513,segmentation.lraspp_mobilenet_v3_large,"Dense blocks, Batch Normalization(BN)",densenet121,0.168410036,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2514,quantization.shufflenet_v2_x1_5,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.223419949,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2515,quantization.shufflenet_v2_x1_0,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.shufflenet_v2_x1_0,1.0,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
2516,convnext_large,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.218553545,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2517,regnet_x_16gf,ReLU non-linearity as activation functions,vgg11,0.157871308,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
2518,inception_v3,quantization.resnet50 is the quantized model of resnet50,quantization.resnet50,0.178939496,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2519,vgg16,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.186909568,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2520,shufflenet_v2_x2_0,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.121541846,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2521,video.r2plus1d_18,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.171390875,1x32x32,video classification,Null,TV
2522,regnet_x_1_6gf,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.177563275,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
2523,vgg11,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.169450684,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
2524,densenet161,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,0.25805864,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
2525,detection.ssdlite320_mobilenet_v3_large,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.166812766,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
2526,quantization.resnet18,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.086324798,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2527,quantization.resnet50,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.149284185,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
2528,resnet34,VGG19 has 19.6 billion FLOPs,vgg19,0.115952049,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
2529,shufflenet_v2_x2_0,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.155231399,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2530,quantization.shufflenet_v2_x2_0,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.113436022,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2531,efficientnet_b3,efficientnet_b3 is used for image classification,efficientnet_b3,1.0,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
2532,efficientnet_b6,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.220331203,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
2533,quantization.googlenet,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.080672977,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2534,segmentation.lraspp_mobilenet_v3_large,The key building block is an Inception Module,inception_v3,0.185664397,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2535,mnasnet0_75,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.214337125,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2536,quantization.resnet50,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.178571925,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
2537,vit_l_16,Constructs a RegNetX_800MF architecture,regnet_x_800mf,0.144267207,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2538,quantization.inception_v3,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.109492767,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2539,segmentation.deeplabv3_resnet50,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.215255453,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2540,convnext_small,They stack residual blocks on top of each other to form network: for example  a ResNet-18 has 18 layers using these blocks,resnet18,0.188106605,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2541,regnet_y_400mf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.217597499,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
2542,inception_v3,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.206569731,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2543,quantization.resnet50,This model has 3.8 billion FLOPs,quantization.resnet50,1.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
2544,regnet_y_800mf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.133916811,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
2545,quantization.resnext101_32x8d,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.205789678,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
2546,vit_b_16,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.109021658,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2547,regnet_x_400mf,The required minimum input size of the model is 29x29,densenet121,0.212279702,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
2548,wide_resnet101_2,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.166675612,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2549,regnet_y_3_2gf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.172434375,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2550,shufflenet_v2_x0_5,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.157598824,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2551,segmentation.fcn_resnet50,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.134167396,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2552,regnet_y_400mf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.11815552,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
2553,vgg11,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.102271733,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
2554,mnasnet0_75,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.130585966,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2555,resnet101,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.07249336,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2556,convnext_large,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.127646469,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2557,efficientnet_b7,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.180281592,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
2558,efficientnet_b4,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.10739857,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
2559,vit_b_32,Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone,segmentation.lraspp_mobilenet_v3_large,0.172526872,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2560,vit_l_16,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2561,video.r3d_18,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.185429811,1x32x32,video classification,Null,TV
2562,video.mc3_18,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.232752306,1x32x32,video classification,Null,TV
2563,segmentation.deeplabv3_mobilenet_v3_large,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.131190874,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2564,regnet_x_16gf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.065234055,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
2565,convnext_large,"Inception network of googlenet stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid",googlenet,0.172077093,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2566,vgg19_bn,VGG19 has 19.6 billion FLOPs,vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
2567,detection.fasterrcnn_mobilenet_v3_large_fpn,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.158023798,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2568,squeezenet1_1,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.14903264,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2569,vgg16,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.160635661,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2570,googlenet,"Inception network of googlenet stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid",googlenet,1.0,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2571,mnasnet0_75,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.165390496,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2572,detection.ssd300_vgg16,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.133890876,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
2573,vit_l_16,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.171833107,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2574,resnet152,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.186309558,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2575,vit_b_16,RPN and Fast R-CNN are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look,detection.fasterrcnn_resnet50_fpn,0.143938461,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2576,regnet_y_16gf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.181503906,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
2577,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.071102046,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
2578,quantization.shufflenet_v2_x0_5,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.163113371,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
2579,video.mc3_18,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.142951678,1x32x32,video classification,Null,TV
2580,regnet_y_3_2gf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.056615184,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2581,convnext_tiny,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.162563991,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2582,vgg13_bn,adopted from BERT,vit_b_16,0.007189608,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
2583,quantization.mobilenet_v2,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.171117509,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
2584,quantization.inception_v3,Shallower network means: Number of layers can be reduced and Training time can be shorter as well,wide_resnet50_2,0.177095305,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2585,resnet101,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet101,1.0,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2586,vit_b_32,MobileNetV2 is a convolutional neural network architecture that seeks to perform well on mobile devices,mobilenet_v2,0.11105136,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2587,segmentation.fcn_resnet50,The required minimum input size of the model is 21x21,squeezenet1_0,0.173339614,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2588,regnet_x_3_2gf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.164019756,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2589,detection.maskrcnn_resnet50_fpn,The original dataset contained images of size 512x512x1,resnet101,0.166078583,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
2590,convnext_base,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.202256634,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2591,regnet_x_3_2gf,The required minimum input size of the model is 29x29,densenet121,0.179121198,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2592,segmentation.deeplabv3_mobilenet_v3_large,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.089752029,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2593,convnext_tiny,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.123122497,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2594,wide_resnet101_2,Replacing ReLU with GeLU,convnext_tiny,0.156879038,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2595,segmentation.deeplabv3_mobilenet_v3_large,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.116768714,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2596,detection.fasterrcnn_mobilenet_v3_large_fpn,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.095066949,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2597,mobilenet_v2,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,1.0,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
2598,efficientnet_b5,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.176225301,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2599,resnet152,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.190877936,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2600,convnext_tiny,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.035158448,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2601,quantization.resnet18,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",quantization.resnet18,1.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2602,detection.fasterrcnn_mobilenet_v3_large_fpn,It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position,detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2603,shufflenet_v2_x0_5,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.183332325,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2604,googlenet,VGG19 has 19.6 billion FLOPs,vgg19,0.060293787,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2605,regnet_y_32gf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.156719225,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
2606,quantization.resnet50,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.166628172,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
2607,regnet_x_8gf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.177934364,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
2608,densenet169,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.180541424,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
2609,mnasnet1_3,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.174079557,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2610,segmentation.deeplabv3_resnet101,Constructs a DeepLabV3 model with a ResNet-101 backbone,segmentation.deeplabv3_resnet101,1.0,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2611,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.065170692,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
2612,convnext_small,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.149407461,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2613,quantization.shufflenet_v2_x1_0,The number of parameters is 3.4 million,mobilenet_v2,0.110954457,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
2614,regnet_y_16gf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_16gf,1.0,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
2615,vgg16_bn,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.175116012,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
2616,resnet18,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.18345776,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
2617,regnet_x_8gf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_x_8gf,1.0,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
2618,shufflenet_v2_x2_0,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.088516652,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2619,quantization.shufflenet_v2_x1_5,ReLU non-linearity as activation functions,vgg11,0.168462973,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2620,regnet_y_3_2gf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.079921973,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2621,vgg16_bn,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.178335529,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
2622,quantization.inception_v3,"ViT-B-32 is slightly faster than ResNet50 and it performs much worse on the 9M subset, but better on 90M+ subsets",vit_b_32,0.185694537,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2623,vit_l_16,Constructs a RegNetY_400MF architecture,regnet_y_400mf,0.15969878,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2624,regnet_x_3_2gf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.156722562,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2625,regnet_y_1_6gf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.119541076,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2626,convnext_large,quantization.resnet18 is the quantized model of resnet18,quantization.resnet18,0.232975151,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2627,vgg19,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.154977598,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
2628,resnet50,resnet50 is used for image classification,resnet50,1.0,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
2629,convnext_base,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.0988426,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2630,video.mc3_18,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.221700087,1x32x32,video classification,Null,TV
2631,quantization.mobilenet_v3_large,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.098191351,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
2632,mnasnet0_75,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.100780281,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2633,detection.ssd300_vgg16,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
2634,segmentation.deeplabv3_resnet101,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.186169601,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2635,densenet121,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.207402818,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
2636,detection.retinanet_resnet50_fpn,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.174657364,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
2637,vit_b_16,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.185965,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2638,quantization.inception_v3,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.10408533,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2639,vit_b_16,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.16762677,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2640,mobilenet_v3_large,Note that this model does not include any fine-tuned heads,vit_b_16,0.184496656,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2641,detection.fasterrcnn_mobilenet_v3_large_320_fpn,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.093933129,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
2642,convnext_tiny,11 weight layers (convolutional + fully connected),vgg11,0.186383734,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2643,efficientnet_b5,adopted from BERT,vit_b_16,0.0,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2644,densenet121,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.118995141,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
2645,resnext101_32x8d,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.139848696,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
2646,squeezenet1_0,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.125021104,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
2647,quantization.shufflenet_v2_x2_0,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.212115772,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2648,regnet_y_1_6gf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.084200086,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2649,wide_resnet101_2,The number of parameters is 3.4 million,mobilenet_v2,0.07017098,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2650,resnext50_32x4d,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.242890816,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
2651,regnet_y_8gf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.177402544,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2652,vit_l_32,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.105807311,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2653,inception_v3,Constructs a RegNetY_3.2GF architecture,regnet_y_3_2gf,0.234896118,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2654,vgg13,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg13,1.0,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
2655,resnet18,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.201429967,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
2656,detection.ssd300_vgg16,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.095813246,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
2657,detection.keypointrcnn_resnet50_fpn,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.209510522,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2658,inception_v3,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,1.0,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2659,vgg13,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.111117125,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
2660,detection.maskrcnn_resnet50_fpn,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.175641089,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
2661,mobilenet_v3_large,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.150492147,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2662,regnet_y_3_2gf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.093676337,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2663,mnasnet0_5,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.218368262,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
2664,quantization.shufflenet_v2_x2_0,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.203540438,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2665,resnet152,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet152,1.0,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2666,squeezenet1_0,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.144282627,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
2667,regnet_y_3_2gf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.181789082,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2668,quantization.resnext101_32x8d,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.23679743,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
2669,vit_l_16,The key building block is an Inception Module,inception_v3,0.152023146,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2670,quantization.resnet18,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.resnet18,1.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2671,regnet_x_800mf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.125433231,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2672,regnet_y_3_2gf,The required minimum input size of the model is 32x32,vgg11,0.157562599,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2673,shufflenet_v2_x1_5,adopted from BERT,vit_b_16,0.003157191,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
2674,googlenet,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.157333682,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2675,resnet50,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",resnet50,1.0,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
2676,regnet_y_3_2gf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.124443718,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
2677,video.mc3_18,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.224302158,1x32x32,video classification,Null,TV
2678,segmentation.deeplabv3_resnet50,The required minimum input size of the model is 29x29,densenet121,0.213142537,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2679,regnet_x_32gf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.156590213,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
2680,resnet34,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.057540226,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
2681,efficientnet_b2,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.13963778,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
2682,vit_b_32,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.110571293,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2683,segmentation.lraspp_mobilenet_v3_large,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.180668046,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2684,regnet_y_800mf,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,1.0,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
2685,quantization.shufflenet_v2_x0_5,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.152018685,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
2686,detection.fasterrcnn_resnet50_fpn,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.16705699,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
2687,resnet50,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.219814537,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
2688,vit_b_16,"Each layer from the FPN is passed to the subnets, enabling it to detect objects as various scales",detection.retinanet_resnet50_fpn,0.199177703,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2689,regnet_x_32gf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.169004473,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
2690,quantization.googlenet,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.101017237,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2691,densenet201,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.208370301,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
2692,wide_resnet101_2,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.155885811,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2693,convnext_small,VGG19 has 19.6 billion FLOPs,vgg19,0.137714639,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2694,efficientnet_b3,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.15890936,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
2695,mobilenet_v3_large,2D max pooling in between the weight layers,vgg11,0.221756276,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2696,inception_v3,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.137655579,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2697,detection.fasterrcnn_mobilenet_v3_large_fpn,The required minimum input size of the model is 32x32,vgg11,0.149301713,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2698,regnet_x_32gf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.198764495,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
2699,efficientnet_b0,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.101219344,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
2700,efficientnet_b7,efficientnet_b7 is used for image classification,efficientnet_b7,1.0,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
2701,segmentation.deeplabv3_resnet101,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.172324542,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2702,shufflenet_v2_x0_5,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.126028273,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2703,efficientnet_b6,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.245173782,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
2704,vgg16,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.168066151,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2705,vgg16,The required minimum input size of the model is 32x32,vgg16,1.0,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2706,mobilenet_v3_small,11 weight layers (convolutional + fully connected),vgg11,0.215366177,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2707,regnet_y_800mf,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.166743925,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
2708,quantization.googlenet,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.237831755,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2709,segmentation.deeplabv3_resnet50,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.113965623,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2710,vgg11_bn,vgg11 is used for image classification,vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
2711,shufflenet_v2_x0_5,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.175900498,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2712,resnet18,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.187333861,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
2713,convnext_base,adopted from BERT,vit_b_16,0.020593953,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2714,regnet_y_8gf,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.13749326,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2715,video.r2plus1d_18,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.121588571,1x32x32,video classification,Null,TV
2716,regnet_x_800mf,It is the simplest of all other vgg versions,vgg11,0.176166695,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2717,detection.retinanet_resnet50_fpn,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.154069064,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
2718,efficientnet_b1,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.234802538,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
2719,efficientnet_b6,obtained by scaling up the baseline network,efficientnet_b6,1.0,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
2720,detection.ssdlite320_mobilenet_v3_large,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.131341025,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
2721,regnet_x_800mf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.082241475,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2722,inception_v3,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.144227569,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2723,mobilenet_v3_large,adopted from BERT,vit_b_16,0.021600848,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2724,quantization.inception_v3,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.150045668,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2725,densenet161,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.168657555,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
2726,vit_l_32,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.130142201,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2727,mobilenet_v3_small,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.155629898,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2728,regnet_y_16gf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.164329686,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
2729,convnext_base,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.225479834,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2730,vit_b_16,Note that this model does not include any fine-tuned heads,vit_b_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2731,segmentation.deeplabv3_resnet50,vit_l_32 is used for image classification,vit_l_32,0.141195616,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2732,detection.ssd300_vgg16,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.132222839,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
2733,video.r3d_18,adopted from BERT,vit_b_16,0.0,1x32x32,video classification,Null,TV
2734,regnet_y_1_6gf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_1_6gf,1.0,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2735,googlenet,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.065208385,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2736,efficientnet_b5,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.214312255,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2737,regnet_x_800mf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.152176126,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2738,detection.fasterrcnn_resnet50_fpn,The number of parameters is 3.4 million,mobilenet_v2,0.105224837,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
2739,regnet_y_32gf,regnet_y_32gf is used for image classification,regnet_y_32gf,1.0,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
2740,densenet121,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.160772187,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
2741,inception_v3,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.156217679,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2742,regnet_y_400mf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.113069114,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
2743,quantization.shufflenet_v2_x2_0,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.130518273,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2744,mnasnet0_5,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.045697706,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
2745,mnasnet0_75,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.162340763,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2746,quantization.inception_v3,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.160025883,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2747,efficientnet_b5,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.24622491,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2748,vgg13_bn,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.196243187,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
2749,convnext_small,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.110253172,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2750,efficientnet_b5,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.229761068,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2751,regnet_x_16gf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_x_16gf,1.0,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
2752,squeezenet1_1,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.175676289,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2753,quantization.resnet50,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.143511906,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
2754,segmentation.deeplabv3_resnet50,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.094434072,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2755,googlenet,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.052136798,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2756,resnext50_32x4d,Replacing ReLU with GeLU,convnext_tiny,0.223722905,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
2757,regnet_y_32gf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.155912323,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
2758,vit_b_32,"It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block",googlenet,0.125269324,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2759,regnet_y_1_6gf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.137749206,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2760,regnet_x_8gf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.159598293,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
2761,detection.retinanet_resnet50_fpn,"The two subnetworks feature a simple design that the authors propose specifically for one-stage, dense detection",detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
2762,inception_v3,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.118983109,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2763,quantization.shufflenet_v2_x1_5,It is the simplest of all other vgg versions,vgg11,0.180287518,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2764,wide_resnet101_2,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.192214598,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2765,segmentation.lraspp_mobilenet_v3_large,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.172107044,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2766,vgg19,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.16965036,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
2767,vit_l_32,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.080628404,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2768,shufflenet_v2_x1_0,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.165983666,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2769,segmentation.lraspp_mobilenet_v3_large,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.119561255,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2770,regnet_x_400mf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.060678366,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
2771,inception_v3,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.160370282,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2772,efficientnet_b4,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.175099823,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
2773,vgg13,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.18412029,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
2774,convnext_tiny,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.05175971,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2775,quantization.inception_v3,"By widening Residual Network (ResNet), the network can be shallower with the same accuracy or improved accuracy",wide_resnet50_2,0.160028154,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2776,video.r3d_18,"For object detection, the authors used MobileNetv2 as the feature extractor of a computationally efficient variant of the SSD",detection.ssdlite320_mobilenet_v3_large,0.207200853,1x32x32,video classification,Null,TV
2777,mnasnet1_0,"MnasNet is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency",mnasnet1_0,1.0,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
2778,vit_b_32,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.089499569,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2779,convnext_large,convnext_large is used for image classification,convnext_large,1.0,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2780,vit_b_32,obtained by scaling up the baseline network,efficientnet_b1,0.138911419,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2781,convnext_large,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.131853384,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2782,video.r3d_18,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.101800832,1x32x32,video classification,Null,TV
2783,regnet_x_400mf,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.183538229,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
2784,mnasnet1_0,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.118458363,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
2785,segmentation.deeplabv3_resnet101,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.176256662,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2786,quantization.resnext101_32x8d,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.105925381,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
2787,shufflenet_v2_x2_0,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.11918282,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2788,inception_v3,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.083322093,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2789,vit_b_16,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.068330836,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2790,efficientnet_b5,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.172864616,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2791,segmentation.lraspp_mobilenet_v3_large,adopted from BERT,vit_b_16,0.0,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2792,regnet_y_1_6gf,ReLU non-linearity as activation functions,vgg11,0.17638822,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
2793,video.mc3_18,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.209722724,1x32x32,video classification,Null,TV
2794,vit_l_32,"It is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",resnext101_32x8d,0.129179727,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2795,regnet_x_400mf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.145238686,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
2796,vgg13_bn,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.218768326,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
2797,vit_b_32,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.105100015,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2798,vgg16,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.143474724,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2799,squeezenet1_1,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.122296214,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2800,vit_l_16,"EfficientNet-B7 achieves new state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy, while being 8.4x smaller than the best existing CNN",efficientnet_b7,0.155780353,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2801,efficientnet_b6,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.128690528,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
2802,inception_v3,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.163809213,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2803,efficientnet_b0,ReLU non-linearity as activation functions,vgg11,0.153486219,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
2804,mnasnet0_75,Proposed a new layer called ROI Pooling that extracts equal-length feature vectors from all proposals (  ROIs) in the same image,detection.fasterrcnn_resnet50_fpn,0.227150917,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2805,segmentation.fcn_resnet50,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.146430868,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2806,densenet169,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.188767265,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
2807,detection.retinanet_resnet50_fpn,The backbone is responsible for computing a convolutional feature map over an entire input image and is an off-the-self convolutional network,detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
2808,regnet_x_16gf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.102448297,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
2809,regnet_x_3_2gf,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.154949755,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2810,mobilenet_v3_large,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.089801566,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2811,regnet_x_16gf,regnet_x_16gf is used for image classification,regnet_x_16gf,1.0,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
2812,convnext_small,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.122380186,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2813,efficientnet_b2,ReLU non-linearity as activation functions,vgg11,0.159449893,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
2814,quantization.shufflenet_v2_x2_0,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.171335859,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2815,convnext_tiny,DenseNet-121 has 120 Convolutions and 4 AvgPool,densenet121,0.212181506,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2816,vit_l_16,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.064060167,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2817,squeezenet1_0,Proposing region proposal network (RPN) which is a fully convolutional network that generates proposals with various scales and aspect ratios,detection.fasterrcnn_resnet50_fpn,0.207045153,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
2818,mnasnet0_75,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.23083665,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2819,googlenet,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.217795305,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2820,mobilenet_v3_small,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.047229249,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2821,vit_l_16,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.103803391,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2822,regnet_x_16gf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.143629097,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
2823,mobilenet_v3_small,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.205570742,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2824,convnext_base,"A ConvNet which has the design simplicity of Convolutional Neural Networks while having better accuracy, performance and scalability than Vision Transformers.",convnext_base,1.0,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2825,regnet_x_3_2gf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.108664472,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2826,googlenet,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.184047232,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
2827,densenet201,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.209144202,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
2828,convnext_small,The required minimum input size of the model is 17x17,squeezenet1_1,0.171862919,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2829,vgg13,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.181667339,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
2830,detection.fasterrcnn_mobilenet_v3_large_fpn,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.203585182,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2831,regnet_x_16gf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.117242452,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
2832,vit_l_32,vit_l_32 is used for image classification,vit_l_32,1.0,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2833,segmentation.fcn_resnet101,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.150478035,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
2834,detection.keypointrcnn_resnet50_fpn,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.091039608,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2835,vgg16,Constructs a RegNetX_8GF architecture,regnet_x_8gf,0.238666216,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
2836,efficientnet_b5,The required minimum input size of the model is 32x32,vgg11,0.218864724,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2837,regnet_x_400mf,adopted from BERT,vit_b_16,0.008749456,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
2838,regnet_x_3_2gf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_x_3_2gf,1.0,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
2839,efficientnet_b4,The number of parameters is 3.4 million,mobilenet_v2,0.156245106,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
2840,efficientnet_b0,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.167258007,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
2841,resnext50_32x4d,"It has 11 weight layers in total, so the name VGG11",vgg11,0.206802599,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
2842,resnet152,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.118576998,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2843,quantization.shufflenet_v2_x1_0,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.153267286,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
2844,densenet201,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.18112366,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
2845,densenet201,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.177969784,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
2846,inception_v3,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.143603838,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2847,vit_l_32,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.118320949,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2848,segmentation.deeplabv3_resnet101,adopted from BERT,vit_b_16,0.002418852,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2849,mobilenet_v3_large,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.142755454,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
2850,detection.keypointrcnn_resnet50_fpn,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.205401563,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2851,mnasnet1_3,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.046167087,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2852,quantization.inception_v3,Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks,wide_resnet50_2,0.147021284,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2853,shufflenet_v2_x0_5,Replacing ReLU with GeLU,convnext_tiny,0.097043316,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2854,quantization.googlenet,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.112964158,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2855,convnext_base,The key building block is an Inception Module,inception_v3,0.119030925,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2856,segmentation.lraspp_mobilenet_v3_large,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.214736891,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2857,segmentation.deeplabv3_mobilenet_v3_large,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.156108107,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2858,regnet_x_8gf,"The RegNet is composed of a stem, body and head",regnet_x_8gf,1.0,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
2859,wide_resnet101_2,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.171177069,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2860,convnext_small,"Inception network of googlenet stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid",googlenet,0.152835511,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2861,vgg13,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.169743745,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
2862,segmentation.deeplabv3_mobilenet_v3_large,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.136838179,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2863,alexnet,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.153822164,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
2864,regnet_x_800mf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.176027664,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2865,quantization.googlenet,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.103587898,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2866,convnext_small,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.053834939,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2867,vit_l_16,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.138431486,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2868,convnext_base,The original dataset contained images of size 512x512x1,resnet101,0.076207631,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2869,mnasnet1_3,"Dense blocks, Batch Normalization(BN)",densenet121,0.096266755,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2870,convnext_small,"In particular, in this section we use the 400 million flops (400MF) regime and train each sampled model for 10 epochs on the ImageNet dataset",regnet_y_400mf,0.144219067,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2871,vit_l_32,Constructs a DeepLabV3 model with a ResNet-101 backbone,segmentation.deeplabv3_resnet101,0.154540179,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2872,shufflenet_v2_x2_0,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.208474277,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
2873,quantization.googlenet,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.154060826,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
2874,shufflenet_v2_x1_0,VGG19 has 19.6 billion FLOPs,vgg19,0.116175948,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2875,densenet121,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.154571916,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
2876,convnext_small,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x0_5,0.189443482,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2877,densenet169,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.176083128,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
2878,resnet18,They stack residual blocks on top of each other to form network: for example  a ResNet-18 has 18 layers using these blocks,resnet18,1.0,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
2879,efficientnet_b2,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.147388177,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
2880,efficientnet_b1,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.179007539,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
2881,mnasnet0_5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.221551418,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
2882,quantization.inception_v3,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.142783752,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2883,resnext50_32x4d,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.114409946,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
2884,convnext_small,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.146062309,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2885,regnet_x_32gf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.050458552,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
2886,inception_v3,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.211343594,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2887,resnet152,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.153040963,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2888,efficientnet_b5,"It has 11 weight layers in total, so the name VGG11",vgg11,0.228592601,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2889,quantization.shufflenet_v2_x1_5,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.198623151,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
2890,resnext101_32x8d,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.200469144,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
2891,segmentation.deeplabv3_resnet101,vit_b_16 is used for image classification,vit_b_16,0.143265839,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
2892,regnet_y_16gf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.133088538,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
2893,vit_l_32,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.166687638,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2894,convnext_small,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.198646318,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2895,shufflenet_v2_x1_5,VGG19 has 19.6 billion FLOPs,vgg19,0.116542573,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
2896,detection.fasterrcnn_mobilenet_v3_large_320_fpn,The number of parameters is 3.4 million,mobilenet_v2,0.099304775,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
2897,vgg11,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.161718348,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
2898,segmentation.fcn_resnet101,Relative fast due to no fully connected layers or a refinement block,segmentation.fcn_resnet101,1.0,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
2899,mnasnet1_3,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.193797727,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2900,shufflenet_v2_x1_0,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.16125155,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2901,vgg19_bn,"A fixed size of (224 * 224) RGB image was given as input to this network which means that the matrix was of shape (224,224,3)",vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
2902,quantization.shufflenet_v2_x2_0,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.181155676,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
2903,vit_l_32,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.119269306,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2904,vit_b_16,Wide ResNet-50-2 Wide Residual Networks,wide_resnet50_2,0.115423808,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2905,quantization.shufflenet_v2_x1_0,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.183262054,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
2906,vit_b_32,Constructs a RegNetY_16GF architecture,regnet_y_16gf,0.18289479,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2907,detection.keypointrcnn_resnet50_fpn,"Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded",vit_b_16,0.219812481,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2908,detection.keypointrcnn_resnet50_fpn,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.151608527,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2909,convnext_small,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.114776561,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2910,convnext_base,"Dense blocks, Batch Normalization(BN)",densenet121,0.11325774,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2911,vit_b_16,MNASNet with depth multiplier of 0.75,mnasnet0_75,0.18062696,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2912,detection.fasterrcnn_resnet50_fpn,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.163569704,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
2913,efficientnet_b5,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.157591056,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2914,mobilenet_v3_small,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.170744357,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
2915,wide_resnet101_2,is a convolutional neural network that is 101 layers deep,wide_resnet101_2,1.0,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
2916,resnext101_32x8d,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.15280224,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
2917,squeezenet1_1,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.173517935,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2918,regnet_x_1_6gf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.11072889,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
2919,quantization.resnet18,The number of parameters is 3.4 million,mobilenet_v2,0.10156332,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2920,quantization.mobilenet_v2,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.097006396,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
2921,squeezenet1_0,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.093089174,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
2922,efficientnet_b7,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.176433581,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
2923,shufflenet_v2_x0_5,The required minimum input size of the model is 21x21,squeezenet1_0,0.185172824,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2924,resnet152,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.199564159,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
2925,detection.ssdlite320_mobilenet_v3_large,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.127447462,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
2926,mnasnet0_75,The network design includes the use of a hard swish activation and squeeze-and-excitation modules in the MBConv blocks,mobilenet_v3_small,0.245443707,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2927,inception_v3,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.130719526,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2928,vit_l_16,EfficientNet-B0 is the baseline network developed by AutoML MNASEfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient,efficientnet_b0,0.18668359,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
2929,convnext_small,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.197122836,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2930,quantization.inception_v3,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.209178612,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2931,convnext_large,"A ConvNet which has the design simplicity of Convolutional Neural Networks while having better accuracy, performance and scalability than Vision Transformers.",convnext_large,1.0,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2932,detection.retinanet_resnet50_fpn,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.209162505,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
2933,squeezenet1_1,Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone,segmentation.lraspp_mobilenet_v3_large,0.205336697,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
2934,quantization.resnet18,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.066391476,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2935,resnext101_32x8d,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.118888315,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
2936,convnext_tiny,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.233280569,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2937,convnext_base,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.149252931,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2938,alexnet,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.156523301,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
2939,detection.fasterrcnn_resnet50_fpn,Proposing region proposal network (RPN) which is a fully convolutional network that generates proposals with various scales and aspect ratios,detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
2940,video.r2plus1d_18,Note that this model does not include any fine-tuned heads,vit_b_16,0.210020535,1x32x32,video classification,Null,TV
2941,convnext_tiny,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.167683619,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2942,efficientnet_b0,"The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks",efficientnet_b0,1.0,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
2943,efficientnet_b6,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.17787328,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
2944,wide_resnet50_2,This is achieved through the use of wide residual blocks,wide_resnet50_2,1.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
2945,regnet_y_800mf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.073364856,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
2946,mnasnet1_3,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.166773051,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
2947,efficientnet_b0,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.089677427,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
2948,inception_v3,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.169370865,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2949,wide_resnet50_2,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.138353097,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
2950,vgg11_bn,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.147101265,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
2951,vit_b_16,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.101360794,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
2952,vgg19,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
2953,convnext_tiny,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.132688754,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
2954,shufflenet_v2_x0_5,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.152500857,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
2955,densenet169,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.189253689,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
2956,regnet_x_800mf,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.178951347,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2957,efficientnet_b5,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.193825386,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
2958,vit_b_32,RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training,detection.retinanet_resnet50_fpn,0.16647414,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2959,regnet_x_1_6gf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.073889072,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
2960,mnasnet0_75,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.180649127,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
2961,regnet_x_16gf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.078117028,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
2962,video.r2plus1d_18,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.113830834,1x32x32,video classification,Null,TV
2963,quantization.resnet18,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.110331129,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
2964,inception_v3,Constructs a DeepLabV3 model with a ResNet-50 backbone,segmentation.deeplabv3_resnet50,0.1930025,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
2965,convnext_small,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.069871231,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
2966,segmentation.deeplabv3_mobilenet_v3_large,Constructs a DeepLabV3 model with a MobileNetV3-Large backbone,segmentation.deeplabv3_mobilenet_v3_large,1.0,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2967,vgg16_bn,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.192420216,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
2968,regnet_y_400mf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.177234697,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
2969,convnext_base,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.164710471,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2970,detection.keypointrcnn_resnet50_fpn,The required minimum input size of the model is 21x21,squeezenet1_0,0.180692781,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2971,densenet161,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.190611057,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
2972,vit_b_32,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.124095089,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
2973,regnet_y_8gf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.10388319,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
2974,resnet101,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.107854548,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
2975,video.r3d_18,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.239447319,1x32x32,video classification,Null,TV
2976,segmentation.fcn_resnet50,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.01101987,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
2977,segmentation.fcn_resnet101,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.159285807,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
2978,segmentation.deeplabv3_mobilenet_v3_large,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.099996619,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
2979,shufflenet_v2_x1_0,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.148240288,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
2980,detection.fasterrcnn_mobilenet_v3_large_fpn,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.118660341,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2981,vit_l_32,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.121113768,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2982,video.r2plus1d_18,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.093932298,1x32x32,video classification,Null,TV
2983,detection.fasterrcnn_mobilenet_v3_large_fpn,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.119459716,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
2984,detection.retinanet_resnet50_fpn,It uses ResNet augmented by Feature Pyramid Network (FPN) as the backbone and two similar subnets - classification and bounding box regressor (regression),detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
2985,vit_l_32,Replacing ReLU with GeLU,convnext_tiny,0.118017813,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
2986,regnet_x_1_6gf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.166388311,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
2987,convnext_large,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.079492248,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
2988,shufflenet_v2_x1_5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.20851313,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
2989,video.mc3_18,It is the simplest of all other vgg versions,vgg11,0.147409608,1x32x32,video classification,Null,TV
2990,segmentation.deeplabv3_resnet50,The dense prediction is achieved by simply up-sampling the output of the last convolution layer and computing pixel-wise loss,segmentation.deeplabv3_resnet50,1.0,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
2991,convnext_base,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.162903375,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
2992,quantization.inception_v3,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",quantization.inception_v3,1.0,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
2993,quantization.shufflenet_v2_x1_0,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.185274716,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
2994,detection.keypointrcnn_resnet50_fpn,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,1.0,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
2995,efficientnet_b4,obtained by scaling up the baseline network,efficientnet_b4,1.0,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
2996,quantization.shufflenet_v2_x1_0,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.171166996,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
2997,segmentation.lraspp_mobilenet_v3_large,RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training,detection.retinanet_resnet50_fpn,0.195815128,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
2998,regnet_x_800mf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.168349296,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
2999,mnasnet1_3,Proposed a new layer called ROI Pooling that extracts equal-length feature vectors from all proposals (  ROIs) in the same image,detection.fasterrcnn_resnet50_fpn,0.223688006,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
3000,vit_l_32,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.19665303,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3001,mobilenet_v3_large,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.170006979,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3002,convnext_large,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.182705747,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3003,quantization.resnext101_32x8d,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.167714849,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
3004,efficientnet_b5,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.210879024,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
3005,mnasnet0_75,"Dense blocks, Batch Normalization(BN)",densenet121,0.089611598,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3006,detection.fasterrcnn_mobilenet_v3_large_320_fpn,The required minimum input size of the model is 32x32,vgg11,0.149263407,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3007,googlenet,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.203535051,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3008,mnasnet0_5,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.197546517,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3009,regnet_y_1_6gf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.1670597,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3010,convnext_tiny,"Each 2-layer block is replaced in the 34-layer net with this 3-layer bottleneck block, resulting in a 50-layer ResNet",resnet50,0.165430284,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3011,vit_b_16,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x0_5,0.123053369,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3012,detection.maskrcnn_resnet50_fpn,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.114259193,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
3013,mobilenet_v3_large,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.218060938,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3014,vit_l_16,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.131976605,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3015,efficientnet_b5,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.202604445,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
3016,vgg11,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.190470423,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
3017,shufflenet_v2_x2_0,adopted from BERT,vit_b_16,0.001045657,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3018,segmentation.deeplabv3_mobilenet_v3_large,The original dataset contained images of size 512x512x1,resnet101,0.12040156,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3019,mnasnet1_3,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.226640567,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
3020,densenet201,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.077106128,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
3021,vgg11_bn,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.115712759,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
3022,vgg13,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.150148529,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3023,quantization.shufflenet_v2_x0_5,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.154984752,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3024,densenet121,"A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other",densenet121,1.0,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
3025,detection.keypointrcnn_resnet50_fpn,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.191146642,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3026,segmentation.lraspp_mobilenet_v3_large,The number of parameters is 3.4 million,mobilenet_v2,0.154808348,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3027,googlenet,GoogLeNet is a type of convolutional neural network based on the Inception architecture,googlenet,1.0,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3028,segmentation.deeplabv3_resnet101,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.17691147,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3029,efficientnet_b5,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.146750912,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
3030,vgg16,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.197670007,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
3031,resnext101_32x8d,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.20462146,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3032,detection.keypointrcnn_resnet50_fpn,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.099293502,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3033,vit_b_32,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.171718861,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3034,mnasnet0_75,The required minimum input size of the model is 29x29,densenet121,0.179989129,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3035,detection.retinanet_resnet50_fpn,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.20354842,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
3036,convnext_base,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.043652722,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3037,convnext_small,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.102476662,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3038,vgg13,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.146238778,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3039,convnext_tiny,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.100863278,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3040,vit_b_32,"RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks",detection.retinanet_resnet50_fpn,0.100563723,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3041,convnext_tiny,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.123876408,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3042,quantization.shufflenet_v2_x1_5,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.132702357,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3043,shufflenet_v2_x2_0,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,shufflenet_v2_x2_0,1.0,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3044,vit_l_16,"Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded",vit_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3045,detection.ssdlite320_mobilenet_v3_large,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.142766495,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3046,mnasnet0_75,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.202670616,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3047,vit_l_32,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x0_5,0.117726355,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3048,vit_l_16,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.136883813,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3049,efficientnet_b5,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.18088839,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
3050,regnet_y_32gf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.184018444,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
3051,quantization.resnet18,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.21623387,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
3052,densenet121,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.165071153,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
3053,quantization.shufflenet_v2_x2_0,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.159828065,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
3054,mobilenet_v3_large,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.19038346,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3055,detection.keypointrcnn_resnet50_fpn,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.153549529,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3056,quantization.googlenet,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.199623128,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3057,densenet161,The densenet-161 is much larger at 100MB in size vs the densenet-121 model's roughly 31MB size,densenet161,1.0,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3058,efficientnet_b6,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.237980962,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3059,efficientnet_b0,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.210126382,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
3060,mnasnet1_3,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.112576215,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
3061,mnasnet1_3,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.045969071,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
3062,efficientnet_b7,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.210290541,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
3063,resnet50,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.138140077,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
3064,mobilenet_v3_small,MobileNetV3 is a convolutional neural network that is designed for mobile phone CPUs,mobilenet_v3_small,1.0,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
3065,googlenet,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.178524243,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3066,convnext_base,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.05566189,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3067,convnext_base,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.106317539,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3068,detection.fasterrcnn_mobilenet_v3_large_fpn,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.07174365,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
3069,efficientnet_b0,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.122564757,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
3070,squeezenet1_0,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.185950577,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
3071,mnasnet1_3,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.170260171,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
3072,mobilenet_v3_large,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.166635409,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3073,mnasnet0_75,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.20785818,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3074,mobilenet_v2,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.148109713,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3075,regnet_x_32gf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.086596186,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
3076,vit_b_16,ResNet-152 can obtain 4.49% error rate,resnet152,0.096691423,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3077,detection.keypointrcnn_resnet50_fpn,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.171356925,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3078,quantization.resnet50,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",quantization.resnet50,1.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
3079,detection.ssdlite320_mobilenet_v3_large,You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close,vit_b_32,0.154877934,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3080,vgg13,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.104271758,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3081,convnext_large,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.063336331,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3082,quantization.mobilenet_v3_large,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.175878678,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
3083,video.mc3_18,"mixed convolution (MC) and consists in employing 3D convolutions only in the early layers of the network, with 2D convolutions in the top layers",video.mc3_18,1.0,1x32x32,video classification,Null,TV
3084,mobilenet_v3_small,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.193038486,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
3085,resnext101_32x8d,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.230317004,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3086,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions",detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3087,video.mc3_18,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.20921891,1x32x32,video classification,Null,TV
3088,alexnet,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.055832483,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
3089,regnet_y_8gf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.187279741,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
3090,efficientnet_b0,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.183877494,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
3091,detection.fasterrcnn_resnet50_fpn,"Dense blocks, Batch Normalization(BN)",densenet121,0.142664262,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
3092,regnet_x_1_6gf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.161456689,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
3093,quantization.shufflenet_v2_x0_5,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.178750327,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3094,inception_v3,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet18,0.224479462,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3095,vgg13_bn,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.136444976,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3096,inception_v3,ReLU non-linearity as activation functions,vgg11,0.158142659,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3097,detection.fasterrcnn_resnet50_fpn,"fasterrcnn_resnet50_fpn is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
3098,quantization.shufflenet_v2_x1_0,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.213842976,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3099,inception_v3,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.121575747,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3100,segmentation.fcn_resnet101,The required minimum input size of the model is 32x32,vgg11,0.153874908,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
3101,convnext_tiny,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.164223008,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3102,mobilenet_v2,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.10371278,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3103,vit_l_32,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.110364218,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3104,quantization.shufflenet_v2_x1_5,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.193009689,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3105,segmentation.deeplabv3_resnet50,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.109250876,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
3106,video.r3d_18,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.130246992,1x32x32,video classification,Null,TV
3107,detection.fasterrcnn_mobilenet_v3_large_fpn,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.073479299,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
3108,vgg11_bn,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
3109,vgg19_bn,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.167161517,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
3110,regnet_x_400mf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.078541853,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3111,regnet_y_1_6gf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.107248731,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3112,efficientnet_b1,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.144291569,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
3113,resnext50_32x4d,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.099952588,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3114,detection.keypointrcnn_resnet50_fpn,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.129313211,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3115,detection.ssdlite320_mobilenet_v3_large,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.147591456,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3116,quantization.inception_v3,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.161384515,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
3117,efficientnet_b6,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.157720003,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3118,resnext50_32x4d,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.244497519,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3119,vgg11,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.074149038,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
3120,video.mc3_18,"A fixed size of (224 * 224) RGB image was given as input to this network which means that the matrix was of shape (224,224,3)",vgg19,0.199954012,1x32x32,video classification,Null,TV
3121,resnet18,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.117701882,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
3122,shufflenet_v2_x2_0,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.196747646,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3123,detection.fasterrcnn_resnet50_fpn,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.219831418,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
3124,regnet_x_3_2gf,The required minimum input size of the model is 32x32,vgg11,0.157170768,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
3125,quantization.resnet18,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.105861412,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
3126,segmentation.deeplabv3_resnet101,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.131765875,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3127,detection.fasterrcnn_mobilenet_v3_large_320_fpn,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.076186974,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3128,mnasnet0_75,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.217622404,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3129,inception_v3,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.157000002,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3130,quantization.shufflenet_v2_x0_5,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.184140592,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3131,quantization.googlenet,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.083042477,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3132,vit_b_32,"The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions",detection.fasterrcnn_resnet50_fpn,0.168377498,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3133,segmentation.fcn_resnet101,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.187784784,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
3134,vit_l_32,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.144833013,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3135,mobilenet_v2,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.209658506,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3136,regnet_y_1_6gf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_1_6gf,1.0,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3137,googlenet,The required minimum input size of the model is 17x17,squeezenet1_1,0.180460921,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3138,regnet_x_8gf,Constructs a RegNetX_8GF architecture,regnet_x_8gf,1.0,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
3139,vgg19,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.140468155,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
3140,convnext_base,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.119158607,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3141,regnet_x_800mf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.162364514,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3142,shufflenet_v2_x0_5,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.153146974,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
3143,detection.maskrcnn_resnet50_fpn,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.123403031,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
3144,googlenet,Replacing ReLU with GeLU,convnext_tiny,0.068285567,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3145,quantization.shufflenet_v2_x1_0,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.117287292,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3146,quantization.resnext101_32x8d,11 weight layers (convolutional + fully connected),vgg11,0.260008775,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
3147,quantization.mobilenet_v2,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.137235958,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
3148,quantization.googlenet,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.104506514,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3149,quantization.mobilenet_v2,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.162104459,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
3150,convnext_large,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.097187984,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3151,vgg11_bn,Not all the convolutional layers are followed by max-pooling layers,vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
3152,regnet_x_32gf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.150450483,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
3153,mobilenet_v3_large,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.193405589,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3154,vgg11_bn,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.161692031,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
3155,mobilenet_v3_large,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.175783744,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3156,regnet_x_800mf,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,1.0,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3157,vgg13,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_400mf,0.211768266,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3158,quantization.shufflenet_v2_x1_5,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.038659676,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3159,efficientnet_b5,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.235743884,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
3160,shufflenet_v2_x2_0,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.120341903,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3161,video.r2plus1d_18,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.168529773,1x32x32,video classification,Null,TV
3162,densenet121,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.205930511,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
3163,regnet_y_8gf,Replacing ReLU with GeLU,convnext_tiny,0.144601465,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
3164,regnet_x_32gf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_x_32gf,1.0,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
3165,mnasnet1_0,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.14135332,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
3166,squeezenet1_1,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.16065133,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
3167,resnext101_32x8d,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.133667219,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3168,resnet18,VGG19 has 19.6 billion FLOPs,vgg19,0.111240476,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
3169,segmentation.fcn_resnet50,r2plus1d_18 is used for video classification,video.r2plus1d_18,0.127685625,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3170,vgg13,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.133240595,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3171,convnext_small,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.144032682,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3172,video.mc3_18,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.229055192,1x32x32,video classification,Null,TV
3173,quantization.shufflenet_v2_x1_0,Replacing ReLU with GeLU,convnext_tiny,0.090420492,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3174,regnet_y_400mf,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,1.0,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
3175,quantization.shufflenet_v2_x1_5,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.171880233,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3176,densenet169,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.115561331,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
3177,video.mc3_18,ResNet Mixed Convolution is a variation of ResNet 3D is a type of model for video that employs 3D convolutions,video.mc3_18,1.0,1x32x32,video classification,Null,TV
3178,quantization.googlenet,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.096164997,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3179,detection.maskrcnn_resnet50_fpn,"maskrcnn_resnet50_fpn is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
3180,googlenet,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.217278413,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3181,vit_b_16,Constructs a DeepLabV3 model with a ResNet-101 backbone,segmentation.deeplabv3_resnet101,0.145027524,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3182,shufflenet_v2_x1_5,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.123163857,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3183,segmentation.deeplabv3_mobilenet_v3_large,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_mobilenet_v3_large,1.0,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3184,detection.fasterrcnn_resnet50_fpn,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
3185,vgg13,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.141184688,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3186,vit_l_16,They stack residual blocks ontop of each other to form network,vgg19_bn,0.108098892,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3187,efficientnet_b3,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.08837806,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
3188,vgg13_bn,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.183797194,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3189,quantization.resnext101_32x8d,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.172764284,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
3190,regnet_y_32gf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.177096075,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
3191,video.r3d_18,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.1682801,1x32x32,video classification,Null,TV
3192,efficientnet_b5,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b5,1.0,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
3193,segmentation.fcn_resnet50,"The authors demonstrate its efficacy with the help of a simple, single stage detector, called RetinaNet, which predicts objects by dense sampling of the input image in location, scale and aspect ratio",detection.retinanet_resnet50_fpn,0.213911352,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3194,vgg16,"MnasNet is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency",mnasnet0_5,0.184498616,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
3195,vgg13,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.124856559,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3196,quantization.shufflenet_v2_x0_5,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.186615127,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3197,detection.fasterrcnn_mobilenet_v3_large_320_fpn,Proposed a new layer called ROI Pooling that extracts equal-length feature vectors from all proposals (  ROIs) in the same image,detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3198,quantization.googlenet,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.132225648,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3199,resnet18,The number of parameters is 3.4 million,mobilenet_v2,0.071529323,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
3200,mnasnet0_5,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.026231414,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3201,regnet_y_1_6gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.176696798,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3202,vgg13_bn,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.089721266,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3203,densenet169,The model output for densenet-201 is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database,densenet169,1.0,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
3204,inception_v3,"RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks",detection.retinanet_resnet50_fpn,0.142951886,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3205,vit_l_32,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.106714177,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3206,efficientnet_b1,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.123936853,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
3207,regnet_x_400mf,Replacing ReLU with GeLU,convnext_tiny,0.137567318,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3208,vit_b_16,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg11,0.165542074,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3209,shufflenet_v2_x1_5,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x1_5,1.0,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3210,video.r2plus1d_18,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.172786458,1x32x32,video classification,Null,TV
3211,detection.retinanet_resnet50_fpn,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.157451497,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
3212,resnext101_32x8d,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.187481631,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3213,segmentation.fcn_resnet101,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.218352456,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
3214,vit_b_32,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.103975263,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3215,mnasnet0_75,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.114938753,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3216,vgg11,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.136511036,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
3217,shufflenet_v2_x1_0,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.176459895,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3218,inception_v3,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.086059055,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3219,vit_l_16,RPN and Fast R-CNN are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look,detection.fasterrcnn_resnet50_fpn,0.146278828,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3220,efficientnet_b3,Replacing ReLU with GeLU,convnext_tiny,0.076062492,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
3221,segmentation.deeplabv3_resnet50,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.114449369,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
3222,resnext101_32x8d,Mask R-CNN extends on the Faster R-CNN by adding another branch in parallel for pixel-level object instance segmentation,detection.maskrcnn_resnet50_fpn,0.236721929,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3223,quantization.mobilenet_v3_large,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.226712374,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
3224,convnext_base,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.212293143,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3225,vgg13,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.153503848,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3226,convnext_large,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.134700666,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3227,regnet_x_800mf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.146149823,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3228,quantization.shufflenet_v2_x1_0,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.162432766,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3229,video.r2plus1d_18,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.190541,1x32x32,video classification,Null,TV
3230,regnet_y_400mf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.207823087,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
3231,segmentation.deeplabv3_mobilenet_v3_large,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.131828216,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3232,mnasnet0_5,adopted from BERT,vit_b_16,0.0,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3233,mobilenet_v2,"ViT-B-32 is slightly faster than ResNet50 and it performs much worse on the 9M subset, but better on 90M+ subsets",vit_b_32,0.186250788,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3234,resnet101,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.164666189,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
3235,convnext_tiny,quantization.resnet18 is the quantized model of resnet18,quantization.resnet18,0.239870763,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3236,efficientnet_b1,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.198724803,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
3237,segmentation.fcn_resnet50,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.149224182,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3238,convnext_small,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.170996529,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3239,vit_b_32,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.061372603,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3240,inception_v3,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.129484322,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3241,quantization.resnext101_32x8d,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.140470337,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
3242,resnet34,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.147572447,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
3243,squeezenet1_0,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.152743808,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
3244,detection.fasterrcnn_resnet50_fpn,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.095304955,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
3245,vit_l_16,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.142686319,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3246,shufflenet_v2_x2_0,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.157920718,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3247,regnet_x_1_6gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.187896369,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
3248,efficientnet_b5,Replacing ReLU with GeLU,convnext_tiny,0.093520951,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
3249,quantization.googlenet,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.186700677,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3250,vit_b_16,They stack residual blocks on top of each other to form network: for example  a ResNet-18 has 18 layers using these blocks,resnet18,0.132360638,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3251,detection.fasterrcnn_mobilenet_v3_large_320_fpn,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.230473618,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3252,shufflenet_v2_x2_0,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.127385499,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3253,alexnet,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.169485414,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
3254,convnext_base,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.119001017,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3255,convnext_small,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.10966395,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3256,detection.fasterrcnn_mobilenet_v3_large_320_fpn,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.23138622,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3257,vit_l_16,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg11,0.171458384,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3258,regnet_y_8gf,The required minimum input size of the model is 32x32,vgg11,0.178028478,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
3259,efficientnet_b6,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.145894486,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3260,shufflenet_v2_x2_0,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.198891018,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3261,densenet161,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.230080321,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3262,video.r3d_18,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.136625847,1x32x32,video classification,Null,TV
3263,quantization.mobilenet_v3_large,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.160150932,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
3264,convnext_large,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.109023381,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3265,vgg19,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.124511023,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
3266,convnext_large,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.126137854,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3267,vgg11_bn,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.189739426,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
3268,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.137510947,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3269,convnext_large,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.10241515,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3270,resnext50_32x4d,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.213096153,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3271,googlenet,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.105286681,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3272,segmentation.fcn_resnet50,"In particular, in this section we use the 400 million flops (400MF) regime and train each sampled model for 10 epochs on the ImageNet dataset",regnet_y_400mf,0.171058185,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3273,regnet_y_1_6gf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.152258202,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3274,squeezenet1_0,"Inception v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using Label Smoothing, Factorized 7 x 7 convolutions, and the use of an auxiliary classifier to propagate label information lower down the network (along with the use of batch normalization for layers in the side head)",inception_v3,0.208198444,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
3275,resnet101,Note that this model does not include any fine-tuned heads,vit_b_16,0.193274851,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
3276,inception_v3,Shallower network means: Number of layers can be reduced and Training time can be shorter as well,wide_resnet50_2,0.164603651,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3277,detection.maskrcnn_resnet50_fpn,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.17223062,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
3278,convnext_small,The required minimum input size of the model is 21x21,squeezenet1_0,0.167589657,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3279,mobilenet_v3_large,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.167740208,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3280,video.r3d_18,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.160230036,1x32x32,video classification,Null,TV
3281,segmentation.deeplabv3_resnet50,The number of parameters is 3.4 million,mobilenet_v2,0.118181363,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
3282,vit_l_16,They stack residual blocks ontop of each other to form network: for example  a ResNet-34 has 34 layers using these blocks,resnet34,0.12068247,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3283,convnext_small,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet18,0.191379151,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3284,quantization.inception_v3,ReLU non-linearity as activation functions,vgg11,0.186474415,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
3285,efficientnet_b3,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.176562075,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
3286,convnext_small,DenseNet-121 has 120 Convolutions and 4 AvgPool,densenet121,0.213821611,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3287,resnet34,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet34,1.0,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
3288,vit_l_32,"We show that MC ResNets yield roughly a 3-4% gain in clip-level accuracy over 2D ResNets of comparable capacity and they match the performance of 3D ResNets, which have 3 times as many parameters",video.mc3_18,0.093348377,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3289,regnet_y_16gf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.123937284,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
3290,shufflenet_v2_x1_0,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.176200267,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3291,mnasnet1_3,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.230190764,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
3292,regnet_y_400mf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.131940757,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
3293,quantization.shufflenet_v2_x1_5,shufflenet_v2_x1_5 is used for image classification,quantization.shufflenet_v2_x1_5,1.0,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3294,regnet_y_32gf,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.155491991,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
3295,regnet_y_1_6gf,The required minimum input size of the model is 17x17,squeezenet1_1,0.195407912,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3296,vgg13,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.142205943,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3297,regnet_x_8gf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.168414237,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
3298,mnasnet0_5,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.147779984,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3299,inception_v3,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.123985518,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3300,vgg13_bn,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.18602457,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3301,segmentation.deeplabv3_mobilenet_v3_large,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_mobilenet_v3_large,1.0,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3302,video.r3d_18,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.19553474,1x32x32,video classification,Null,TV
3303,quantization.inception_v3,"RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks",detection.retinanet_resnet50_fpn,0.122550633,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
3304,detection.ssdlite320_mobilenet_v3_large,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.170667854,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3305,efficientnet_b2,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.173623946,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
3306,alexnet,This model has 3.8 billion FLOPs,resnet50,0.133917158,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
3307,segmentation.deeplabv3_mobilenet_v3_large,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.185986368,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3308,quantization.shufflenet_v2_x1_5,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,quantization.shufflenet_v2_x1_5,1.0,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3309,vgg13_bn,"VGG13 has the same architecture as VGG11, but with a different network size",vgg13_bn,1.0,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3310,vit_l_16,They stack residual blocks on top of each other to form network: for example  a ResNet-50 has fifty layers using these blocks,resnet50,0.107448738,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3311,regnet_y_16gf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.154398784,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
3312,convnext_large,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.119614847,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3313,regnet_x_16gf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.135580719,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
3314,shufflenet_v2_x1_0,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.122463793,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3315,efficientnet_b6,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.166165174,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3316,efficientnet_b4,ReLU non-linearity as activation functions,vgg11,0.183389172,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
3317,convnext_small,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.041178564,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3318,efficientnet_b4,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.203332195,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
3319,convnext_large,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.068604534,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3320,detection.keypointrcnn_resnet50_fpn,Relative fast due to no fully connected layers or a refinement block,segmentation.fcn_resnet50,0.163066922,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3321,mnasnet0_5,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.153464042,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3322,detection.ssdlite320_mobilenet_v3_large,"Dense blocks, Batch Normalization(BN)",densenet121,0.165775938,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3323,efficientnet_b6,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.126710924,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3324,detection.keypointrcnn_resnet50_fpn,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.134321182,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3325,vgg13_bn,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.13522017,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3326,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.061052281,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3327,quantization.shufflenet_v2_x1_0,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.188200124,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3328,quantization.shufflenet_v2_x1_5,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.162686946,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3329,quantization.shufflenet_v2_x1_0,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.177384622,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3330,mnasnet0_75,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.192072804,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3331,regnet_x_800mf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.090141361,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3332,resnet152,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.134361123,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
3333,inception_v3,Constructs a RegNetX_16GF architecture,regnet_x_16gf,0.215160798,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3334,video.r3d_18,This model has 3.8 billion FLOPs,resnet50,0.167410595,1x32x32,video classification,Null,TV
3335,segmentation.lraspp_mobilenet_v3_large,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,1.0,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3336,vgg11,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.220402248,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
3337,regnet_y_3_2gf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.150608996,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
3338,regnet_x_800mf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.183510087,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3339,regnet_y_8gf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.163405257,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
3340,quantization.googlenet,"It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block",quantization.googlenet,1.0,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3341,quantization.shufflenet_v2_x0_5,The number of parameters is 3.4 million,mobilenet_v2,0.117386167,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3342,resnext101_32x8d,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.135533083,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3343,detection.retinanet_resnet50_fpn,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.162295203,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
3344,squeezenet1_0,"As a whole, Faster R-CNN consists of two modules",detection.fasterrcnn_resnet50_fpn,0.238251548,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
3345,quantization.shufflenet_v2_x0_5,"Training Techniques are Weight Decay, SGD with Momentum",quantization.shufflenet_v2_x0_5,1.0,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3346,resnext50_32x4d,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.107600152,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3347,vgg11,SqueezeNet 1.0 achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters,squeezenet1_0,0.183451504,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
3348,detection.ssd300_vgg16,It is the simplest of all other vgg versions,vgg11,0.149593119,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
3349,convnext_small,Constructs an SSDlite model with input size 320x320 and a MobileNetV3 Large backbone,detection.ssdlite320_mobilenet_v3_large,0.101837485,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3350,video.r3d_18,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.191526028,1x32x32,video classification,Null,TV
3351,detection.ssdlite320_mobilenet_v3_large,is a convolutional neural network that is 152 layers deep,resnet152,0.16484437,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3352,inception_v3,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.118118833,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3353,resnext50_32x4d,"In particular, in this section we use the 400 million flops (400MF) regime and train each sampled model for 10 epochs on the ImageNet dataset",regnet_y_400mf,0.272129428,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3354,mnasnet0_75,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.192300846,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3355,efficientnet_b2,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,1.0,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
3356,squeezenet1_1,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.168986359,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
3357,resnet50,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.129888579,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
3358,segmentation.fcn_resnet101,fcn_resnet101 is used for semantic segmentation,segmentation.fcn_resnet101,1.0,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
3359,convnext_small,Note that this model does not include any fine-tuned heads,vit_b_16,0.234764199,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3360,vgg13,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.113543065,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3361,regnet_x_8gf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.10124309,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
3362,shufflenet_v2_x1_5,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.173196693,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3363,vit_b_16,Constructs a RegNetY_8GF architecture,regnet_y_8gf,0.19296924,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3364,quantization.shufflenet_v2_x0_5,shufflenet_v2_x0_5 is used for image classification,quantization.shufflenet_v2_x0_5,1.0,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3365,vit_l_16,Constructs a RegNetY_800MF architecture,regnet_y_800mf,0.149427154,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3366,segmentation.fcn_resnet50,vit_l_32 is used for image classification,vit_l_32,0.124935689,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3367,segmentation.deeplabv3_resnet50,adopted from BERT,vit_b_16,0.002418852,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
3368,inception_v3,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.157234023,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3369,vgg13_bn,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.107284229,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3370,shufflenet_v2_x2_0,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.206604988,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3371,googlenet,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.049456424,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3372,resnet18,The required minimum input size of the model is 29x29,densenet121,0.173348711,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
3373,vit_b_16,adopted from BERT,vit_b_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3374,shufflenet_v2_x0_5,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,1.0,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
3375,inception_v3,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.206357109,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3376,quantization.googlenet,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.147153866,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3377,mnasnet1_3,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.211166394,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
3378,detection.ssd300_vgg16,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.138818197,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
3379,efficientnet_b4,EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient,efficientnet_b4,1.0,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
3380,efficientnet_b6,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.210692186,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3381,vgg16,ReLU non-linearity as activation functions,vgg11,0.1545026,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
3382,mnasnet0_5,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.055344706,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3383,regnet_x_800mf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.154338655,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3384,vgg11_bn,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.147344123,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
3385,quantization.mobilenet_v2,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.144539706,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
3386,resnext50_32x4d,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.17577716,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3387,regnet_x_32gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.17207407,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
3388,detection.maskrcnn_resnet50_fpn,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.092592465,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
3389,convnext_base,deeplabv3_mobilenet_v3_large is used for semantic segmentation,segmentation.deeplabv3_mobilenet_v3_large,0.155333551,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3390,densenet169,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.180733719,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
3391,convnext_small,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.039554068,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3392,vit_l_32,"It consists of convolutions, max pooling and dense layers as the basic building blocks",alexnet,0.191868604,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3393,convnext_base,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_base,1.0,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3394,video.r2plus1d_18,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.178933582,1x32x32,video classification,Null,TV
3395,segmentation.deeplabv3_resnet101,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.176155229,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3396,efficientnet_b1,obtained by scaling up the baseline network,efficientnet_b1,1.0,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
3397,regnet_x_16gf,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.202739192,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
3398,regnet_x_3_2gf,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.147222899,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
3399,quantization.shufflenet_v2_x0_5,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.153342958,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3400,squeezenet1_1,"(2+1)D  convolutional block, which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution",video.r2plus1d_18,0.168201198,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
3401,segmentation.deeplabv3_resnet101,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.138042692,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3402,regnet_x_800mf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.192848115,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3403,efficientnet_b6,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b6,1.0,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3404,convnext_small,"Googlenet has 9 Inception modules, 22 layers deep (21 convolutional layers and one fully connected layer) when counting only layers with parameters (or 27 layers if we also count pooling) and 12 times fewer parameters than AlexNet",googlenet,0.166258101,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3405,detection.ssdlite320_mobilenet_v3_large,r3d_18 is used for video classification,video.r3d_18,0.191105433,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3406,resnet152,The required minimum input size of the model is 21x21,squeezenet1_0,0.181669823,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
3407,convnext_large,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.139547357,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3408,quantization.shufflenet_v2_x1_5,The number of parameters is 3.4 million,mobilenet_v2,0.119393046,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3409,detection.fasterrcnn_mobilenet_v3_large_fpn,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.050929127,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
3410,video.mc3_18,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.237253662,1x32x32,video classification,Null,TV
3411,detection.maskrcnn_resnet50_fpn,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.175641089,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
3412,convnext_large,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.084691895,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3413,vit_l_16,Constructs a DeepLabV3 model with a MobileNetV3-Large backbone,segmentation.deeplabv3_mobilenet_v3_large,0.137409703,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3414,vit_l_16,"By widening Residual Network (ResNet), the network can be shallower with the same accuracy or improved accuracy",wide_resnet50_2,0.140725142,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3415,efficientnet_b6,adopted from BERT,vit_b_16,0.0,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3416,resnet18,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.059882605,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
3417,segmentation.lraspp_mobilenet_v3_large,ReLU non-linearity as activation functions,vgg11,0.11492268,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3418,mobilenet_v2,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.057703629,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3419,resnext101_32x8d,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.104542545,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3420,convnext_base,The required minimum input size of the model is 29x29,densenet121,0.148753983,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3421,wide_resnet101_2,ReLU non-linearity as activation functions,vgg11,0.210446695,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
3422,detection.keypointrcnn_resnet50_fpn,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.073285683,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3423,detection.maskrcnn_resnet50_fpn,"Dense blocks, Batch Normalization(BN)",densenet121,0.154531444,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
3424,regnet_y_400mf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.190265155,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
3425,shufflenet_v2_x2_0,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.150386342,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3426,densenet161,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.170830208,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3427,resnext101_32x8d,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.168801676,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3428,quantization.resnet50,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.146321239,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
3429,convnext_small,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.135657993,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3430,regnet_y_1_6gf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.195234951,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3431,vit_b_32,This model has 3.8 billion FLOPs,resnet50,0.147364715,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3432,regnet_x_1_6gf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.155336667,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
3433,regnet_y_1_6gf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.101720342,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3434,mnasnet0_5,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.19427758,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3435,resnet101,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.136129749,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
3436,segmentation.lraspp_mobilenet_v3_large,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.158414112,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3437,quantization.shufflenet_v2_x0_5,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.113606231,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3438,vgg13,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.212070627,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3439,quantization.shufflenet_v2_x1_5,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.18973389,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3440,resnet50,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.164673775,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
3441,shufflenet_v2_x1_0,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.168929048,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3442,inception_v3,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.182243931,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3443,efficientnet_b2,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.195533548,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
3444,resnet101,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.155238573,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
3445,efficientnet_b7,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.169601207,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
3446,regnet_y_8gf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.141482148,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
3447,video.mc3_18,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.087501259,1x32x32,video classification,Null,TV
3448,detection.ssdlite320_mobilenet_v3_large,"Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded",vit_b_16,0.163686746,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3449,regnet_x_8gf,2D max pooling in between the weight layers,vgg11,0.158894898,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
3450,mnasnet0_75,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.208528062,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3451,alexnet,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.220135265,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
3452,regnet_x_800mf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.082150269,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3453,convnext_tiny,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.055821139,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3454,densenet169,The densenet-169 is larger at just about 55MB in size vs the densenet-121 model's roughly 31MB size,densenet169,1.0,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
3455,regnet_y_1_6gf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.174021637,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3456,regnet_x_400mf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.160338019,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3457,densenet169,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.212375019,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
3458,detection.ssdlite320_mobilenet_v3_large,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.191713919,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3459,efficientnet_b2,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.178895697,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
3460,vgg13,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.109553378,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3461,squeezenet1_1,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.242272799,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
3462,inception_v3,Constructs a DeepLabV3 model with a ResNet-101 backbone,segmentation.deeplabv3_resnet101,0.195071258,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3463,segmentation.deeplabv3_mobilenet_v3_large,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.089387483,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3464,convnext_small,They stack residual blocks ontop of each other to form network: for example  a ResNet-152 has 152 layers using these blocks,resnet152,0.164254908,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3465,efficientnet_b6,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.215082973,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3466,regnet_x_8gf,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.167210392,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
3467,segmentation.fcn_resnet101,The required minimum input size of the model is 29x29,densenet121,0.183188228,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
3468,regnet_y_16gf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.163687583,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
3469,mnasnet1_0,"1x1 Convolution, Batch Normalization, Convolution, Depth wise Separable Convolution, Dropout, Global Average Pooling, Inverted Residual Block, Residual Connection, ReLU, Max Pooling, Softmax, Squeeze-and-Excitation Block",mnasnet1_0,1.0,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
3470,efficientnet_b7,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.06230666,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
3471,vgg13,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.175362255,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3472,vit_l_32,quantization.mobilenet_v3_large is the quantized model of mobilenet_v3_large,quantization.mobilenet_v3_large,0.136539383,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3473,segmentation.deeplabv3_resnet101,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.111629274,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3474,quantization.shufflenet_v2_x2_0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.213516192,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
3475,convnext_small,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.138911821,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3476,inception_v3,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.082402401,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3477,regnet_y_800mf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.112641566,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
3478,regnet_y_1_6gf,The required minimum input size of the model is 32x32,vgg11,0.167151617,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3479,inception_v3,The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block,wide_resnet50_2,0.177095724,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3480,shufflenet_v2_x1_5,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.16328091,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3481,regnet_x_8gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.214160706,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
3482,video.r2plus1d_18,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.067546969,1x32x32,video classification,Null,TV
3483,mnasnet0_5,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.223521754,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3484,regnet_x_16gf,"The RegNet is composed of a stem, body and head",regnet_x_16gf,1.0,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
3485,efficientnet_b3,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.148873474,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
3486,quantization.resnext101_32x8d,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.196106393,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
3487,quantization.shufflenet_v2_x2_0,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.183066539,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
3488,resnet34,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.101159884,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
3489,mobilenet_v3_large,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.11555227,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3490,video.r2plus1d_18,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.205612075,1x32x32,video classification,Null,TV
3491,video.mc3_18,You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close,vit_b_32,0.158212891,1x32x32,video classification,Null,TV
3492,quantization.shufflenet_v2_x1_0,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.126555966,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3493,mnasnet0_75,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.194393973,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3494,segmentation.deeplabv3_mobilenet_v3_large,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.119938202,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3495,densenet169,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.147529962,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
3496,convnext_small,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.109798911,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3497,video.r2plus1d_18,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.179290144,1x32x32,video classification,Null,TV
3498,efficientnet_b4,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.154758352,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
3499,convnext_base,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.021137553,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3500,vit_b_32,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.180336016,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3501,segmentation.lraspp_mobilenet_v3_large,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels",vit_b_16,0.251926783,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3502,wide_resnet50_2,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,1.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
3503,vit_b_16,Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks,wide_resnet50_2,0.114979697,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3504,mobilenet_v2,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.163503493,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3505,googlenet,adopted from BERT,vit_b_16,0.017625759,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3506,wide_resnet101_2,Shallower network means: Number of layers can be reduced and Training time can be shorter as well,wide_resnet101_2,1.0,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
3507,resnext101_32x8d,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.105212895,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3508,mobilenet_v3_small,Note that this model does not include any fine-tuned heads,vit_b_16,0.169924969,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
3509,densenet201,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.146793836,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
3510,video.r2plus1d_18,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.205785356,1x32x32,video classification,Null,TV
3511,convnext_large,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.08322547,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3512,mnasnet1_0,It is the simplest of all other vgg versions,vgg11,0.189489029,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
3513,video.r3d_18,It is the simplest of all other vgg versions,vgg11,0.150876578,1x32x32,video classification,Null,TV
3514,vgg13_bn,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.176784302,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3515,vgg19_bn,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.144895026,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
3516,vgg13,Constructs a RegNetY_8GF architecture,regnet_y_8gf,0.25731485,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3517,quantization.shufflenet_v2_x0_5,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.208641179,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3518,vit_l_32,Wide ResNet-50-2 Wide Residual Networks,wide_resnet50_2,0.13872383,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3519,densenet161,VGG19 has 19.6 billion FLOPs,vgg19,0.137791901,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3520,efficientnet_b5,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.086108865,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
3521,regnet_x_1_6gf,adopted from BERT,vit_b_16,0.010567685,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
3522,regnet_x_16gf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.175928728,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
3523,vit_l_32,deeplabv3_mobilenet_v3_large is used for semantic segmentation,segmentation.deeplabv3_mobilenet_v3_large,0.198742698,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3524,convnext_large,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.093076504,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3525,segmentation.fcn_resnet101,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.132513137,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
3526,regnet_x_16gf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.159827538,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
3527,vgg13_bn,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.134369567,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3528,convnext_small,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.155283984,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3529,regnet_y_32gf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.071019009,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
3530,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.092880271,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3531,convnext_base,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_400mf,0.211131918,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3532,detection.ssd300_vgg16,adopted from BERT,vit_b_16,0.003825122,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
3533,shufflenet_v2_x1_0,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x1_0,1.0,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3534,convnext_base,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.041371409,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3535,detection.retinanet_resnet50_fpn,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.201150731,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
3536,wide_resnet50_2,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.150924533,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
3537,vit_b_16,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.16468115,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3538,convnext_base,Shallower network means: Number of layers can be reduced and Training time can be shorter as well,wide_resnet50_2,0.171590161,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3539,densenet161,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.166456797,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3540,vgg13_bn,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.10984745,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3541,mnasnet1_3,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.033021118,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
3542,convnext_small,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.125399772,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3543,vgg11_bn,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.173458253,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
3544,segmentation.lraspp_mobilenet_v3_large,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.175173573,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3545,segmentation.deeplabv3_resnet50,Constructs a DeepLabV3 model with a ResNet-50 backbone,segmentation.deeplabv3_resnet50,1.0,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
3546,segmentation.deeplabv3_resnet101,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.077874042,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3547,mnasnet1_0,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.053740475,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
3548,mobilenet_v2,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.147948507,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3549,vit_l_16,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.160066133,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3550,vit_l_32,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.141699155,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3551,regnet_x_3_2gf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.176899603,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
3552,vit_b_16,densenet169 is More acurate than DenseNet-121,densenet169,0.102022429,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3553,quantization.inception_v3,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.046876292,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
3554,regnet_y_3_2gf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.154451374,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
3555,detection.fasterrcnn_mobilenet_v3_large_fpn,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
3556,quantization.shufflenet_v2_x0_5,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.125359038,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3557,detection.ssd300_vgg16,"ssd300_vgg16 is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
3558,efficientnet_b4,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.168882778,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
3559,vit_l_16,This issue was later solved by using better backbone architectures like ResNet and other small fixes,detection.ssd300_vgg16,0.106903798,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3560,vit_l_32,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.112848745,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3561,vgg19,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.17491142,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
3562,shufflenet_v2_x1_5,The number of parameters is 3.4 million,mobilenet_v2,0.100682653,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3563,convnext_large,"By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder",vit_b_16,0.172152568,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3564,resnet101,is a convolutional neural network that is 101 layers deep,resnet101,1.0,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
3565,convnext_tiny,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.198921434,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3566,regnet_y_32gf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.183783615,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
3567,densenet161,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.196179613,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3568,video.mc3_18,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.250311649,1x32x32,video classification,Null,TV
3569,segmentation.deeplabv3_resnet101,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.105060231,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3570,shufflenet_v2_x2_0,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.163465801,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3571,efficientnet_b7,obtained by scaling up the baseline network,efficientnet_b7,1.0,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
3572,googlenet,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.167540124,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3573,convnext_tiny,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.13610427,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3574,regnet_x_32gf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.160496804,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
3575,vit_l_32,Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks,wide_resnet50_2,0.139842293,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3576,segmentation.lraspp_mobilenet_v3_large,"(2+1)D  convolutional block, which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution",video.r2plus1d_18,0.226944679,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3577,quantization.shufflenet_v2_x1_5,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.149263401,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3578,resnet152,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.106573518,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
3579,vit_l_16,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.120651571,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3580,mobilenet_v3_large,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.180782278,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3581,regnet_x_1_6gf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.156563851,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
3582,mnasnet0_75,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.030390208,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3583,resnet152,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.223446167,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
3584,detection.maskrcnn_resnet50_fpn,"Mask R-CNN uses anchor boxes to detect multiple objects, objects of different scales, and overlapping objects in an image",detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
3585,vit_l_16,"The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions",detection.fasterrcnn_resnet50_fpn,0.200862936,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3586,quantization.resnet50,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.157464175,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
3587,quantization.mobilenet_v3_large,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.22777997,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
3588,vit_l_32,"ssdlite320_mobilenet_v3_large is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.ssdlite320_mobilenet_v3_large,0.213770916,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3589,resnet50,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.102953309,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
3590,quantization.googlenet,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.197488939,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3591,wide_resnet101_2,"By widening Residual Network (ResNet), the network can be shallower with the same accuracy or improved accuracy",wide_resnet101_2,1.0,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
3592,shufflenet_v2_x1_0,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.157725249,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3593,resnext101_32x8d,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.141852601,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3594,vit_b_32,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.121943247,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3595,mobilenet_v2,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,1.0,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3596,quantization.inception_v3,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.204617988,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
3597,quantization.shufflenet_v2_x0_5,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.167241395,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3598,video.mc3_18,VGG19 has 19.6 billion FLOPs,vgg19,0.099687219,1x32x32,video classification,Null,TV
3599,squeezenet1_1,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.124301426,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
3600,regnet_x_3_2gf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.140874156,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
3601,segmentation.deeplabv3_resnet50,VGG19 has 19.6 billion FLOPs,vgg19,0.102558027,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
3602,convnext_small,"The number of channels in outer 1x1 convolutions is the same, Wide ResNet-50-2 has 2048-1024-2048 channels",wide_resnet50_2,0.223912495,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3603,resnext50_32x4d,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.191955671,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3604,convnext_small,11 weight layers (convolutional + fully connected),vgg11,0.188533001,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3605,segmentation.fcn_resnet50,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.155605286,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3606,vgg16,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.170130528,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
3607,quantization.inception_v3,"Compared with ResNet and PolyNet, ResNeXt has 3.03% Top-5 error rate, which is a large relative improvement of about 15%",resnext50_32x4d,0.168606996,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
3608,quantization.shufflenet_v2_x2_0,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.186241588,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
3609,efficientnet_b1,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.125054079,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
3610,mnasnet0_5,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.09622287,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3611,detection.ssdlite320_mobilenet_v3_large,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.152633787,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3612,mobilenet_v2,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.206507591,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3613,convnext_tiny,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.08515605,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3614,mnasnet0_75,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.201554259,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3615,efficientnet_b6,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.164793605,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3616,inception_v3,ResNeXt is a homogeneous neural network which reduces the number of hyper parameters required by conventional ResNet,resnext50_32x4d,0.188227907,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3617,segmentation.deeplabv3_mobilenet_v3_large,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.147971049,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3618,densenet161,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.066663422,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3619,quantization.resnext101_32x8d,"Compared with ResNet and PolyNet, ResNeXt has 3.03% Top-5 error rate, which is a large relative improvement of about 15%",quantization.resnext101_32x8d,1.0,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
3620,regnet_y_32gf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.189998165,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
3621,mobilenet_v2,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.198842374,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3622,vgg16_bn,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.178722259,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
3623,convnext_large,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.221575111,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3624,vit_b_32,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.082907783,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3625,segmentation.lraspp_mobilenet_v3_large,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.187601364,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3626,regnet_y_3_2gf,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.146419606,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
3627,vit_b_32,2D max pooling in between the weight layers,vgg11,0.108764051,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3628,quantization.mobilenet_v2,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.174487626,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
3629,quantization.shufflenet_v2_x0_5,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.205521862,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3630,convnext_large,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.078103047,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3631,detection.retinanet_resnet50_fpn,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.166250051,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
3632,squeezenet1_0,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.240209507,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
3633,regnet_x_3_2gf,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.185864279,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
3634,shufflenet_v2_x1_5,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.179935881,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3635,efficientnet_b4,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.177589278,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
3636,convnext_large,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.128958086,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3637,densenet161,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.202160258,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3638,resnet18,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.159527227,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
3639,regnet_x_16gf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.163450194,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
3640,densenet121,adopted from BERT,vit_b_16,0.0,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
3641,densenet121,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.169476238,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
3642,segmentation.deeplabv3_resnet101,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.117098472,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3643,regnet_y_16gf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.14145677,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
3644,regnet_x_32gf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.149202932,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
3645,quantization.resnet18,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.065916238,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
3646,detection.ssdlite320_mobilenet_v3_large,The number of parameters is 3.4 million,mobilenet_v2,0.132770248,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3647,detection.retinanet_resnet50_fpn,The number of parameters is 3.4 million,mobilenet_v2,0.078155304,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
3648,vit_b_16,"Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded",vit_b_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3649,quantization.shufflenet_v2_x2_0,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,quantization.shufflenet_v2_x2_0,1.0,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
3650,mnasnet1_3,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.199582775,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
3651,inception_v3,Replacing ReLU with GeLU,convnext_tiny,0.094062549,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3652,quantization.mobilenet_v2,Replacing ReLU with GeLU,convnext_tiny,0.099866467,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
3653,quantization.googlenet,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.113701182,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3654,resnet50,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.167144008,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
3655,detection.fasterrcnn_mobilenet_v3_large_fpn,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.101494393,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
3656,regnet_x_32gf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.136613857,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
3657,mnasnet0_75,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.249525979,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3658,video.mc3_18,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.21956588,1x32x32,video classification,Null,TV
3659,convnext_base,Constructs a DeepLabV3 model with a ResNet-50 backbone,segmentation.deeplabv3_resnet50,0.211031713,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3660,densenet121,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.170510485,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
3661,detection.retinanet_resnet50_fpn,This model has 3.8 billion FLOPs,resnet50,0.124630809,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
3662,video.mc3_18,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.104379484,1x32x32,video classification,Null,TV
3663,regnet_x_32gf,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.129381578,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
3664,regnet_x_8gf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.165692343,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
3665,mobilenet_v3_large,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.151342756,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3666,detection.keypointrcnn_resnet50_fpn,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.207272707,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3667,convnext_small,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.163009277,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3668,inception_v3,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.176803512,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3669,regnet_x_400mf,ReLU non-linearity as activation functions,vgg11,0.149248492,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3670,efficientnet_b4,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.219015971,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
3671,regnet_y_16gf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.149838586,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
3672,convnext_tiny,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.086561022,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3673,video.r2plus1d_18,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.142170358,1x32x32,video classification,Null,TV
3674,mnasnet0_5,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.18876936,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3675,resnet18,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.196133786,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
3676,shufflenet_v2_x0_5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.203686472,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
3677,quantization.shufflenet_v2_x2_0,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.161650588,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
3678,segmentation.fcn_resnet50,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,1.0,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3679,vgg16_bn,"The authors demonstrate its efficacy with the help of a simple, single stage detector, called RetinaNet, which predicts objects by dense sampling of the input image in location, scale and aspect ratio",detection.retinanet_resnet50_fpn,0.165850888,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
3680,vgg16,adopted from BERT,vit_b_16,0.003370401,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
3681,convnext_large,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.229541828,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3682,resnext50_32x4d,The number of parameters is 3.4 million,mobilenet_v2,0.158784784,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3683,efficientnet_b3,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.175630054,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
3684,vit_b_16,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.111027551,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3685,vit_b_16,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.12352135,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3686,regnet_x_32gf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.069510617,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
3687,shufflenet_v2_x1_0,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.159673215,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3688,densenet121,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.22996404,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
3689,vgg11,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.153673865,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
3690,segmentation.fcn_resnet101,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.147905656,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
3691,convnext_tiny,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.162411568,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3692,inception_v3,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.128865459,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3693,segmentation.lraspp_mobilenet_v3_large,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.128864565,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3694,wide_resnet101_2,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.256145947,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
3695,detection.ssdlite320_mobilenet_v3_large,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.178040616,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3696,segmentation.deeplabv3_mobilenet_v3_large,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.111327538,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3697,regnet_x_3_2gf,The required minimum input size of the model is 21x21,squeezenet1_0,0.200696594,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
3698,regnet_y_3_2gf,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.170772216,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
3699,inception_v3,ResNet-152 can obtain 4.49% error rate,resnet152,0.14807944,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3700,segmentation.fcn_resnet50,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.144294927,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3701,vgg11_bn,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
3702,alexnet,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.198423781,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
3703,efficientnet_b6,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.140876091,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3704,detection.ssdlite320_mobilenet_v3_large,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.16004209,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3705,vit_b_32,ReLU non-linearity as activation functions,vgg11,0.091626143,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3706,wide_resnet101_2,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.11318093,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
3707,regnet_y_800mf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.163053231,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
3708,shufflenet_v2_x1_5,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.122671532,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3709,vit_l_16,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.080880985,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3710,regnet_x_400mf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.159909909,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3711,segmentation.fcn_resnet50,"Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded",vit_b_16,0.163766112,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3712,video.r2plus1d_18,"It has 11 weight layers in total, so the name VGG11",vgg11,0.146577172,1x32x32,video classification,Null,TV
3713,regnet_x_400mf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.121773011,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3714,shufflenet_v2_x0_5,The required minimum input size of the model is 32x32,vgg11,0.14193148,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
3715,quantization.shufflenet_v2_x2_0,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.044634794,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
3716,wide_resnet101_2,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.160316161,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
3717,densenet169,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.213980189,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
3718,detection.retinanet_resnet50_fpn,You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close,vit_b_32,0.185274138,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
3719,convnext_large,They stack residual blocks ontop of each other to form network: for example  a ResNet-34 has 34 layers using these blocks,resnet34,0.176053293,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3720,convnext_tiny,They stack residual blocks on top of each other to form network: for example  a ResNet-50 has fifty layers using these blocks,resnet50,0.149330033,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3721,shufflenet_v2_x1_5,"It has 11 weight layers in total, so the name VGG11",vgg11,0.182411419,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3722,resnext50_32x4d,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.123074112,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3723,regnet_x_1_6gf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.086812931,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
3724,detection.ssdlite320_mobilenet_v3_large,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg11,0.200426907,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3725,detection.fasterrcnn_resnet50_fpn,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.11249391,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
3726,vgg13_bn,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.145732136,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3727,convnext_base,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.241603759,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3728,mobilenet_v3_large,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.156483772,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3729,video.r2plus1d_18,"This effectively doubles the number of nonlinearities compared to a network using full 3D convolutions for the same number of parameters, thus rendering the model capable of representing more complex functions",video.r2plus1d_18,1.0,1x32x32,video classification,Null,TV
3730,densenet161,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.191485648,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3731,regnet_x_800mf,adopted from BERT,vit_b_16,0.008749456,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3732,segmentation.deeplabv3_mobilenet_v3_large,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_mobilenet_v3_large,1.0,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3733,convnext_large,They stack residual blocks ontop of each other to form network: for example  a ResNet-152 has 152 layers using these blocks,resnet152,0.174939709,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3734,regnet_x_1_6gf,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.227707078,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
3735,convnext_large,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.172813537,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3736,vgg16,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.209364891,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
3737,regnet_x_400mf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.152575692,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3738,vgg13,Constructs a RegNetY_3.2GF architecture,regnet_y_3_2gf,0.230370767,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3739,resnext50_32x4d,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.227812834,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3740,resnext50_32x4d,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.199350365,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3741,densenet161,ReLU non-linearity as activation functions,vgg11,0.120205621,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3742,mnasnet0_5,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.136280604,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3743,resnet18,adopted from BERT,vit_b_16,0.0,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
3744,squeezenet1_0,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.183252801,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
3745,regnet_y_16gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.176298017,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
3746,quantization.resnet18,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.142435093,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
3747,mnasnet0_75,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.112303038,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3748,quantization.mobilenet_v2,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.177549424,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
3749,resnet34,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.210785651,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
3750,detection.ssdlite320_mobilenet_v3_large,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.194510591,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3751,detection.ssd300_vgg16,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.157224258,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
3752,vgg13_bn,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.171524721,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3753,detection.ssdlite320_mobilenet_v3_large,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.141065576,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3754,efficientnet_b7,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.163432403,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
3755,convnext_small,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_400mf,0.228001718,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3756,efficientnet_b2,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.099086368,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
3757,regnet_x_400mf,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.148443982,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3758,mobilenet_v3_large,"It consists of convolutions, max pooling and dense layers as the basic building blocks",alexnet,0.218418619,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3759,vgg19,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.102724982,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
3760,resnext50_32x4d,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.108299029,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3761,efficientnet_b0,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.201909727,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
3762,mnasnet1_3,The required minimum input size of the model is 17x17,squeezenet1_1,0.193817198,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
3763,efficientnet_b6,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.149422083,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3764,video.r2plus1d_18,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.15984583,1x32x32,video classification,Null,TV
3765,regnet_x_800mf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.153910095,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3766,vit_b_16,The densenet-161 is much larger at 100MB in size vs the densenet-121 model's roughly 31MB size,densenet161,0.109905955,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3767,convnext_tiny,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.158327214,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3768,convnext_large,11 weight layers (convolutional + fully connected),vgg11,0.191863014,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3769,resnext101_32x8d,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.107004956,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3770,convnext_large,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.099678511,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3771,efficientnet_b5,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.17270577,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
3772,inception_v3,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.131268326,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3773,resnext101_32x8d,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.102604907,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3774,video.r2plus1d_18,adopted from BERT,vit_b_16,0.0,1x32x32,video classification,Null,TV
3775,detection.ssd300_vgg16,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
3776,video.r3d_18,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.198047346,1x32x32,video classification,Null,TV
3777,detection.ssdlite320_mobilenet_v3_large,"ssdlite320_mobilenet_v3_large is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.ssdlite320_mobilenet_v3_large,1.0,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3778,detection.ssd300_vgg16,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
3779,googlenet,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.077547867,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3780,regnet_y_8gf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.152391984,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
3781,resnext101_32x8d,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.134389145,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3782,mobilenet_v3_large,It is the simplest of all other vgg versions,vgg11,0.177899194,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3783,convnext_small,The original dataset contained images of size 512x512x1,resnet101,0.071993218,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3784,quantization.shufflenet_v2_x0_5,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.128761417,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3785,shufflenet_v2_x0_5,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.117050313,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
3786,efficientnet_b4,VGG19 has 19.6 billion FLOPs,vgg19,0.134739926,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
3787,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.158722957,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3788,shufflenet_v2_x1_5,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.16176488,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3789,detection.keypointrcnn_resnet50_fpn,The number of parameters is 3.4 million,mobilenet_v2,0.09190093,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3790,resnext101_32x8d,"It is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",resnext101_32x8d,1.0,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3791,alexnet,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.043750795,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
3792,regnet_x_1_6gf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.17160442,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
3793,convnext_base,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.071641194,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3794,regnet_y_16gf,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,1.0,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
3795,resnet34,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.2339609,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
3796,quantization.shufflenet_v2_x1_0,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.15958801,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3797,quantization.shufflenet_v2_x0_5,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.225766321,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
3798,segmentation.deeplabv3_resnet50,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.174979035,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
3799,regnet_x_3_2gf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.141373919,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
3800,segmentation.lraspp_mobilenet_v3_large,r3d_18 is used for video classification,video.r3d_18,0.204776201,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3801,alexnet,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.160592508,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
3802,wide_resnet50_2,Replacing ReLU with GeLU,convnext_tiny,0.132112347,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
3803,resnet34,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",resnet34,1.0,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
3804,densenet201,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.195161472,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
3805,vgg16_bn,vgg16_bn is used for image classification,vgg16_bn,1.0,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
3806,vgg16_bn,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.146866178,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
3807,regnet_x_3_2gf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.18775149,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
3808,quantization.inception_v3,Wide ResNet-50-2 Wide Residual Networks,wide_resnet50_2,0.149166097,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
3809,convnext_tiny,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.182902677,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3810,segmentation.fcn_resnet50,ssd is a method for detecting objects in images using a single deep neural network,detection.ssd300_vgg16,0.200459737,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3811,regnet_x_8gf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.081602723,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
3812,convnext_tiny,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.038984725,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
3813,quantization.shufflenet_v2_x1_0,shufflenet_v2_x1_0 is used for image classification,quantization.shufflenet_v2_x1_0,1.0,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3814,squeezenet1_1,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_1,1.0,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
3815,vit_l_32,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.136862934,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3816,detection.keypointrcnn_resnet50_fpn,"Dense blocks, Batch Normalization(BN)",densenet121,0.114740887,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3817,resnext101_32x8d,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.239443172,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3818,segmentation.deeplabv3_mobilenet_v3_large,vit_l_16 is used for image classification,vit_l_16,0.140806367,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
3819,regnet_y_3_2gf,ReLU non-linearity as activation functions,vgg11,0.170945788,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
3820,convnext_large,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.169400002,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3821,quantization.mobilenet_v2,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.14353702,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
3822,mobilenet_v3_small,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.17862279,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
3823,vgg13,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.125585309,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3824,segmentation.lraspp_mobilenet_v3_large,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.155123085,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3825,quantization.shufflenet_v2_x1_0,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.163854816,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3826,vit_b_32,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.162997807,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3827,vgg11_bn,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.183598913,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
3828,shufflenet_v2_x2_0,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x2_0,1.0,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3829,resnext101_32x8d,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.075257188,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
3830,vgg16_bn,This network is a pretty large network and it has about 138 million parameters,vgg16_bn,1.0,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
3831,mobilenet_v3_large,It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position,detection.fasterrcnn_resnet50_fpn,0.26102798,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3832,shufflenet_v2_x2_0,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.165333612,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3833,efficientnet_b4,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.224516258,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
3834,vgg16_bn,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.145774886,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
3835,regnet_x_3_2gf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.109831219,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
3836,detection.maskrcnn_resnet50_fpn,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.089646209,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
3837,alexnet,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.170404543,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
3838,alexnet,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.146852822,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
3839,quantization.shufflenet_v2_x2_0,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.20783305,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
3840,densenet161,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.165333234,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
3841,regnet_x_1_6gf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.151523396,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
3842,squeezenet1_0,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.212142272,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
3843,quantization.shufflenet_v2_x1_5,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.128440433,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3844,regnet_x_8gf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.127146478,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
3845,efficientnet_b1,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.175176293,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
3846,mnasnet0_5,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.173384994,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3847,squeezenet1_0,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.137044674,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
3848,vgg16_bn,It follows this arrangement of convolution and max pool layers consistently throughout the whole architecture,vgg16_bn,1.0,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
3849,mobilenet_v2,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.140701233,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3850,regnet_y_16gf,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.162680221,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
3851,mobilenet_v2,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.173924233,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3852,shufflenet_v2_x2_0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.21030554,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3853,efficientnet_b2,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.172160047,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
3854,resnet152,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.115969033,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
3855,inception_v3,Constructs a RegNetY_32GF architecture,regnet_y_32gf,0.213854909,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3856,regnet_y_8gf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.137884423,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
3857,segmentation.lraspp_mobilenet_v3_large,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.17832838,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
3858,vgg13,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.191614898,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3859,segmentation.deeplabv3_resnet101,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.0839007,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3860,detection.fasterrcnn_resnet50_fpn,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.162288474,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
3861,detection.retinanet_resnet50_fpn,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.057239009,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
3862,resnext50_32x4d,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.149131775,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3863,regnet_y_800mf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.150319125,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
3864,quantization.shufflenet_v2_x2_0,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.127973963,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
3865,regnet_y_3_2gf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.191437104,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
3866,regnet_x_800mf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.155949163,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3867,regnet_y_1_6gf,The required minimum input size of the model is 29x29,densenet121,0.184577492,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
3868,vgg13,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.118445545,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3869,resnet50,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.226981837,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
3870,convnext_base,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.179497158,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3871,wide_resnet101_2,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.201478146,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
3872,vgg16_bn,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.139445587,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
3873,vgg13,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.160827274,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3874,googlenet,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.132504063,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3875,detection.retinanet_resnet50_fpn,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.039546736,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
3876,resnet34,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.138106575,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
3877,quantization.resnet50,It is the simplest of all other vgg versions,vgg11,0.152540186,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
3878,detection.ssd300_vgg16,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
3879,resnet18,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.142577166,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
3880,mobilenet_v3_large,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.19670271,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3881,vit_l_16,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.137403337,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3882,quantization.mobilenet_v2,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.152734116,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
3883,vit_l_16,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.063244082,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3884,shufflenet_v2_x2_0,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.153769905,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3885,regnet_x_800mf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.166805953,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3886,mnasnet0_75,VGG19 has 19.6 billion FLOPs,vgg19,0.130098626,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3887,regnet_y_16gf,2D max pooling in between the weight layers,vgg11,0.165087354,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
3888,segmentation.fcn_resnet50,The required minimum input size of the model is 32x32,vgg11,0.161497429,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3889,wide_resnet101_2,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.104296325,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
3890,mnasnet0_75,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.207711409,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
3891,quantization.shufflenet_v2_x1_0,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.208348727,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3892,quantization.shufflenet_v2_x1_0,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.185122415,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3893,vit_l_32,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.107775474,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3894,regnet_y_400mf,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.051459763,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
3895,quantization.mobilenet_v2,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.056655791,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
3896,vgg11_bn,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.10663856,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
3897,shufflenet_v2_x2_0,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.134366243,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3898,regnet_y_800mf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.190478292,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
3899,efficientnet_b6,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.176061086,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3900,vgg13,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.198124051,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3901,segmentation.fcn_resnet101,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.169681436,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
3902,detection.fasterrcnn_resnet50_fpn,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.068328004,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
3903,vit_l_16,MobileNetv3-Large is 27% faster than MobileNetV2 while maintaining similar mAP,mobilenet_v3_large,0.08462342,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3904,mnasnet0_5,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.19993932,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3905,mobilenet_v2,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.202325821,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
3906,efficientnet_b7,"EfficientNet-B7 achieves new state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy, while being 8.4x smaller than the best existing CNN",efficientnet_b7,1.0,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
3907,regnet_y_800mf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.182286154,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
3908,regnet_x_16gf,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.151910734,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
3909,regnet_y_8gf,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.14130698,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
3910,video.mc3_18,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.216613657,1x32x32,video classification,Null,TV
3911,shufflenet_v2_x1_0,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.11143658,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3912,vgg13,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.103027107,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3913,vit_b_32,11 weight layers (convolutional + fully connected),vgg11,0.146653472,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3914,squeezenet1_1,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.190147723,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
3915,shufflenet_v2_x2_0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.205128498,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
3916,segmentation.fcn_resnet50,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.01143154,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
3917,densenet121,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.201606447,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
3918,densenet201,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.216507117,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
3919,quantization.resnext101_32x8d,ResNeXt is a homogeneous neural network which reduces the number of hyper parameters required by conventional ResNet,quantization.resnext101_32x8d,1.0,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
3920,regnet_x_400mf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.085977602,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3921,efficientnet_b7,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.214807202,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
3922,vit_b_16,vit_b_16 is used for image classification,vit_b_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3923,convnext_small,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.245629115,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
3924,resnext50_32x4d,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.180056475,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3925,mnasnet1_0,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.120702377,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
3926,regnet_y_32gf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.16270967,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
3927,vgg16,It follows this arrangement of convolution and max pool layers consistently throughout the whole architecture,vgg16,1.0,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
3928,vgg13_bn,The overall structure of 3D ResNet with 4 residual groups,video.r3d_18,0.190826277,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3929,vit_l_32,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.121872077,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
3930,quantization.shufflenet_v2_x1_5,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.185425898,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
3931,efficientnet_b6,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.129723468,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
3932,googlenet,"Googlenet has 9 Inception modules, 22 layers deep (21 convolutional layers and one fully connected layer) when counting only layers with parameters (or 27 layers if we also count pooling) and 12 times fewer parameters than AlexNet",googlenet,1.0,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
3933,vit_l_16,MobileNetV3 is a convolutional neural network that is designed for mobile phone CPUs,mobilenet_v3_small,0.148300944,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3934,detection.keypointrcnn_resnet50_fpn,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.17259815,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3935,resnet18,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.238529818,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
3936,quantization.shufflenet_v2_x2_0,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.038659676,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
3937,quantization.inception_v3,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.167273074,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
3938,quantization.mobilenet_v3_large,adopted from BERT,vit_b_16,0.015429175,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
3939,quantization.googlenet,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.157796243,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3940,mobilenet_v3_large,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.178556761,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
3941,shufflenet_v2_x1_5,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.10506419,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3942,vgg13_bn,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.183753062,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3943,shufflenet_v2_x1_0,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.151515031,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3944,vit_b_32,The key building block is an Inception Module,inception_v3,0.079657732,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
3945,detection.ssd300_vgg16,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
3946,detection.fasterrcnn_resnet50_fpn,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.149886646,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
3947,quantization.inception_v3,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.150307957,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
3948,detection.fasterrcnn_mobilenet_v3_large_fpn,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.143197311,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
3949,quantization.googlenet,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.085035986,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
3950,efficientnet_b2,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.225084653,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
3951,regnet_x_400mf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_x_400mf,1.0,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3952,convnext_base,quantization.resnet18 is the quantized model of resnet18,quantization.resnet18,0.246626586,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3953,video.mc3_18,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.18434283,1x32x32,video classification,Null,TV
3954,detection.ssdlite320_mobilenet_v3_large,adopted from BERT,vit_b_16,0.025795351,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3955,mobilenet_v3_small,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,0.154890645,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
3956,convnext_base,MobileNet v3-Large has 5.4 million parameters,mobilenet_v3_large,0.121641176,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3957,densenet201,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.16804018,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
3958,vgg13,The overall structure of 3D ResNet with 4 residual groups,video.r3d_18,0.224980209,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
3959,regnet_x_1_6gf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_x_1_6gf,1.0,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
3960,quantization.shufflenet_v2_x1_0,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.112669105,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3961,detection.keypointrcnn_resnet50_fpn,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.143699491,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
3962,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.048250646,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3963,resnet101,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.159777665,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
3964,regnet_x_400mf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.172320123,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
3965,resnet34,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.117832999,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
3966,vit_b_16,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.124911514,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
3967,regnet_y_8gf,"The RegNet is composed of a stem, body and head",regnet_y_8gf,1.0,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
3968,inception_v3,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.158349499,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3969,video.mc3_18,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.069842434,1x32x32,video classification,Null,TV
3970,detection.maskrcnn_resnet50_fpn,Replacing ReLU with GeLU,convnext_tiny,0.138389627,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
3971,shufflenet_v2_x0_5,The number of parameters is 3.4 million,mobilenet_v2,0.098695164,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
3972,efficientnet_b0,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.123367596,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
3973,inception_v3,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.125894099,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3974,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.072552768,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
3975,quantization.resnet50,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.095400032,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
3976,regnet_x_32gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.178647135,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
3977,shufflenet_v2_x1_0,ReLU non-linearity as activation functions,vgg11,0.14266809,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3978,shufflenet_v2_x1_5,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.199938407,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3979,mnasnet1_0,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.168149713,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
3980,mnasnet1_0,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.176541613,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
3981,segmentation.fcn_resnet101,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.133673844,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
3982,segmentation.deeplabv3_resnet101,deeplabv3_resnet101 is used for semantic segmentation,segmentation.deeplabv3_resnet101,1.0,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3983,detection.ssdlite320_mobilenet_v3_large,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.16601337,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
3984,regnet_x_800mf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.196042343,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
3985,convnext_base,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet18,0.197354329,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
3986,inception_v3,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.189029492,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
3987,convnext_large,They stack residual blocks ontop of each other to form network,vgg19_bn,0.108559271,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
3988,segmentation.deeplabv3_resnet101,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.109910553,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
3989,resnext50_32x4d,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.210092951,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
3990,shufflenet_v2_x0_5,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,1.0,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
3991,quantization.inception_v3,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.12824278,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
3992,shufflenet_v2_x1_0,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.053132206,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
3993,quantization.shufflenet_v2_x1_0,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.105651686,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
3994,mnasnet0_5,Proposed a new layer called ROI Pooling that extracts equal-length feature vectors from all proposals (  ROIs) in the same image,detection.fasterrcnn_resnet50_fpn,0.221199403,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3995,vit_l_16,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,shufflenet_v2_x0_5,0.146375511,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
3996,vgg13_bn,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.123464341,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
3997,shufflenet_v2_x1_5,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.124438918,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
3998,wide_resnet101_2,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.130013035,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
3999,shufflenet_v2_x1_5,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.048324595,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4000,segmentation.fcn_resnet50,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.099564591,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
4001,segmentation.fcn_resnet101,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.08797863,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
4002,segmentation.lraspp_mobilenet_v3_large,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.214829898,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4003,detection.ssdlite320_mobilenet_v3_large,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.139161346,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4004,regnet_x_3_2gf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_x_3_2gf,1.0,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
4005,video.mc3_18,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,1.0,1x32x32,video classification,Null,TV
4006,mobilenet_v3_large,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.105725657,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4007,segmentation.lraspp_mobilenet_v3_large,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.235564429,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4008,shufflenet_v2_x0_5,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.164809959,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4009,efficientnet_b1,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.085579787,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
4010,segmentation.deeplabv3_mobilenet_v3_large,implemented using Atrous Convolution,segmentation.deeplabv3_mobilenet_v3_large,1.0,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4011,resnet152,adopted from BERT,vit_b_16,0.0,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4012,detection.retinanet_resnet50_fpn,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.036703264,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
4013,vit_l_16,based on ResNeXt-50 as its backbone along with the feature Pyramid Network (FPN) for better accuracy and speed,detection.maskrcnn_resnet50_fpn,0.184801914,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4014,quantization.googlenet,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.088468424,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4015,densenet169,The key building block is an Inception Module,inception_v3,0.164806114,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
4016,vit_l_16,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.170343099,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4017,efficientnet_b5,The key building block is an Inception Module,inception_v3,0.207534157,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4018,mobilenet_v3_small,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.118607126,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
4019,mnasnet1_3,"60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax",alexnet,0.18302455,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4020,convnext_large,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.20196292,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4021,shufflenet_v2_x1_5,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.052260418,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4022,squeezenet1_1,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.103076419,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4023,segmentation.deeplabv3_resnet101,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.182834683,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4024,regnet_y_32gf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_32gf,1.0,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4025,mobilenet_v2,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.20428817,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
4026,efficientnet_b6,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.195097081,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
4027,efficientnet_b2,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b2,1.0,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4028,vit_l_32,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.062581907,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4029,regnet_y_8gf,It is the simplest of all other vgg versions,vgg11,0.194671232,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4030,segmentation.deeplabv3_resnet50,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.203650226,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4031,regnet_x_3_2gf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.160737835,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
4032,mnasnet0_75,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.142360367,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4033,segmentation.deeplabv3_mobilenet_v3_large,vit_l_32 is used for image classification,vit_l_32,0.133348077,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4034,convnext_base,MNASNet with depth multiplier of 0.75,mnasnet0_75,0.198236898,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4035,inception_v3,Constructs a RegNetY_400MF architecture,regnet_y_400mf,0.225906079,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4036,quantization.resnext101_32x8d,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.147202513,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4037,quantization.googlenet,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.199214267,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4038,efficientnet_b0,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.134764782,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
4039,convnext_tiny,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.126051532,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4040,resnet152,The required minimum input size of the model is 17x17,squeezenet1_1,0.226429724,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4041,squeezenet1_0,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,1.0,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4042,mnasnet1_0,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.096546681,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
4043,vgg19_bn,They stack residual blocks ontop of each other to form network,vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
4044,squeezenet1_0,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.161288499,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4045,regnet_y_400mf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.142156789,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
4046,segmentation.deeplabv3_resnet101,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.173332752,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4047,shufflenet_v2_x0_5,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.123725241,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4048,mnasnet0_75,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.192391125,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4049,quantization.resnext101_32x8d,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.121353754,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4050,segmentation.fcn_resnet101,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.240331041,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
4051,efficientnet_b2,Note that this model does not include any fine-tuned heads,vit_b_16,0.175601618,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4052,vit_l_16,Note that this model does not include any fine-tuned heads,vit_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4053,vit_b_16,deeplabv3_mobilenet_v3_large is used for semantic segmentation,segmentation.deeplabv3_mobilenet_v3_large,0.180251552,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4054,resnet152,is a convolutional neural network that is 152 layers deep,resnet152,1.0,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4055,detection.keypointrcnn_resnet50_fpn,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,1.0,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4056,shufflenet_v2_x1_0,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.094711993,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4057,regnet_y_800mf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.106130232,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
4058,efficientnet_b2,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.180097857,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4059,vit_l_16,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.140480603,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4060,convnext_base,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x0_5,0.190751656,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4061,convnext_tiny,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.145844272,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4062,regnet_x_32gf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.156417859,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
4063,quantization.resnext101_32x8d,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.158156419,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4064,segmentation.fcn_resnet101,"Fully Convolutional Networks, or FCNs, are an architecture used mainly for semantic segmentation",segmentation.fcn_resnet101,1.0,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
4065,shufflenet_v2_x0_5,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,1.0,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4066,squeezenet1_0,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.122379006,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4067,vit_l_16,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet18,0.177700725,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4068,convnext_base,The required minimum input size of the model is 17x17,squeezenet1_1,0.161729496,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4069,mnasnet0_75,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.135527963,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4070,quantization.mobilenet_v3_large,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.171951143,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4071,mnasnet0_75,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.027118526,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4072,mnasnet0_5,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.172727625,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4073,quantization.shufflenet_v2_x2_0,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.151247801,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
4074,vit_b_32,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.126270302,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4075,vgg13,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.225185577,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4076,convnext_tiny,The densenet-161 is much larger at 100MB in size vs the densenet-121 model's roughly 31MB size,densenet161,0.187215893,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4077,quantization.resnext101_32x8d,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.050477977,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4078,mnasnet0_75,mnasnet0_75 is used for image classification,mnasnet0_75,1.0,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4079,efficientnet_b2,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.15865414,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4080,mnasnet1_3,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.252093072,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4081,quantization.googlenet,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.117933307,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4082,convnext_small,They stack residual blocks ontop of each other to form network: for example  a ResNet-34 has 34 layers using these blocks,resnet34,0.16889234,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4083,convnext_large,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.232339872,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4084,segmentation.deeplabv3_resnet50,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.172629693,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4085,video.mc3_18,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.179605897,1x32x32,video classification,Null,TV
4086,mnasnet1_3,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.164988389,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4087,vgg13,Constructs a RegNetX_8GF architecture,regnet_x_8gf,0.249145191,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4088,mnasnet0_5,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.158521697,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4089,regnet_x_8gf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.18219279,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4090,efficientnet_b4,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.143457375,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
4091,quantization.shufflenet_v2_x1_5,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.041808335,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
4092,quantization.resnext101_32x8d,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.159837433,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4093,regnet_y_16gf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_16gf,1.0,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
4094,mnasnet0_5,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.153822054,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4095,quantization.resnet18,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.069961523,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
4096,mnasnet0_5,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.224396326,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4097,regnet_x_3_2gf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.103682665,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
4098,quantization.inception_v3,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.11041046,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4099,convnext_tiny,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.082346332,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4100,vit_l_16,EfficientNet-B7 only has 66 million parameters,efficientnet_b7,0.131763197,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4101,vit_b_32,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.173065893,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4102,detection.fasterrcnn_resnet50_fpn,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.080042667,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
4103,segmentation.deeplabv3_resnet50,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.156875981,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4104,segmentation.deeplabv3_resnet50,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.11149304,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4105,regnet_x_1_6gf,It is the simplest of all other vgg versions,vgg11,0.191259858,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4106,regnet_y_8gf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.140372341,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4107,detection.fasterrcnn_mobilenet_v3_large_fpn,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.229782983,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4108,densenet161,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.151733022,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
4109,mobilenet_v2,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.18542068,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
4110,convnext_small,Constructs a DeepLabV3 model with a ResNet-50 backbone,segmentation.deeplabv3_resnet50,0.204233608,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4111,segmentation.lraspp_mobilenet_v3_large,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.203123364,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4112,convnext_tiny,"The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks",efficientnet_b0,0.138274133,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4113,googlenet,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,1.0,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4114,shufflenet_v2_x1_5,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.187257974,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4115,regnet_x_16gf,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.160399576,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
4116,regnet_x_800mf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.160757395,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4117,quantization.shufflenet_v2_x0_5,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.149057863,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4118,shufflenet_v2_x1_5,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.183943278,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4119,detection.fasterrcnn_mobilenet_v3_large_fpn,"The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals",detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4120,vit_b_16,"It is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",resnext101_32x8d,0.133643951,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4121,shufflenet_v2_x1_5,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.231324639,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4122,regnet_y_3_2gf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_3_2gf,1.0,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
4123,shufflenet_v2_x1_0,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.207147418,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4124,segmentation.lraspp_mobilenet_v3_large,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.236930901,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4125,vgg13,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.19990566,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4126,segmentation.fcn_resnet50,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.12327898,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
4127,video.mc3_18,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.103149131,1x32x32,video classification,Null,TV
4128,resnext50_32x4d,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.167931855,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4129,shufflenet_v2_x0_5,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.123959696,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4130,regnet_y_400mf,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,1.0,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
4131,regnet_y_1_6gf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.159775916,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
4132,vit_l_16,GoogLeNet is a type of convolutional neural network based on the Inception architecture,googlenet,0.165606717,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4133,regnet_x_1_6gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.166997439,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4134,regnet_y_16gf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.134068456,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
4135,quantization.shufflenet_v2_x2_0,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.073690232,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
4136,vgg16,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.140644833,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
4137,resnet50,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.144221825,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
4138,quantization.mobilenet_v3_large,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.12837793,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4139,detection.ssd300_vgg16,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
4140,vit_l_32,"The number of channels in outer 1x1 convolutions is the same, Wide ResNet-50-2 has 2048-1024-2048 channels",wide_resnet50_2,0.151783583,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4141,regnet_y_16gf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.152113056,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
4142,convnext_large,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.174066494,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4143,convnext_large,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.182166583,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4144,segmentation.deeplabv3_resnet101,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.195980065,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4145,resnet34,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.167798041,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
4146,googlenet,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.144232266,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4147,mobilenet_v3_large,"This effectively doubles the number of nonlinearities compared to a network using full 3D convolutions for the same number of parameters, thus rendering the model capable of representing more complex functions",video.r2plus1d_18,0.239910907,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4148,vit_l_16,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.131844765,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4149,regnet_x_400mf,regnet_x_400mf is used for image classification,regnet_x_400mf,1.0,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
4150,detection.fasterrcnn_mobilenet_v3_large_320_fpn,It works similarly to Faster R-CNN with ResNet-50 FPN backbone,detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
4151,squeezenet1_0,Replacing ReLU with GeLU,convnext_tiny,0.064965482,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4152,vit_l_32,The key building block is an Inception Module,inception_v3,0.139040473,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4153,efficientnet_b6,It is the simplest of all other vgg versions,vgg11,0.165775964,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
4154,efficientnet_b1,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.104795463,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
4155,convnext_large,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.079278788,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4156,detection.ssdlite320_mobilenet_v3_large,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.136260916,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4157,segmentation.deeplabv3_resnet101,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels",vit_b_16,0.171275115,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4158,mobilenet_v3_large,mobilenet_v3_large is used for image classification,mobilenet_v3_large,1.0,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4159,quantization.googlenet,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.172472868,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4160,densenet169,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.113697018,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
4161,inception_v3,The key building block is an Inception Module,inception_v3,1.0,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4162,regnet_x_8gf,Replacing ReLU with GeLU,convnext_tiny,0.141699829,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4163,regnet_y_16gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.209720308,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
4164,vit_b_16,MNASNet with depth multiplier of 1.3,mnasnet1_3,0.199474656,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4165,googlenet,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.12304398,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4166,mnasnet1_3,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.064907295,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4167,segmentation.deeplabv3_resnet50,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.077698106,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4168,shufflenet_v2_x1_0,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.175749424,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4169,regnet_y_32gf,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.177617464,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4170,regnet_y_1_6gf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.104858348,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
4171,mnasnet0_75,Replacing ReLU with GeLU,convnext_tiny,0.083633757,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4172,detection.fasterrcnn_mobilenet_v3_large_fpn,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.081094967,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4173,vit_b_16,They stack residual blocks ontop of each other to form network: for example  a ResNet-152 has 152 layers using these blocks,resnet152,0.119353185,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4174,quantization.resnet18,resnet18  is used for image classification,quantization.resnet18,1.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
4175,resnext101_32x8d,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.164594818,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4176,resnet50,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.164756292,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
4177,quantization.shufflenet_v2_x0_5,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.149101998,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4178,convnext_tiny,quantization.mobilenet_v3_large is the quantized model of mobilenet_v3_large,quantization.mobilenet_v3_large,0.157777404,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4179,quantization.inception_v3,"Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet)",squeezenet1_0,0.189598746,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4180,googlenet,"A fixed size of (224 * 224) RGB image was given as input to this network which means that the matrix was of shape (224,224,3)",vgg19,0.158898664,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4181,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"Dense blocks, Batch Normalization(BN)",densenet121,0.137216197,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
4182,video.mc3_18,This model has 3.8 billion FLOPs,resnet50,0.134270579,1x32x32,video classification,Null,TV
4183,quantization.mobilenet_v3_large,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.213071574,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4184,squeezenet1_0,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.080931686,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4185,detection.retinanet_resnet50_fpn,adopted from BERT,vit_b_16,0.001138883,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
4186,mobilenet_v2,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.150806534,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
4187,regnet_y_8gf,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.153708257,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4188,efficientnet_b5,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.151806084,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4189,vit_b_32,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4190,regnet_x_800mf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.163631164,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4191,detection.ssd300_vgg16,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.131725316,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
4192,shufflenet_v2_x1_0,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.180983276,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4193,regnet_y_32gf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.15941392,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4194,segmentation.deeplabv3_mobilenet_v3_large,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.090153409,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4195,efficientnet_b4,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.201683569,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
4196,detection.ssdlite320_mobilenet_v3_large,"Inception v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using Label Smoothing, Factorized 7 x 7 convolutions, and the use of an auxiliary classifier to propagate label information lower down the network (along with the use of batch normalization for layers in the side head)",inception_v3,0.187635198,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4197,inception_v3,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,0.210442886,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4198,vit_b_16,They stack residual blocks ontop of each other to form network,vgg19_bn,0.110623849,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4199,vgg13,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.172382498,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4200,mnasnet1_3,mnasnet1_3 is used for image classification,mnasnet1_3,1.0,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4201,convnext_small,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.12561011,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4202,segmentation.deeplabv3_resnet101,implemented using Atrous Convolution,segmentation.deeplabv3_resnet101,1.0,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4203,inception_v3,Constructs a RegNetX_32GF architecture,regnet_x_32gf,0.194526268,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4204,resnet34,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.097993193,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
4205,mnasnet0_5,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.237794826,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4206,regnet_y_1_6gf,2D max pooling in between the weight layers,vgg11,0.163169944,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
4207,vgg16_bn,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.128039732,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
4208,resnext101_32x8d,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.106241362,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4209,quantization.mobilenet_v2,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.139529593,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
4210,vgg16_bn,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.17959145,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
4211,detection.ssd300_vgg16,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.137807693,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
4212,resnext50_32x4d,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.102348656,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4213,resnet34,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.187550311,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
4214,quantization.shufflenet_v2_x1_5,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.148553234,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
4215,efficientnet_b2,"It has 11 weight layers in total, so the name VGG11",vgg11,0.218684685,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4216,segmentation.lraspp_mobilenet_v3_large,VGG19 has 19.6 billion FLOPs,vgg19,0.142701259,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4217,regnet_x_1_6gf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.153551285,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4218,shufflenet_v2_x1_5,shufflenet_v2_x1_5 is used for image classification,shufflenet_v2_x1_5,1.0,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4219,convnext_large,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.160395323,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4220,detection.fasterrcnn_resnet50_fpn,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.126655644,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
4221,resnext50_32x4d,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.090844603,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4222,regnet_x_400mf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.180892604,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
4223,quantization.inception_v3,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.148605588,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4224,convnext_large,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.065357734,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4225,quantization.googlenet,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.089287231,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4226,quantization.shufflenet_v2_x0_5,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.186407197,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4227,mnasnet1_0,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.144857411,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
4228,regnet_x_8gf,It is the simplest of all other vgg versions,vgg11,0.19569776,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4229,resnet101,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.128571299,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
4230,quantization.resnet18,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.13859773,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
4231,convnext_tiny,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,0.190651906,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4232,resnet101,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.182313977,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
4233,inception_v3,"resnext50_32x4d is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",resnext50_32x4d,0.211514371,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4234,detection.fasterrcnn_mobilenet_v3_large_fpn,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.225277764,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4235,googlenet,The required minimum input size of the model is 21x21,squeezenet1_0,0.15681493,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4236,resnet152,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.057783658,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4237,inception_v3,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.203036082,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4238,segmentation.deeplabv3_mobilenet_v3_large,adopted from BERT,vit_b_16,0.002418852,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4239,vit_l_32,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.144191018,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4240,convnext_large,The required minimum input size of the model is 21x21,squeezenet1_0,0.17105832,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4241,regnet_x_16gf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.150283494,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
4242,inception_v3,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.200103424,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4243,vgg11,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.221499209,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4244,quantization.inception_v3,inception_v3 is used for image classification,quantization.inception_v3,1.0,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4245,detection.ssdlite320_mobilenet_v3_large,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.166303821,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4246,resnext101_32x8d,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.158099109,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4247,densenet169,"Dense blocks, Batch Normalization(BN)",densenet169,1.0,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
4248,vgg11_bn,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.19428468,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
4249,mobilenet_v3_large,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.14481234,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4250,squeezenet1_1,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.173400273,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4251,video.r3d_18,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.195358004,1x32x32,video classification,Null,TV
4252,vit_b_32,There are 4 convolutional layers in each module (excluding the 1x1 convolutional layer),resnet18,0.170406155,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4253,convnext_small,ResNet-152 can obtain 4.49% error rate,resnet152,0.219346645,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4254,mnasnet0_75,"By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder",vit_b_16,0.22236604,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4255,mnasnet1_3,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.214382589,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4256,segmentation.deeplabv3_resnet50,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,1.0,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4257,resnext50_32x4d,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.139494843,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4258,squeezenet1_1,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.153394191,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4259,alexnet,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.162402286,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
4260,vgg11,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.131791186,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4261,regnet_y_1_6gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.175341975,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
4262,regnet_x_800mf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.148642314,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4263,alexnet,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.058032723,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
4264,segmentation.deeplabv3_resnet101,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet101,1.0,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4265,convnext_small,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.147242887,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4266,quantization.googlenet,ReLU non-linearity as activation functions,vgg11,0.15385042,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4267,regnet_x_800mf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.096476028,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4268,shufflenet_v2_x0_5,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.159072309,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4269,regnet_x_800mf,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.158996794,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4270,resnet50,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.195491756,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
4271,densenet121,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.17015422,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
4272,video.r3d_18,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.189205334,1x32x32,video classification,Null,TV
4273,efficientnet_b4,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.18192632,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
4274,detection.keypointrcnn_resnet50_fpn,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.129180145,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4275,vit_b_16,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.113013003,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4276,shufflenet_v2_x1_0,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.174945529,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4277,vit_l_32,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384",vit_l_32,1.0,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4278,quantization.shufflenet_v2_x1_0,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.161052825,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4279,quantization.shufflenet_v2_x1_0,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",quantization.shufflenet_v2_x1_0,1.0,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4280,regnet_y_1_6gf,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.143134098,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
4281,regnet_y_32gf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_32gf,1.0,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4282,regnet_x_400mf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.172271355,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
4283,mobilenet_v3_large,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.165969452,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4284,detection.keypointrcnn_resnet50_fpn,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,1.0,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4285,quantization.shufflenet_v2_x1_5,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.18093048,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
4286,quantization.shufflenet_v2_x1_0,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.211443342,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4287,mnasnet1_3,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.226286332,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4288,vgg16,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.21317341,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
4289,mobilenet_v3_small,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.14729993,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
4290,convnext_small,The required minimum input size of the model is 32x32,vgg11,0.133533768,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4291,shufflenet_v2_x2_0,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.055793492,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
4292,efficientnet_b2,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.168012425,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4293,regnet_y_32gf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.160574529,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4294,vit_b_32,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.097409733,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4295,resnet50,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet50,1.0,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
4296,vit_l_32,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.164549499,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4297,vit_b_16,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.123584798,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4298,googlenet,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.077330796,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4299,quantization.mobilenet_v3_large,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.093212632,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4300,wide_resnet50_2,is a convolutional neural network that is 50 layers deep,wide_resnet50_2,1.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
4301,detection.fasterrcnn_mobilenet_v3_large_320_fpn,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.228046832,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
4302,detection.fasterrcnn_resnet50_fpn,Proposed a new layer called ROI Pooling that extracts equal-length feature vectors from all proposals (  ROIs) in the same image,detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
4303,quantization.resnet18,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.206896105,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
4304,detection.ssdlite320_mobilenet_v3_large,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.166494631,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4305,mnasnet1_3,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.172667831,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4306,densenet201,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.181127946,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
4307,convnext_small,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.078197685,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4308,quantization.inception_v3,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.185084278,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4309,vgg11_bn,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.213955535,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
4310,regnet_y_32gf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.066701856,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4311,efficientnet_b6,VGG19 has 19.6 billion FLOPs,vgg19,0.106353849,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
4312,vit_l_16,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.122956223,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4313,vit_l_16,"Googlenet has 9 Inception modules, 22 layers deep (21 convolutional layers and one fully connected layer) when counting only layers with parameters (or 27 layers if we also count pooling) and 12 times fewer parameters than AlexNet",googlenet,0.14193141,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4314,detection.fasterrcnn_resnet50_fpn,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.180806648,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
4315,vit_b_32,GoogLeNet is a type of convolutional neural network based on the Inception architecture,googlenet,0.115787085,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4316,mnasnet1_3,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.215684886,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4317,mnasnet1_3,The required minimum input size of the model is 32x32,vgg11,0.114887491,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4318,resnext101_32x8d,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.182878897,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4319,regnet_y_8gf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.08414676,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4320,vit_l_16,They stack residual blocks ontop of each other to form network: for example  a ResNet-152 has 152 layers using these blocks,resnet152,0.117102554,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4321,efficientnet_b5,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.177231962,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4322,detection.fasterrcnn_mobilenet_v3_large_320_fpn,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.216563829,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
4323,resnet101,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.218508171,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
4324,shufflenet_v2_x0_5,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.177929499,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4325,vgg11_bn,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.156583285,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
4326,detection.ssdlite320_mobilenet_v3_large,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,1.0,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4327,regnet_x_800mf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_x_800mf,1.0,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4328,detection.ssdlite320_mobilenet_v3_large,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.098293602,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4329,vgg13_bn,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.14092628,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4330,detection.retinanet_resnet50_fpn,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.139030422,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
4331,resnet50,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.187003613,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
4332,densenet201,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.151557367,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
4333,convnext_small,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.103341567,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4334,quantization.inception_v3,You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close,vit_b_32,0.112174355,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4335,vit_b_16,"By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder",vit_b_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4336,squeezenet1_1,Constructs a Keypoint R-CNN model with a ResNet-50-FPN backbone,detection.keypointrcnn_resnet50_fpn,0.265656141,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4337,detection.ssdlite320_mobilenet_v3_large,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.124368195,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4338,regnet_x_800mf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.117392172,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4339,detection.fasterrcnn_resnet50_fpn,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.183672992,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
4340,regnet_x_400mf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.10149957,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
4341,resnet34,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.068055799,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
4342,regnet_x_8gf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.06802448,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4343,regnet_y_8gf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.130428467,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4344,video.r3d_18,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.223592947,1x32x32,video classification,Null,TV
4345,efficientnet_b0,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.256067538,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
4346,convnext_base,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.041226535,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4347,convnext_small,"A ConvNet which has the design simplicity of Convolutional Neural Networks while having better accuracy, performance and scalability than Vision Transformers.",convnext_small,1.0,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4348,regnet_x_1_6gf,The required minimum input size of the model is 29x29,densenet121,0.189584883,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4349,resnet101,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.189311034,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
4350,convnext_large,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.162545785,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4351,quantization.inception_v3,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.113101303,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4352,video.r3d_18,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.129987533,1x32x32,video classification,Null,TV
4353,segmentation.deeplabv3_mobilenet_v3_large,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.083365659,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4354,video.r2plus1d_18,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.209428295,1x32x32,video classification,Null,TV
4355,vgg16_bn,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.181350368,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
4356,vit_l_32,"The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks",efficientnet_b0,0.090564996,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4357,googlenet,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.135536895,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4358,squeezenet1_1,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.161700829,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4359,efficientnet_b5,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.225273412,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4360,efficientnet_b5,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.156141508,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4361,quantization.mobilenet_v3_large,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.134613479,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4362,resnet18,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.101080555,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4363,mnasnet0_5,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.094878798,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4364,quantization.shufflenet_v2_x0_5,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.084246526,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4365,inception_v3,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.145057511,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4366,quantization.shufflenet_v2_x1_0,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.183700523,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4367,convnext_small,adopted from BERT,vit_b_16,0.025859012,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4368,densenet161,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.201882941,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
4369,inception_v3,Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone,segmentation.lraspp_mobilenet_v3_large,0.171120336,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4370,wide_resnet50_2,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.170538567,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
4371,quantization.shufflenet_v2_x0_5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.207569152,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4372,shufflenet_v2_x2_0,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.16578776,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
4373,squeezenet1_0,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.186982435,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4374,detection.fasterrcnn_mobilenet_v3_large_fpn,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.226472288,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4375,detection.ssdlite320_mobilenet_v3_large,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.171894183,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4376,regnet_y_32gf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.130310174,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4377,vgg19_bn,adopted from BERT,vit_b_16,0.002624593,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
4378,detection.ssd300_vgg16,Constructs a RetinaNet model with a ResNet-50-FPN backbone,detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
4379,vgg11_bn,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.136256052,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
4380,vgg13_bn,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.198237592,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4381,detection.keypointrcnn_resnet50_fpn,The required minimum input size of the model is 17x17,squeezenet1_1,0.211309237,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4382,convnext_base,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.155344906,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4383,efficientnet_b5,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.143985258,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4384,vit_l_16,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.069090596,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4385,squeezenet1_0,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.181024006,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4386,inception_v3,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.081180702,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4387,segmentation.deeplabv3_mobilenet_v3_large,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.180667537,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4388,video.mc3_18,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.151017458,1x32x32,video classification,Null,TV
4389,resnet18,Note that this model does not include any fine-tuned heads,vit_b_16,0.179882338,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4390,segmentation.deeplabv3_resnet50,"Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded",vit_b_16,0.184969763,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4391,quantization.shufflenet_v2_x1_0,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.182381487,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4392,mnasnet1_0,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.164680101,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
4393,squeezenet1_0,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.073412027,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4394,convnext_base,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.209927167,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4395,shufflenet_v2_x2_0,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.094462746,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
4396,resnet152,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.09256261,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4397,video.r2plus1d_18,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.093422305,1x32x32,video classification,Null,TV
4398,vit_b_16,"A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other",densenet121,0.150471568,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4399,resnext50_32x4d,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.194279522,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4400,efficientnet_b1,ReLU non-linearity as activation functions,vgg11,0.159322643,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
4401,vgg11,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.125393942,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4402,regnet_y_16gf,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_16gf,1.0,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
4403,quantization.shufflenet_v2_x2_0,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.181043455,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
4404,quantization.shufflenet_v2_x0_5,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.157403795,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4405,inception_v3,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.109103001,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4406,efficientnet_b0,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.159022254,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
4407,vgg13_bn,SqueezeNet 1.0 achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters,squeezenet1_0,0.184045635,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4408,efficientnet_b1,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.138424847,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
4409,vgg11,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.202142938,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4410,detection.ssdlite320_mobilenet_v3_large,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.158191731,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4411,regnet_x_8gf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.148615422,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4412,quantization.shufflenet_v2_x1_5,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",quantization.shufflenet_v2_x1_5,1.0,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
4413,vit_l_16,"Each 2-layer block is replaced in the 34-layer net with this 3-layer bottleneck block, resulting in a 50-layer ResNet",resnet50,0.126620315,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4414,convnext_tiny,The number of parameters is 3.4 million,mobilenet_v2,0.072641439,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4415,resnet18,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.090306513,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4416,resnet152,This model has 3.8 billion FLOPs,resnet50,0.098973739,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4417,inception_v3,"By widening Residual Network (ResNet), the network can be shallower with the same accuracy or improved accuracy",wide_resnet50_2,0.165287154,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4418,vgg11,2D max pooling in between the weight layers,vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4419,vit_b_16,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.138340892,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4420,vgg16,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg16,1.0,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
4421,efficientnet_b1,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.156306638,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
4422,resnet152,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.096575974,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4423,vgg11,Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone,segmentation.lraspp_mobilenet_v3_large,0.191768572,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4424,quantization.mobilenet_v2,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.136335823,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
4425,densenet161,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.152273096,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
4426,mnasnet0_5,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.216003979,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4427,vgg13,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.148495978,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4428,quantization.shufflenet_v2_x1_5,Constructs a ShuffleNetV2 with 1.5x output channels,quantization.shufflenet_v2_x1_5,1.0,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
4429,segmentation.fcn_resnet50,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.182063762,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
4430,quantization.shufflenet_v2_x1_0,ReLU non-linearity as activation functions,vgg11,0.163787363,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4431,segmentation.fcn_resnet101,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.010520632,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
4432,quantization.shufflenet_v2_x2_0,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.157957119,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
4433,vit_b_16,They stack residual blocks on top of each other to form network: for example  a ResNet-101 has 101 layers using these blocks,resnet101,0.120195529,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4434,convnext_large,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.059139897,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4435,densenet121,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.183649309,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
4436,efficientnet_b4,The key building block is an Inception Module,inception_v3,0.189746687,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
4437,vit_l_16,Vision Transformer (ViT) model trained using the DINO method,vit_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4438,inception_v3,"Inception v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using Label Smoothing, Factorized 7 x 7 convolutions, and the use of an auxiliary classifier to propagate label information lower down the network (along with the use of batch normalization for layers in the side head)",inception_v3,1.0,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4439,convnext_tiny,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.155040663,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4440,detection.fasterrcnn_resnet50_fpn,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.127586997,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
4441,efficientnet_b6,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.144203208,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
4442,convnext_tiny,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.219342132,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4443,vit_l_16,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.114150217,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4444,quantization.resnet18,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.212971257,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
4445,shufflenet_v2_x1_5,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x1_5,1.0,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4446,regnet_x_8gf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.150680542,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4447,segmentation.deeplabv3_mobilenet_v3_large,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.193849427,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4448,detection.ssdlite320_mobilenet_v3_large,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.120334814,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4449,resnet152,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.185503417,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4450,quantization.shufflenet_v2_x1_0,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.163402259,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4451,regnet_y_800mf,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_800mf,1.0,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
4452,video.r3d_18,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.07566306,1x32x32,video classification,Null,TV
4453,detection.fasterrcnn_mobilenet_v3_large_fpn,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.075280466,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4454,detection.ssd300_vgg16,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.163905736,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
4455,densenet169,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.121147392,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
4456,resnext101_32x8d,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.239647049,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4457,densenet161,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.159231063,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
4458,shufflenet_v2_x1_5,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.207217801,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4459,efficientnet_b2,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.191378863,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4460,regnet_y_1_6gf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.141460121,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
4461,resnext101_32x8d,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.207436442,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4462,detection.retinanet_resnet50_fpn,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.063898877,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
4463,mnasnet1_3,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.189463799,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4464,googlenet,googlenet is used for image classification,googlenet,1.0,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4465,vit_l_32,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.184887062,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4466,vgg13,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.098515381,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4467,mnasnet1_3,"(2+1)D  convolutional block, which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution",video.r2plus1d_18,0.145488809,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4468,convnext_base,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.120490948,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4469,squeezenet1_1,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.100125509,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4470,densenet201,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.107465262,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
4471,video.r3d_18,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.124942082,1x32x32,video classification,Null,TV
4472,convnext_tiny,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.05123667,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4473,segmentation.deeplabv3_resnet50,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.158696044,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4474,regnet_x_1_6gf,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.157929595,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4475,vgg11,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.136975991,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4476,mobilenet_v3_large,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.168783649,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4477,shufflenet_v2_x0_5,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.181077939,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4478,mnasnet1_0,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.137457174,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
4479,resnet152,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.119859136,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4480,convnext_tiny,"This is a model that has been pre-trained on the ImageNet dataset--a dataset that has 100,000+ images across 200 different classes",resnet34,0.142908918,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4481,densenet201,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.194091891,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
4482,shufflenet_v2_x1_5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.203534897,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4483,resnet18,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.192869496,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4484,resnet18,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,1.0,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4485,resnext50_32x4d,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.22686607,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4486,efficientnet_b5,This model has 3.8 billion FLOPs,resnet50,0.1815309,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4487,regnet_y_800mf,Constructs a RegNetY_800MF architecture,regnet_y_800mf,1.0,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
4488,regnet_y_32gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.206736909,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4489,detection.keypointrcnn_resnet50_fpn,Avoiding the use of dense layers means less parameters (making the networks faster to train),segmentation.fcn_resnet50,0.204355986,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4490,vit_b_16,"Compared with ResNet and PolyNet, ResNeXt has 3.03% Top-5 error rate, which is a large relative improvement of about 15%",resnext50_32x4d,0.091099962,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4491,quantization.shufflenet_v2_x2_0,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.149329327,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
4492,inception_v3,"Compared with ResNet and PolyNet, ResNeXt has 3.03% Top-5 error rate, which is a large relative improvement of about 15%",resnext50_32x4d,0.190110892,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4493,shufflenet_v2_x1_0,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.161940343,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4494,regnet_y_800mf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.145914528,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
4495,efficientnet_b5,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.154692877,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4496,quantization.mobilenet_v3_large,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.160567386,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4497,convnext_small,The densenet-161 is much larger at 100MB in size vs the densenet-121 model's roughly 31MB size,densenet161,0.186602674,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4498,vit_l_16,"A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other",densenet121,0.157940281,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4499,efficientnet_b4,adopted from BERT,vit_b_16,0.0,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
4500,quantization.resnext101_32x8d,"It is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",quantization.resnext101_32x8d,1.0,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4501,segmentation.lraspp_mobilenet_v3_large,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.212476707,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4502,shufflenet_v2_x2_0,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.120974246,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
4503,convnext_tiny,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.089624304,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4504,regnet_x_32gf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.17316646,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
4505,segmentation.deeplabv3_resnet101,The required minimum input size of the model is 29x29,densenet121,0.209480262,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4506,densenet121,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.152825747,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
4507,squeezenet1_1,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.13165335,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4508,squeezenet1_1,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.235444789,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4509,segmentation.deeplabv3_mobilenet_v3_large,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_mobilenet_v3_large,1.0,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4510,efficientnet_b2,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.170300007,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4511,detection.fasterrcnn_resnet50_fpn,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.071921273,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
4512,regnet_y_8gf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.161637323,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4513,video.r3d_18,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.06859881,1x32x32,video classification,Null,TV
4514,vgg19_bn,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.107587185,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
4515,convnext_base,EfficientNet-B7 only has 66 million parameters,efficientnet_b7,0.182364897,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4516,shufflenet_v2_x1_0,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,1.0,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4517,shufflenet_v2_x0_5,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.053402063,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4518,googlenet,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.073841251,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4519,regnet_x_8gf,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_8gf,1.0,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4520,alexnet,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.097573104,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
4521,quantization.shufflenet_v2_x1_0,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.129384544,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4522,segmentation.deeplabv3_mobilenet_v3_large,vit_b_16 is used for image classification,vit_b_16,0.135930878,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4523,convnext_base,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.127977534,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4524,convnext_base,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.030215174,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4525,regnet_y_400mf,regnet_y_400mf is used for image classification,regnet_y_400mf,1.0,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
4526,inception_v3,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.112702337,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4527,convnext_large,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.177359312,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4528,regnet_x_3_2gf,"ssd300_vgg16 is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.ssd300_vgg16,0.075339417,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
4529,mobilenet_v3_large,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.114379594,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4530,squeezenet1_0,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.149051436,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4531,efficientnet_b6,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.204511458,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
4532,detection.keypointrcnn_resnet50_fpn,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.181653539,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4533,resnet152,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.177547915,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4534,mnasnet1_3,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.120020919,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4535,vit_b_32,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.10935613,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4536,efficientnet_b5,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.21588831,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4537,mnasnet0_75,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.211899939,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4538,segmentation.deeplabv3_mobilenet_v3_large,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.185469444,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4539,resnet18,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.206769124,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4540,segmentation.deeplabv3_resnet101,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.173733904,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4541,detection.keypointrcnn_resnet50_fpn,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.11443849,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4542,video.r2plus1d_18,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r2plus1d_18,1.0,1x32x32,video classification,Null,TV
4543,vit_l_32,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.127687531,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4544,detection.fasterrcnn_mobilenet_v3_large_fpn,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.059267211,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4545,quantization.shufflenet_v2_x2_0,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.16544314,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
4546,regnet_y_32gf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.142741468,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4547,efficientnet_b7,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.206507166,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
4548,shufflenet_v2_x0_5,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.108878818,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4549,resnet18,The required minimum input size of the model is 21x21,squeezenet1_0,0.177933035,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4550,squeezenet1_1,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.155606254,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4551,detection.keypointrcnn_resnet50_fpn,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.157895962,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4552,mobilenet_v3_large,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,0.185794665,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4553,regnet_x_800mf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.081955027,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4554,vit_l_16,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.124408547,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4555,vgg19,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.197341022,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
4556,regnet_y_8gf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.189619987,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4557,detection.ssdlite320_mobilenet_v3_large,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.161277002,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4558,segmentation.fcn_resnet50,Relative fast due to no fully connected layers or a refinement block,segmentation.fcn_resnet50,1.0,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
4559,vit_b_16,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.12550522,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4560,video.r2plus1d_18,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.148515081,1x32x32,video classification,Null,TV
4561,inception_v3,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.188349508,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4562,regnet_y_8gf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.117094975,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4563,vgg13_bn,Constructs a RegNetX_1.6GF architecture,regnet_x_1_6gf,0.199774951,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4564,shufflenet_v2_x1_0,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.207284493,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4565,quantization.resnext101_32x8d,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.19115715,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4566,quantization.shufflenet_v2_x0_5,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.039737124,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4567,efficientnet_b0,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.23099004,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
4568,segmentation.deeplabv3_mobilenet_v3_large,deeplabv3_mobilenet_v3_large is used for semantic segmentation,segmentation.deeplabv3_mobilenet_v3_large,1.0,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4569,densenet161,Replacing ReLU with GeLU,convnext_tiny,0.083318398,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
4570,mobilenet_v3_large,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,1.0,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4571,quantization.shufflenet_v2_x1_5,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.073440274,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
4572,resnet18,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.03570281,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4573,mnasnet1_0,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.171295577,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
4574,resnet18,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.220739149,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4575,mnasnet0_75,"60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax",alexnet,0.189560125,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4576,vgg13_bn,RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training,detection.retinanet_resnet50_fpn,0.213217462,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4577,vgg19_bn,The required minimum input size of the model is 32x32,vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
4578,quantization.shufflenet_v2_x0_5,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.16143475,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4579,resnet34,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.172554861,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
4580,detection.keypointrcnn_resnet50_fpn,Consider we are solving a Person-Background segmentation problem using Mask-RCNN,detection.keypointrcnn_resnet50_fpn,1.0,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4581,efficientnet_b3,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.133005113,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
4582,resnet18,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.118289266,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4583,resnext101_32x8d,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.233647201,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4584,quantization.resnext101_32x8d,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.198004467,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4585,densenet121,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.178222321,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
4586,vit_l_16,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.11620181,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4587,efficientnet_b1,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.131191871,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
4588,googlenet,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.142916068,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4589,video.r3d_18,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.223088951,1x32x32,video classification,Null,TV
4590,regnet_x_400mf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.134907044,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
4591,shufflenet_v2_x2_0,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.111648042,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
4592,densenet201,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.167108004,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
4593,detection.fasterrcnn_mobilenet_v3_large_fpn,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.07380591,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4594,vgg19,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.168607481,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
4595,efficientnet_b7,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.188587035,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
4596,vit_l_16,Replacing ReLU with GeLU,convnext_tiny,0.128679391,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4597,mobilenet_v2,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,1.0,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
4598,detection.maskrcnn_resnet50_fpn,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.062333106,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
4599,alexnet,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.041570659,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
4600,inception_v3,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.089895813,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4601,resnet34,adopted from BERT,vit_b_16,0.0,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
4602,segmentation.fcn_resnet50,They use option 2 for increasing dimensions,resnet50,0.223634461,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
4603,quantization.shufflenet_v2_x2_0,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.212598866,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
4604,quantization.googlenet,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.136733841,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4605,regnet_x_800mf,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_800mf,1.0,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4606,convnext_base,Note that this model does not include any fine-tuned heads,vit_b_16,0.2163697,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4607,regnet_x_800mf,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.118534427,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4608,mobilenet_v2,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.166298226,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
4609,regnet_x_32gf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.153970215,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
4610,detection.ssdlite320_mobilenet_v3_large,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.148340147,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4611,inception_v3,Constructs a RegNetX_8GF architecture,regnet_x_8gf,0.226813843,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4612,segmentation.lraspp_mobilenet_v3_large,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.229526603,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4613,convnext_base,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.164275979,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4614,efficientnet_b6,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.188694509,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
4615,densenet169,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.141006287,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
4616,efficientnet_b6,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.147671096,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
4617,squeezenet1_1,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.160551329,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4618,regnet_y_3_2gf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.108409243,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
4619,shufflenet_v2_x0_5,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.165641889,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4620,vgg13_bn,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.132590773,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4621,vgg11_bn,MobileNetv3-Large is 27% faster than MobileNetV2 while maintaining similar mAP,mobilenet_v3_large,0.172458095,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
4622,vgg11_bn,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.15975649,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
4623,efficientnet_b0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.184194836,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
4624,vit_b_32,Replacing ReLU with GeLU,convnext_tiny,0.132988751,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4625,resnext101_32x8d,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.090193696,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4626,vgg13,quantization.resnet18 is the quantized model of resnet18,quantization.resnet18,0.214587126,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4627,mobilenet_v3_large,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.168972839,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4628,mobilenet_v3_large,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.135856432,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4629,googlenet,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.117300052,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4630,convnext_large,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.207759346,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4631,wide_resnet50_2,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.175378676,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
4632,regnet_x_3_2gf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.081093376,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
4633,quantization.shufflenet_v2_x1_0,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.130890673,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4634,vit_l_32,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.097562642,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4635,densenet161,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.178791776,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
4636,segmentation.deeplabv3_mobilenet_v3_large,RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training,detection.retinanet_resnet50_fpn,0.175624542,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4637,regnet_x_1_6gf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.140120479,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4638,quantization.mobilenet_v2,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.177811487,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
4639,quantization.shufflenet_v2_x0_5,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.211708382,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4640,mnasnet0_5,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.06009949,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4641,detection.retinanet_resnet50_fpn,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.092123542,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
4642,vit_l_16,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.125437858,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4643,regnet_x_32gf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.07791602,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
4644,resnet101,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.144327819,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
4645,wide_resnet50_2,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.1791361,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
4646,quantization.mobilenet_v3_large,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.164804321,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4647,efficientnet_b5,ReLU non-linearity as activation functions,vgg11,0.198547754,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4648,convnext_large,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.211532259,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4649,shufflenet_v2_x1_0,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x1_0,1.0,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4650,convnext_base,DenseNet-121 has 120 Convolutions and 4 AvgPool,densenet121,0.213919284,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4651,regnet_x_400mf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.0818122,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
4652,quantization.mobilenet_v2,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.085609619,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
4653,video.r2plus1d_18,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.224355455,1x32x32,video classification,Null,TV
4654,vgg13,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.135434177,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4655,convnext_large,2D max pooling in between the weight layers,vgg11,0.135628553,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4656,squeezenet1_0,The required minimum input size of the model is 21x21,squeezenet1_0,1.0,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4657,regnet_y_800mf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_800mf,1.0,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
4658,regnet_y_3_2gf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.178119493,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
4659,convnext_tiny,Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks,wide_resnet50_2,0.185716601,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4660,efficientnet_b7,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.145813409,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
4661,regnet_x_32gf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.16364056,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
4662,efficientnet_b5,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.136871883,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4663,mnasnet0_75,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.221229965,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4664,segmentation.lraspp_mobilenet_v3_large,"A ConvNet which has the design simplicity of Convolutional Neural Networks while having better accuracy, performance and scalability than Vision Transformers.",convnext_small,0.193125841,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4665,squeezenet1_1,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.165859206,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4666,resnet50,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.098508442,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
4667,video.r3d_18,The overall structure of 3D ResNet with 4 residual groups,video.r3d_18,1.0,1x32x32,video classification,Null,TV
4668,convnext_base,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.139486336,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4669,vgg11,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.173368431,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4670,segmentation.deeplabv3_resnet101,"Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded",vit_b_16,0.185922346,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4671,segmentation.deeplabv3_mobilenet_v3_large,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.174774254,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4672,video.r3d_18,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.225663479,1x32x32,video classification,Null,TV
4673,quantization.mobilenet_v3_large,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.162293399,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4674,vgg16,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.215565166,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
4675,convnext_base,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.139535063,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4676,regnet_x_16gf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.172394501,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
4677,mnasnet0_5,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.062834863,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4678,convnext_tiny,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.106706274,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4679,segmentation.fcn_resnet50,Note that this model does not include any fine-tuned heads,vit_b_16,0.181833005,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
4680,quantization.shufflenet_v2_x1_5,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.133278468,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
4681,video.r3d_18,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.058935083,1x32x32,video classification,Null,TV
4682,googlenet,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.115016249,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4683,mobilenet_v3_small,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.202408815,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
4684,mnasnet0_75,The required minimum input size of the model is 21x21,squeezenet1_0,0.213622044,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4685,detection.fasterrcnn_mobilenet_v3_large_fpn,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.070788877,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4686,quantization.googlenet,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.039102599,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4687,vit_l_16,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.115139619,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4688,quantization.mobilenet_v2,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.191115455,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
4689,quantization.mobilenet_v3_large,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.147849477,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4690,quantization.googlenet,"Inception network of googlenet stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid",quantization.googlenet,1.0,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4691,quantization.resnet50,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.122401179,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
4692,segmentation.deeplabv3_resnet101,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.107537324,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4693,convnext_small,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.103852294,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4694,regnet_y_400mf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.180429545,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
4695,segmentation.deeplabv3_resnet50,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.160732862,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4696,mobilenet_v3_small,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.124833742,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
4697,convnext_base,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.231616788,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4698,shufflenet_v2_x0_5,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.16032299,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4699,vgg11_bn,ReLU non-linearity as activation functions,vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
4700,vgg13_bn,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.168012201,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4701,vit_b_16,based on ResNeXt-50 as its backbone along with the feature Pyramid Network (FPN) for better accuracy and speed,detection.maskrcnn_resnet50_fpn,0.184349869,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4702,regnet_y_3_2gf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.135977263,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
4703,quantization.shufflenet_v2_x1_5,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.131672448,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
4704,convnext_base,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.140882617,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4705,googlenet,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.084776998,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4706,detection.fasterrcnn_mobilenet_v3_large_fpn,The RPN implements the terminology of neural network with attention to tell the object detection (Fast R-CNN) where to look,detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4707,densenet121,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.14776732,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
4708,densenet169,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.192762801,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
4709,segmentation.fcn_resnet101,The original dataset contained images of size 512x512x1,resnet101,0.088792956,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
4710,mnasnet0_5,The required minimum input size of the model is 29x29,densenet121,0.164059083,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4711,resnext101_32x8d,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.140735632,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4712,vit_b_32,Constructs a Mask R-CNN model with a ResNet-50-FPN backbone,detection.maskrcnn_resnet50_fpn,0.192874495,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4713,regnet_y_3_2gf,The required minimum input size of the model is 17x17,squeezenet1_1,0.190543053,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
4714,mnasnet1_0,Replacing ReLU with GeLU,convnext_tiny,0.122222071,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
4715,mnasnet0_5,Replacing ReLU with GeLU,convnext_tiny,0.075657292,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4716,convnext_large,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.207445866,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4717,vgg19_bn,vgg19_bn is used for image classification,vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
4718,video.mc3_18,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.241748063,1x32x32,video classification,Null,TV
4719,vgg19,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.164684397,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
4720,vit_b_32,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.137043901,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4721,segmentation.lraspp_mobilenet_v3_large,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.115878764,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4722,detection.fasterrcnn_mobilenet_v3_large_320_fpn,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.083154871,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
4723,detection.ssd300_vgg16,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.091801043,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
4724,convnext_large,MobileNet v3-Small has 2.5 million parameters,mobilenet_v3_small,0.158246639,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4725,quantization.googlenet,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.148559902,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4726,resnext50_32x4d,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.125896018,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4727,mnasnet0_75,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.148266512,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4728,segmentation.deeplabv3_mobilenet_v3_large,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.123736994,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4729,detection.fasterrcnn_mobilenet_v3_large_fpn,Note that this model does not include any fine-tuned heads,vit_b_16,0.179059267,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4730,densenet161,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.140486789,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
4731,mobilenet_v3_large,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.151580134,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4732,resnext50_32x4d,"Mask R-CNN uses anchor boxes to detect multiple objects, objects of different scales, and overlapping objects in an image",detection.maskrcnn_resnet50_fpn,0.187982714,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4733,detection.fasterrcnn_mobilenet_v3_large_fpn,The required minimum input size of the model is 21x21,squeezenet1_0,0.17852796,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4734,mnasnet1_0,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.189616221,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
4735,squeezenet1_0,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.159094659,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4736,regnet_y_800mf,2D max pooling in between the weight layers,vgg11,0.157218029,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
4737,wide_resnet101_2,This is achieved through the use of wide residual blocks,wide_resnet101_2,1.0,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
4738,regnet_x_16gf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.1622441,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
4739,vgg16_bn,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.172027884,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
4740,detection.fasterrcnn_mobilenet_v3_large_320_fpn,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.120632913,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
4741,detection.fasterrcnn_mobilenet_v3_large_fpn,VGG19 has 19.6 billion FLOPs,vgg19,0.155158756,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4742,efficientnet_b3,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.098536156,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
4743,regnet_x_400mf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.1684326,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
4744,quantization.resnet50,They stack residual blocks on top of each other to form network: for example  a ResNet-50 has fifty layers using these blocks,quantization.resnet50,1.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
4745,convnext_base,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.112783032,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4746,mobilenet_v3_large,11 weight layers (convolutional + fully connected),vgg11,0.190768373,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4747,quantization.mobilenet_v3_large,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.21882987,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4748,convnext_tiny,Constructs an SSDlite model with input size 320x320 and a MobileNetV3 Large backbone,detection.ssdlite320_mobilenet_v3_large,0.101717805,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4749,mnasnet0_75,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.21306292,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4750,shufflenet_v2_x1_5,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.12401413,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4751,googlenet,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.191886301,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4752,alexnet,MobileNet v3-Small has 2.5 million parameters,mobilenet_v3_small,0.186712203,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
4753,efficientnet_b2,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.168285081,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4754,convnext_large,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.200126292,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4755,convnext_large,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.174066494,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4756,mnasnet0_5,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.159174343,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4757,mobilenet_v2,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.142202203,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
4758,mnasnet0_5,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.070376301,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4759,quantization.mobilenet_v3_large,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",quantization.mobilenet_v3_large,1.0,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4760,regnet_y_8gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.218747374,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4761,mnasnet0_75,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.149492666,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4762,regnet_y_32gf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.150620397,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4763,mnasnet0_5,The squeezing (bottleneck layer) and expansion behavior is common in neural architectures,squeezenet1_1,0.249905636,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4764,convnext_large,They stack residual blocks on top of each other to form network: for example  a ResNet-101 has 101 layers using these blocks,resnet101,0.171339518,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4765,convnext_small,The densenet-169 is larger at just about 55MB in size vs the densenet-121 model's roughly 31MB size,densenet169,0.164659615,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4766,efficientnet_b2,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.148256353,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4767,vgg13_bn,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.129474991,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4768,regnet_y_8gf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.178208622,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4769,regnet_y_32gf,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.144890118,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4770,video.r2plus1d_18,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.128491337,1x32x32,video classification,Null,TV
4771,vit_l_32,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet18,0.171615551,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4772,resnext50_32x4d,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.242160115,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4773,inception_v3,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.136103343,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4774,quantization.shufflenet_v2_x1_0,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.115280147,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4775,detection.maskrcnn_resnet50_fpn,The number of parameters is 3.4 million,mobilenet_v2,0.062656878,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
4776,efficientnet_b1,Replacing ReLU with GeLU,convnext_tiny,0.092624941,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
4777,regnet_x_16gf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.151322965,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
4778,squeezenet1_1,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.156636994,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4779,regnet_x_800mf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.082241475,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4780,inception_v3,"The number of channels in outer 1x1 convolutions is the same, Wide ResNet-50-2 has 2048-1024-2048 channels",wide_resnet50_2,0.101083294,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4781,inception_v3,SqueezeNet 1.0 achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters,squeezenet1_0,0.232821785,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4782,vit_l_32,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.185580626,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4783,regnet_y_3_2gf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.111365149,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
4784,convnext_tiny,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.138380855,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4785,convnext_small,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.071437814,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4786,wide_resnet50_2,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.236970109,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
4787,regnet_x_1_6gf,ReLU non-linearity as activation functions,vgg11,0.165528816,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4788,quantization.resnet50,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.176745773,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
4789,detection.fasterrcnn_mobilenet_v3_large_fpn,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.174325599,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4790,regnet_y_32gf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_32gf,1.0,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4791,segmentation.fcn_resnet50,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,1.0,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
4792,quantization.shufflenet_v2_x2_0,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.181509938,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
4793,vit_l_32,The network design includes the use of a hard swish activation and squeeze-and-excitation modules in the MBConv blocks,mobilenet_v3_small,0.167242534,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4794,shufflenet_v2_x1_0,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.141213167,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4795,quantization.inception_v3,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.150770576,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4796,squeezenet1_0,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.22791994,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4797,efficientnet_b2,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.136145443,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4798,efficientnet_b2,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.164104652,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4799,segmentation.fcn_resnet50,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.042580302,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
4800,detection.fasterrcnn_mobilenet_v3_large_320_fpn,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.108802036,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
4801,resnext50_32x4d,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.225068867,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4802,quantization.inception_v3,This network is a pretty large network and it has about 138 million parameters,vgg16,0.134642954,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4803,squeezenet1_0,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.109323476,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
4804,regnet_x_16gf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.159410573,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
4805,vgg16,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.152349152,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
4806,vit_l_32,"Each layer from the FPN is passed to the subnets, enabling it to detect objects as various scales",detection.retinanet_resnet50_fpn,0.211775498,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4807,vgg11,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.130278846,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4808,quantization.resnext101_32x8d,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.176767971,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4809,detection.fasterrcnn_mobilenet_v3_large_fpn,The original dataset contained images of size 512x512x1,resnet101,0.160425091,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
4810,efficientnet_b5,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.221562922,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4811,segmentation.deeplabv3_resnet50,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.189903003,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4812,video.r3d_18,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.102323744,1x32x32,video classification,Null,TV
4813,quantization.mobilenet_v3_large,217 multiply adds 5400000 parameters and top accuracy is 75.6,quantization.mobilenet_v3_large,1.0,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
4814,quantization.shufflenet_v2_x0_5,ReLU non-linearity as activation functions,vgg11,0.164430163,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4815,detection.keypointrcnn_resnet50_fpn,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.119125692,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4816,regnet_x_8gf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.174350742,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4817,mobilenet_v3_small,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.126914969,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
4818,detection.maskrcnn_resnet50_fpn,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.075802145,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
4819,segmentation.lraspp_mobilenet_v3_large,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.104765569,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4820,vgg19,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.200597185,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
4821,mobilenet_v2,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.161337915,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
4822,mnasnet1_3,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,0.230426704,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4823,convnext_base,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.167773134,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4824,vit_l_16,Proposing region proposal network (RPN) which is a fully convolutional network that generates proposals with various scales and aspect ratios,detection.fasterrcnn_resnet50_fpn,0.146201339,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4825,shufflenet_v2_x2_0,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,1.0,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
4826,segmentation.fcn_resnet101,Avoiding the use of dense layers means less parameters (making the networks faster to train),segmentation.fcn_resnet101,1.0,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
4827,quantization.shufflenet_v2_x0_5,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.133171958,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4828,shufflenet_v2_x0_5,It is the simplest of all other vgg versions,vgg11,0.170530556,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4829,quantization.resnext101_32x8d,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.180765102,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4830,quantization.shufflenet_v2_x1_0,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.098008112,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4831,vit_l_32,They stack residual blocks on top of each other to form network: for example  a ResNet-18 has 18 layers using these blocks,resnet18,0.144002472,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4832,vgg11_bn,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.188032384,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
4833,vgg16_bn,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.197698835,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
4834,resnext50_32x4d,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.153808578,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4835,segmentation.fcn_resnet50,vit_b_32 is used for image classification,vit_b_32,0.128462579,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
4836,quantization.shufflenet_v2_x2_0,The number of parameters is 3.4 million,mobilenet_v2,0.110665464,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
4837,quantization.inception_v3,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.174795054,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4838,regnet_y_800mf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.170600697,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
4839,detection.fasterrcnn_mobilenet_v3_large_320_fpn,The RPN implements the terminology of neural network with attention to tell the object detection (Fast R-CNN) where to look,detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
4840,quantization.mobilenet_v2,The key building block is an Inception Module,inception_v3,0.168848669,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
4841,resnext101_32x8d,"Compared with ResNet and PolyNet, ResNeXt has 3.03% Top-5 error rate, which is a large relative improvement of about 15%",resnext101_32x8d,1.0,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4842,efficientnet_b7,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.141800227,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
4843,densenet121,"Dense blocks, Batch Normalization(BN)",densenet121,1.0,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
4844,regnet_y_8gf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.180878648,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4845,inception_v3,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.203495519,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4846,quantization.inception_v3,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.15135722,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4847,vgg11,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.061725582,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4848,vgg13,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.144748457,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4849,regnet_x_1_6gf,2D max pooling in between the weight layers,vgg11,0.155294041,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4850,densenet201,"Dense blocks, Batch Normalization(BN)",densenet201,1.0,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
4851,convnext_small,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.189048201,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4852,segmentation.deeplabv3_resnet50,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.192445448,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4853,mobilenet_v3_large,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.112596117,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4854,regnet_x_800mf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.164301061,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4855,vit_l_16,Constructs a DeepLabV3 model with a ResNet-101 backbone,segmentation.deeplabv3_resnet101,0.146474455,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
4856,resnet152,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.120072659,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
4857,shufflenet_v2_x1_5,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x1_5,1.0,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4858,regnet_x_16gf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.160440079,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
4859,vgg19_bn,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.148137836,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
4860,mnasnet1_3,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.139892129,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
4861,quantization.mobilenet_v2,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.161580011,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
4862,resnet34,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.154056662,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
4863,inception_v3,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.151998884,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4864,densenet161,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.214770248,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
4865,regnet_y_1_6gf,It is the simplest of all other vgg versions,vgg11,0.190184566,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
4866,vgg19_bn,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
4867,vgg13_bn,Constructs a RegNetY_3.2GF architecture,regnet_y_3_2gf,0.192144617,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4868,regnet_x_400mf,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.170266242,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
4869,segmentation.deeplabv3_resnet101,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet101,1.0,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4870,quantization.shufflenet_v2_x0_5,Replacing ReLU with GeLU,convnext_tiny,0.092006176,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
4871,shufflenet_v2_x1_5,The required minimum input size of the model is 21x21,squeezenet1_0,0.183208123,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4872,regnet_y_3_2gf,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.138924573,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
4873,quantization.inception_v3,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.086006713,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4874,mnasnet1_0,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.13312209,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
4875,mnasnet0_5,ReLU non-linearity as activation functions,vgg11,0.085813969,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4876,segmentation.lraspp_mobilenet_v3_large,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.165958282,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4877,regnet_y_8gf,ReLU non-linearity as activation functions,vgg11,0.176414252,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4878,regnet_y_400mf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.147171544,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
4879,vit_b_32,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.141679955,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4880,quantization.mobilenet_v2,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.167630302,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
4881,efficientnet_b5,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.14639624,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
4882,efficientnet_b4,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.149971467,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
4883,shufflenet_v2_x1_0,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.151789816,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4884,detection.ssdlite320_mobilenet_v3_large,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.199263203,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4885,vit_b_32,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.119278714,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4886,segmentation.fcn_resnet50,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.250241929,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
4887,regnet_y_16gf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.183205044,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
4888,video.r3d_18,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.170583485,1x32x32,video classification,Null,TV
4889,squeezenet1_1,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.195131071,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
4890,quantization.mobilenet_v2,You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close,vit_b_32,0.14055007,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
4891,mnasnet0_75,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.062592023,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4892,vit_l_32,"It builds upon ShuffleNet v1, which utilised pointwise group convolutions, bottleneck-like structures, and a channel shuffle operation",shufflenet_v2_x0_5,0.180589663,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4893,quantization.googlenet,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.131095652,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
4894,vgg19,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.111269115,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
4895,quantization.inception_v3,"The loss function of Faster R-CNN is updated with the mask loss and as in FPN, it uses 5 anchor boxes with 3 aspect ratio",detection.maskrcnn_resnet50_fpn,0.205485119,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4896,regnet_x_1_6gf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.086812931,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4897,shufflenet_v2_x1_5,Replacing ReLU with GeLU,convnext_tiny,0.098996553,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4898,densenet161,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.104138084,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
4899,vit_l_32,"Compared with ResNet and PolyNet, ResNeXt has 3.03% Top-5 error rate, which is a large relative improvement of about 15%",resnext50_32x4d,0.112253177,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4900,quantization.shufflenet_v2_x2_0,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.183216097,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
4901,convnext_base,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.097320316,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4902,vgg13_bn,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.141223331,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4903,quantization.inception_v3,It follows this arrangement of convolution and max pool layers consistently throughout the whole architecture,vgg16,0.224936888,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4904,regnet_x_8gf,adopted from BERT,vit_b_16,0.013895141,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4905,vgg16,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.138536837,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
4906,resnext50_32x4d,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.10797521,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4907,regnet_x_32gf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.187551702,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
4908,regnet_y_16gf,ReLU non-linearity as activation functions,vgg11,0.169095536,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
4909,vgg13,vgg13 is used for image classification,vgg13,1.0,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4910,segmentation.deeplabv3_mobilenet_v3_large,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.172244163,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
4911,regnet_y_32gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.179238803,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4912,detection.keypointrcnn_resnet50_fpn,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.185843713,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4913,quantization.inception_v3,"60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax",alexnet,0.182089245,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4914,vgg19_bn,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.150880308,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
4915,quantization.inception_v3,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.106242828,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4916,vgg13,Constructs a RegNetX_400MF architecture,regnet_x_400mf,0.232095391,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4917,inception_v3,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.131702234,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4918,vit_b_32,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.143360283,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4919,regnet_y_8gf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.157789572,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
4920,regnet_x_32gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.202181994,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
4921,mnasnet0_5,"60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax",alexnet,0.2025777,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4922,densenet201,"A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other",densenet201,1.0,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
4923,regnet_y_16gf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.180558948,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
4924,resnet18,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.183006869,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
4925,convnext_tiny,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.148762097,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4926,resnext101_32x8d,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.185317671,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4927,vgg13_bn,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.151523421,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
4928,alexnet,VGG19 has 19.6 billion FLOPs,vgg19,0.042166287,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
4929,resnext101_32x8d,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.218278345,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4930,vgg11,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.146864328,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4931,shufflenet_v2_x0_5,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.104421058,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
4932,mobilenet_v3_large,"(2+1)D  convolutional block, which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution",video.r2plus1d_18,0.156049484,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
4933,segmentation.deeplabv3_resnet50,deeplabv3_resnet50 is used for semantic segmentation,segmentation.deeplabv3_resnet50,1.0,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
4934,mnasnet0_5,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.148776874,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4935,vgg13,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.194862451,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
4936,regnet_x_1_6gf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_x_1_6gf,1.0,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4937,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.1319195,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
4938,regnet_y_16gf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.165869106,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
4939,regnet_x_8gf,The required minimum input size of the model is 21x21,squeezenet1_0,0.218837521,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4940,googlenet,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.208076504,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4941,densenet169,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.184884544,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
4942,vit_b_32,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.134722551,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4943,efficientnet_b7,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.100760628,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
4944,detection.keypointrcnn_resnet50_fpn,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.143452471,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4945,regnet_y_1_6gf,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.164270788,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
4946,regnet_x_32gf,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.159025262,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
4947,shufflenet_v2_x1_0,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.170490046,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
4948,quantization.resnext101_32x8d,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.17988663,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
4949,inception_v3,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.20431454,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4950,regnet_x_1_6gf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.166853594,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4951,vit_l_32,This network is a pretty large network and it has about 138 million parameters,vgg16,0.109792098,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4952,densenet201,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.16330825,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
4953,resnext50_32x4d,You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close,vit_b_32,0.153964654,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
4954,vit_b_16,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.138660842,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4955,quantization.inception_v3,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",quantization.inception_v3,1.0,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
4956,googlenet,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.094104424,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4957,mnasnet0_5,They stack residual blocks ontop of each other to form network,vgg19_bn,0.240869741,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4958,regnet_x_800mf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.17367464,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
4959,mobilenet_v3_small,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.175331775,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
4960,regnet_y_1_6gf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.153113618,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
4961,vgg19,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
4962,segmentation.deeplabv3_resnet101,"Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384",vit_l_32,0.16245672,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4963,shufflenet_v2_x1_5,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.107903789,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
4964,segmentation.lraspp_mobilenet_v3_large,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.229526603,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
4965,convnext_tiny,They stack residual blocks ontop of each other to form network: for example  a ResNet-34 has 34 layers using these blocks,resnet34,0.1597606,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
4966,efficientnet_b2,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.244499299,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
4967,shufflenet_v2_x2_0,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.155648912,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
4968,detection.ssdlite320_mobilenet_v3_large,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384",vit_l_32,0.192980804,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
4969,detection.fasterrcnn_resnet50_fpn,The required minimum input size of the model is 17x17,squeezenet1_1,0.200536129,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
4970,detection.ssd300_vgg16,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
4971,convnext_small,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.156986433,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
4972,vgg16_bn,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16_bn,1.0,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
4973,regnet_y_1_6gf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.180346499,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
4974,regnet_x_8gf,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.155751917,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
4975,regnet_x_1_6gf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.097673746,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
4976,regnet_y_32gf,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.207125208,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
4977,detection.maskrcnn_resnet50_fpn,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.062113449,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
4978,vgg11,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_400mf,0.179269158,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
4979,resnext101_32x8d,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.198336303,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
4980,googlenet,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.042180211,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
4981,regnet_y_400mf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.171512249,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
4982,vit_l_32,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.164033108,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
4983,detection.keypointrcnn_resnet50_fpn,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.242540766,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
4984,video.r3d_18,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.204774039,1x32x32,video classification,Null,TV
4985,segmentation.deeplabv3_resnet101,The dense prediction is achieved by simply up-sampling the output of the last convolution layer and computing pixel-wise loss,segmentation.deeplabv3_resnet101,1.0,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4986,efficientnet_b4,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.160652357,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
4987,quantization.shufflenet_v2_x1_0,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.149038948,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
4988,wide_resnet101_2,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.147114536,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
4989,vit_b_32,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.170646149,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
4990,convnext_large,Constructs a DeepLabV3 model with a ResNet-101 backbone,segmentation.deeplabv3_resnet101,0.213908919,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
4991,vit_b_16,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.13115828,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
4992,segmentation.deeplabv3_resnet101,The number of parameters is 3.4 million,mobilenet_v2,0.116265251,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
4993,mnasnet0_75,"It has 11 weight layers in total, so the name VGG11",vgg11,0.236054967,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4994,shufflenet_v2_x2_0,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x2_0,1.0,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
4995,mnasnet0_75,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.197254931,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
4996,mnasnet0_5,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.088911824,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
4997,inception_v3,inception_v3 is used for image classification,inception_v3,1.0,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
4998,regnet_x_3_2gf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.156023478,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
4999,quantization.resnext101_32x8d,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.171472122,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5000,quantization.mobilenet_v3_large,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.175301068,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5001,densenet201,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.242934378,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5002,shufflenet_v2_x2_0,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.103552842,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
5003,efficientnet_b4,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.130201131,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
5004,quantization.shufflenet_v2_x2_0,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.133456206,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5005,segmentation.deeplabv3_resnet50,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.130584501,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5006,resnext101_32x8d,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.175342474,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5007,regnet_y_32gf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.163737462,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
5008,mnasnet1_3,MNASNet with depth multiplier of 1.3,mnasnet1_3,1.0,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5009,resnet50,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.171922778,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
5010,squeezenet1_1,Proposing region proposal network (RPN) which is a fully convolutional network that generates proposals with various scales and aspect ratios,detection.fasterrcnn_resnet50_fpn,0.197388716,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5011,detection.ssdlite320_mobilenet_v3_large,"It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block",googlenet,0.197966071,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5012,efficientnet_b0,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.168187972,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
5013,vit_l_16,"By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder",vit_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5014,segmentation.lraspp_mobilenet_v3_large,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.21575,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5015,resnext101_32x8d,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.192712633,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5016,vgg13_bn,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.108614462,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5017,convnext_tiny,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.166793415,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5018,quantization.shufflenet_v2_x0_5,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.120704774,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5019,densenet201,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.179161967,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5020,video.r3d_18,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.133991563,1x32x32,video classification,Null,TV
5021,quantization.resnext101_32x8d,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.131714794,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5022,segmentation.lraspp_mobilenet_v3_large,There are 4 convolutional layers in each module (excluding the 1x1 convolutional layer),resnet18,0.189716554,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5023,regnet_x_800mf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.184427129,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
5024,detection.maskrcnn_resnet50_fpn,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.185689771,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
5025,alexnet,MobileNet v3-Large has 5.4 million parameters,mobilenet_v3_large,0.203309538,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5026,mobilenet_v3_small,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.141516148,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
5027,detection.ssd300_vgg16,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.104932776,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
5028,efficientnet_b6,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.154322855,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
5029,quantization.inception_v3,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.115011546,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5030,segmentation.deeplabv3_resnet101,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.090195265,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
5031,quantization.mobilenet_v3_large,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.192316503,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5032,quantization.resnet50,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.155993186,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
5033,mnasnet0_75,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.076275955,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5034,segmentation.fcn_resnet50,The required minimum input size of the model is 17x17,squeezenet1_1,0.188532536,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
5035,segmentation.deeplabv3_resnet101,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.173984335,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
5036,inception_v3,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.162524478,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5037,quantization.shufflenet_v2_x1_5,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.164249542,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5038,googlenet,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.259362362,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5039,convnext_base,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.115359015,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5040,mnasnet0_5,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.196099594,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5041,densenet201,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.203354834,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5042,inception_v3,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.123820995,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5043,shufflenet_v2_x2_0,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.141560748,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
5044,regnet_y_3_2gf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.176501843,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5045,vgg16_bn,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.165951183,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
5046,mnasnet0_5,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.03163783,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5047,regnet_y_400mf,ReLU non-linearity as activation functions,vgg11,0.167384864,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5048,vit_b_16,EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient,efficientnet_b1,0.179831825,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5049,quantization.shufflenet_v2_x0_5,VGG19 has 19.6 billion FLOPs,vgg19,0.114519829,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5050,convnext_base,11 weight layers (convolutional + fully connected),vgg11,0.187010049,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5051,resnet101,VGG19 has 19.6 billion FLOPs,vgg19,0.10206589,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5052,shufflenet_v2_x0_5,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.125611635,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5053,mnasnet0_75,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.198990504,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5054,shufflenet_v2_x1_0,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x1_0,1.0,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5055,quantization.resnext101_32x8d,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.resnext101_32x8d,1.0,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5056,quantization.inception_v3,VGG19 has 19.6 billion FLOPs,vgg19,0.106437272,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5057,video.r3d_18,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.203090989,1x32x32,video classification,Null,TV
5058,vit_b_32,adopted from BERT,vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5059,resnet152,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.104024781,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5060,regnet_x_32gf,"The RegNet is composed of a stem, body and head",regnet_x_32gf,1.0,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5061,quantization.resnext101_32x8d,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.150678914,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5062,convnext_large,The required minimum input size of the model is 17x17,squeezenet1_1,0.159543369,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5063,detection.maskrcnn_resnet50_fpn,"Mask R-CNN performed better than the existing state of the art single-model architectures, added an extra functionality of instance segmentation with little overhead computations",detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
5064,quantization.shufflenet_v2_x1_5,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.135305395,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5065,mnasnet1_3,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.102063314,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5066,mnasnet1_3,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.123095346,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5067,quantization.inception_v3,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.081894378,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5068,convnext_tiny,"Inception network of googlenet stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid",googlenet,0.156635771,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5069,mobilenet_v3_small,adopted from BERT,vit_b_16,0.0,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
5070,vgg11,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.18185102,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
5071,mnasnet0_75,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.163901359,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5072,regnet_y_3_2gf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.114950398,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5073,detection.fasterrcnn_resnet50_fpn,Note that this model does not include any fine-tuned heads,vit_b_16,0.17865622,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
5074,detection.ssd300_vgg16,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.122652584,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
5075,quantization.shufflenet_v2_x1_5,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.11139979,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5076,mnasnet0_75,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.204498773,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5077,convnext_large,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x0_5,0.198092224,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5078,resnet101,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.104347629,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5079,efficientnet_b1,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.222118121,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
5080,segmentation.deeplabv3_resnet50,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.135105379,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5081,shufflenet_v2_x2_0,Replacing ReLU with GeLU,convnext_tiny,0.095283057,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
5082,regnet_y_8gf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.197595155,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
5083,regnet_x_8gf,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.208175661,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5084,vit_l_16,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.133824382,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5085,squeezenet1_1,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.163408474,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5086,convnext_large,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.098773835,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5087,segmentation.deeplabv3_mobilenet_v3_large,The required minimum input size of the model is 21x21,squeezenet1_0,0.170853673,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5088,detection.keypointrcnn_resnet50_fpn,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.056833504,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5089,shufflenet_v2_x1_0,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.12052363,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5090,mnasnet1_3,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.181087181,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5091,quantization.shufflenet_v2_x1_5,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.228078365,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5092,convnext_base,The required minimum input size of the model is 21x21,squeezenet1_0,0.154494595,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5093,densenet161,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.111616823,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
5094,resnext50_32x4d,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.128643701,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5095,mobilenet_v2,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.10445058,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
5096,regnet_x_8gf,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.221059782,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5097,vgg13_bn,The required minimum input size of the model is 32x32,vgg13_bn,1.0,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5098,squeezenet1_1,"It has 11 weight layers in total, so the name VGG11",vgg11,0.199437217,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5099,efficientnet_b5,VGG19 has 19.6 billion FLOPs,vgg19,0.101825772,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
5100,quantization.googlenet,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.180157582,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5101,inception_v3,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.095164896,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5102,shufflenet_v2_x1_5,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.182221331,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5103,efficientnet_b4,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.200809576,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
5104,regnet_x_8gf,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.117194485,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5105,convnext_tiny,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.132091649,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5106,convnext_large,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.169355647,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5107,detection.keypointrcnn_resnet50_fpn,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.199921835,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5108,resnet101,Replacing ReLU with GeLU,convnext_tiny,0.170347124,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5109,mobilenet_v3_large,Constructor for 18 layer Mixed Convolution network,video.r3d_18,0.217432138,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
5110,regnet_y_1_6gf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.091267186,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5111,densenet169,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.043355031,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5112,detection.maskrcnn_resnet50_fpn,MobileNet v3-Large has 5.4 million parameters,mobilenet_v3_large,0.167457178,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
5113,resnet18,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.179938469,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5114,regnet_x_3_2gf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.131560945,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5115,convnext_large,This model has 3.8 billion FLOPs,resnet50,0.116824106,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5116,vgg13_bn,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.146737205,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5117,vgg16_bn,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.148027418,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
5118,convnext_small,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.146461065,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5119,resnext50_32x4d,"Inception v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using Label Smoothing, Factorized 7 x 7 convolutions, and the use of an auxiliary classifier to propagate label information lower down the network (along with the use of batch normalization for layers in the side head)",inception_v3,0.241643529,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5120,resnet152,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.115224635,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5121,convnext_tiny,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.059218368,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5122,segmentation.deeplabv3_resnet50,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.155569463,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5123,segmentation.lraspp_mobilenet_v3_large,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.22701641,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5124,regnet_y_8gf,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.172131852,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
5125,quantization.resnext101_32x8d,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.15036875,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5126,detection.maskrcnn_resnet50_fpn,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.141867463,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
5127,vit_l_32,"Using the DenseNet-121 architecture to understand the table, we can see that every dense block has varying number of layers (repetitions) featuring two convolutions each; a 1x1 sized kernel as the bottleneck layer and 3x3 kernel to perform the convolution operation",densenet121,0.168781955,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5128,quantization.googlenet,GoogLeNet is a type of convolutional neural network based on the Inception architecture,quantization.googlenet,1.0,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5129,regnet_x_400mf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.185156352,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
5130,resnet152,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.141238099,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5131,resnext50_32x4d,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.143668925,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5132,regnet_y_3_2gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.172537529,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5133,segmentation.deeplabv3_mobilenet_v3_large,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.197160034,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5134,shufflenet_v2_x1_5,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.103001749,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5135,regnet_x_16gf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.187303298,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5136,quantization.shufflenet_v2_x1_0,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.150911764,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5137,vgg13_bn,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.162383255,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5138,vgg13_bn,quantization.resnet18 is the quantized model of resnet18,quantization.resnet18,0.180932971,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5139,vgg11,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.074518787,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
5140,googlenet,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.263722017,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5141,mnasnet0_5,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.10919722,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5142,detection.fasterrcnn_resnet50_fpn,adopted from BERT,vit_b_16,0.003308228,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
5143,quantization.resnet18,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.096867683,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
5144,vit_l_16,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,0.159308848,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5145,convnext_large,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.228878017,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5146,segmentation.deeplabv3_resnet50,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.171422664,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5147,efficientnet_b5,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.154700438,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
5148,squeezenet1_1,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.117375587,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5149,shufflenet_v2_x0_5,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.188007469,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5150,vgg11,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.187525519,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
5151,shufflenet_v2_x0_5,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.232620008,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5152,segmentation.deeplabv3_resnet101,Replacing ReLU with GeLU,convnext_tiny,0.172238941,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
5153,quantization.shufflenet_v2_x0_5,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.13511851,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5154,vit_b_16,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.136177229,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5155,convnext_base,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.042492383,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5156,vgg13,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,0.186341848,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5157,segmentation.deeplabv3_mobilenet_v3_large,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.107401174,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5158,convnext_large,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.131651755,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5159,segmentation.deeplabv3_mobilenet_v3_large,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.169120665,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5160,inception_v3,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.216377785,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5161,squeezenet1_1,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.088825321,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5162,vit_l_16,This network is a pretty large network and it has about 138 million parameters,vgg16,0.091360203,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5163,squeezenet1_1,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.110581954,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5164,densenet121,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.206819276,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
5165,vgg13,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.169210199,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5166,efficientnet_b0,It is the simplest of all other vgg versions,vgg11,0.175351697,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
5167,convnext_large,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.151863357,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5168,vgg11,ReLU non-linearity as activation functions,vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
5169,regnet_x_32gf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.108854862,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5170,regnet_y_800mf,"The RegNet is composed of a stem, body and head",regnet_y_800mf,1.0,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
5171,resnet50,This model has 3.8 billion FLOPs,resnet50,1.0,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
5172,mnasnet1_3,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.149792736,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5173,convnext_large,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.11549473,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5174,convnext_large,Shallower network means: Number of layers can be reduced and Training time can be shorter as well,wide_resnet50_2,0.198691222,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5175,quantization.shufflenet_v2_x1_0,"Training Techniques are Weight Decay, SGD with Momentum",quantization.shufflenet_v2_x1_0,1.0,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5176,vit_l_16,Constructs a Fully-Convolutional Network model with a ResNet-50 backbone,segmentation.fcn_resnet50,0.103599333,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5177,shufflenet_v2_x0_5,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.159665098,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5178,segmentation.deeplabv3_resnet50,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,1.0,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5179,regnet_y_800mf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.179456427,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
5180,quantization.resnet18,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.223976995,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
5181,convnext_tiny,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.120930364,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5182,vit_b_16,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.085578632,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5183,video.r2plus1d_18,"(2+1)D  convolutional block, which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution",video.r2plus1d_18,1.0,1x32x32,video classification,Null,TV
5184,segmentation.lraspp_mobilenet_v3_large,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.165753811,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5185,quantization.mobilenet_v2,It is the simplest of all other vgg versions,vgg11,0.187493356,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
5186,regnet_x_32gf,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_32gf,1.0,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5187,regnet_y_3_2gf,2D max pooling in between the weight layers,vgg11,0.165380043,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5188,segmentation.deeplabv3_resnet50,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384",vit_l_32,0.181805298,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5189,quantization.mobilenet_v2,Factorizing traditional convolutions to 1x1 convolutions,quantization.mobilenet_v2,1.0,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
5190,resnext50_32x4d,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.206326924,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5191,regnet_x_800mf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_x_800mf,1.0,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
5192,efficientnet_b3,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.222131991,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
5193,mobilenet_v3_small,The network design includes the use of a hard swish activation and squeeze-and-excitation modules in the MBConv blocks,mobilenet_v3_small,1.0,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
5194,detection.fasterrcnn_mobilenet_v3_large_fpn,"As a whole, Faster R-CNN consists of two modules",detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
5195,shufflenet_v2_x2_0,The required minimum input size of the model is 21x21,squeezenet1_0,0.18129826,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
5196,regnet_x_1_6gf,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.174497789,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5197,quantization.shufflenet_v2_x2_0,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.109111628,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5198,quantization.resnext101_32x8d,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.233788103,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5199,segmentation.lraspp_mobilenet_v3_large,Avoiding the use of dense layers means less parameters (making the networks faster to train),segmentation.fcn_resnet50,0.263211352,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5200,regnet_y_400mf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.160507453,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5201,mnasnet0_5,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.145411335,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5202,quantization.shufflenet_v2_x2_0,"Training Techniques are Weight Decay, SGD with Momentum",quantization.shufflenet_v2_x2_0,1.0,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5203,mobilenet_v2,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.094886935,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
5204,densenet201,It is the simplest of all other vgg versions,vgg11,0.182456795,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5205,densenet161,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.14446596,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
5206,resnet18,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.151515223,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5207,squeezenet1_0,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.187536652,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5208,quantization.inception_v3,MobileNetv3-Large is 27% faster than MobileNetV2 while maintaining similar mAP,mobilenet_v3_large,0.147904615,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5209,regnet_y_8gf,The required minimum input size of the model is 17x17,squeezenet1_1,0.20526825,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
5210,densenet169,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,0.243887229,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5211,detection.maskrcnn_resnet50_fpn,The required minimum input size of the model is 32x32,vgg11,0.183887141,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
5212,segmentation.fcn_resnet50,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.041996367,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
5213,vit_b_32,Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2,vgg16,0.174350011,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5214,squeezenet1_0,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.166690119,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5215,vgg13_bn,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.168213608,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5216,wide_resnet101_2,The required minimum input size of the model is 32x32,vgg11,0.145066828,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
5217,vgg19,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.12731883,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
5218,vgg19_bn,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.169555411,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
5219,vit_l_16,"Inception network of googlenet stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid",googlenet,0.153859642,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5220,resnet18,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.098701074,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5221,regnet_y_1_6gf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.104290118,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5222,regnet_y_3_2gf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.148952373,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5223,regnet_x_1_6gf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.106264216,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5224,regnet_x_8gf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.089739444,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5225,detection.retinanet_resnet50_fpn,The required minimum input size of the model is 32x32,vgg11,0.11285083,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
5226,quantization.shufflenet_v2_x2_0,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.119232631,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5227,resnet152,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.129128496,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5228,resnext50_32x4d,"The authors demonstrate its efficacy with the help of a simple, single stage detector, called RetinaNet, which predicts objects by dense sampling of the input image in location, scale and aspect ratio",detection.retinanet_resnet50_fpn,0.250362933,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5229,convnext_large,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.10963787,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5230,regnet_x_3_2gf,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,1.0,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5231,vgg16,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.177480052,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
5232,shufflenet_v2_x1_0,It is the simplest of all other vgg versions,vgg11,0.169578299,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5233,shufflenet_v2_x1_0,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.229412848,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5234,densenet169,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.111646932,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5235,shufflenet_v2_x1_0,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.118304183,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5236,mobilenet_v3_small,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.16216301,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
5237,quantization.shufflenet_v2_x0_5,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.133288055,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5238,efficientnet_b3,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.224762386,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
5239,quantization.inception_v3,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.08272186,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5240,resnet18,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.059373325,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5241,convnext_small,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_small,1.0,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5242,convnext_small,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.080286689,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5243,resnet101,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",resnet101,1.0,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5244,resnet34,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.084034594,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
5245,quantization.shufflenet_v2_x1_5,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.165033334,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5246,detection.ssd300_vgg16,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.172478753,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
5247,resnet152,The required minimum input size of the model is 29x29,densenet121,0.195457531,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5248,segmentation.deeplabv3_resnet101,The required minimum input size of the model is 32x32,vgg11,0.182408538,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
5249,resnet101,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.089273599,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5250,quantization.shufflenet_v2_x1_0,The main building block is a ShuffleNet v2 Block,quantization.shufflenet_v2_x1_0,1.0,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5251,detection.retinanet_resnet50_fpn,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.081083875,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
5252,regnet_x_3_2gf,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.14408198,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5253,quantization.shufflenet_v2_x1_0,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.178361601,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5254,regnet_x_16gf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.143716474,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5255,convnext_large,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.203769637,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5256,segmentation.fcn_resnet101,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.184847791,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5257,vit_l_32,They stack residual blocks on top of each other to form network: for example  a ResNet-50 has fifty layers using these blocks,resnet50,0.122034375,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5258,regnet_x_800mf,Proposed a new layer called ROI Pooling that extracts equal-length feature vectors from all proposals (  ROIs) in the same image,detection.fasterrcnn_resnet50_fpn,0.171182916,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
5259,quantization.googlenet,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.187103951,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5260,vit_b_32,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.069135729,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5261,regnet_y_3_2gf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.163804327,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5262,video.mc3_18,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.08186194,1x32x32,video classification,Null,TV
5263,quantization.resnet18,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.190827218,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
5264,efficientnet_b7,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.208902276,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
5265,mobilenet_v3_large,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.200518904,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
5266,segmentation.deeplabv3_resnet50,This model has 3.8 billion FLOPs,resnet50,0.11892232,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5267,vit_b_16,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.119264468,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5268,regnet_x_400mf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.165623365,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
5269,vgg13,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.173197018,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5270,squeezenet1_1,squeezenet1_1 is used for image classification,squeezenet1_1,1.0,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5271,quantization.shufflenet_v2_x0_5,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.105169318,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5272,segmentation.lraspp_mobilenet_v3_large,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.178992991,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5273,quantization.shufflenet_v2_x1_0,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.161461157,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5274,quantization.shufflenet_v2_x1_5,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.152445525,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5275,mnasnet0_5,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.1677483,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5276,detection.fasterrcnn_resnet50_fpn,"The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection",detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
5277,quantization.mobilenet_v3_large,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.156153968,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5278,mnasnet1_0,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.067523022,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
5279,convnext_base,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.187893093,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5280,shufflenet_v2_x2_0,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.182572744,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
5281,alexnet,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.133167787,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5282,mnasnet0_5,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.173078487,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5283,quantization.resnet50,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.061337562,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
5284,densenet169,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.138155635,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5285,wide_resnet50_2,"The number of channels in outer 1x1 convolutions is the same, Wide ResNet-50-2 has 2048-1024-2048 channels",wide_resnet50_2,1.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
5286,vit_b_32,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.12587843,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5287,quantization.shufflenet_v2_x0_5,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.182786043,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5288,resnet50,They use option 2 for increasing dimensions,resnet50,1.0,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
5289,resnet101,adopted from BERT,vit_b_16,0.0,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5290,inception_v3,Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks,wide_resnet50_2,0.14611614,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5291,vgg13_bn,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.155157022,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5292,regnet_y_3_2gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.201780684,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5293,regnet_x_1_6gf,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.047840763,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5294,regnet_y_8gf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.169743931,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
5295,segmentation.lraspp_mobilenet_v3_large,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.204495573,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5296,vit_l_16,Constructs a RegNetY_8GF architecture,regnet_y_8gf,0.191457064,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5297,regnet_y_8gf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.105823454,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
5298,efficientnet_b7,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.128597291,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
5299,vgg11,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.06845282,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
5300,regnet_x_8gf,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.168535087,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5301,vgg16_bn,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.181979436,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
5302,densenet201,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.15994315,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5303,wide_resnet101_2,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.237634954,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
5304,vit_l_16,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.077464977,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5305,mnasnet0_75,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.122813423,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5306,quantization.resnet50,Replacing ReLU with GeLU,convnext_tiny,0.132973898,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
5307,quantization.resnet50,quantization.resnet50 is the quantized model of resnet50,quantization.resnet50,1.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
5308,quantization.resnet18,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.147059216,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
5309,vit_b_16,The network design includes the use of a hard swish activation and squeeze-and-excitation modules in the MBConv blocks,mobilenet_v3_small,0.165907879,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5310,resnet50,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.159494143,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
5311,vit_b_16,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.104046968,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5312,vgg13_bn,ReLU non-linearity as activation functions,vgg11,0.135581282,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5313,mnasnet1_0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.180659035,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
5314,video.mc3_18,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.211934307,1x32x32,video classification,Null,TV
5315,detection.fasterrcnn_resnet50_fpn,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.059074569,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
5316,mnasnet0_5,"(2+1)D  convolutional block, which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution",video.r2plus1d_18,0.105457723,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5317,mnasnet1_3,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.154477268,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5318,segmentation.lraspp_mobilenet_v3_large,Replacing ReLU with GeLU,convnext_tiny,0.016401108,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5319,detection.fasterrcnn_mobilenet_v3_large_320_fpn,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
5320,segmentation.deeplabv3_resnet101,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.212006,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
5321,detection.retinanet_resnet50_fpn,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.031662819,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
5322,convnext_tiny,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.060800592,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5323,convnext_tiny,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.053368791,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5324,shufflenet_v2_x0_5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.208883516,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5325,googlenet,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.191975399,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5326,vit_b_32,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.124770202,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5327,inception_v3,It is the simplest of all other vgg versions,vgg11,0.180247462,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5328,vgg16,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.199513099,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
5329,efficientnet_b4,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.140913923,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
5330,vit_l_16,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.156324663,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5331,segmentation.deeplabv3_resnet50,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.084067924,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5332,convnext_base,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.063626028,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5333,shufflenet_v2_x1_5,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.091984148,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5334,detection.ssd300_vgg16,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.115656136,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
5335,vit_b_16,Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone,segmentation.lraspp_mobilenet_v3_large,0.155484057,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5336,quantization.resnext101_32x8d,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.148386004,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5337,regnet_x_800mf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.117096518,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
5338,quantization.mobilenet_v3_large,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.133992806,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5339,resnext50_32x4d,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.131720526,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5340,regnet_x_16gf,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.133779403,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5341,shufflenet_v2_x2_0,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.180081123,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
5342,resnet152,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.2055285,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5343,convnext_base,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.058143636,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5344,resnext50_32x4d,"Mask R-CNN performed better than the existing state of the art single-model architectures, added an extra functionality of instance segmentation with little overhead computations",detection.maskrcnn_resnet50_fpn,0.22778888,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5345,segmentation.deeplabv3_resnet50,Note that this model does not include any fine-tuned heads,vit_b_16,0.142152603,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5346,detection.ssdlite320_mobilenet_v3_large,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.161307859,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5347,quantization.inception_v3,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.inception_v3,1.0,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5348,efficientnet_b7,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.21789325,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
5349,detection.keypointrcnn_resnet50_fpn,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.074072966,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5350,densenet169,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.163934501,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5351,efficientnet_b1,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.171602216,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
5352,efficientnet_b4,EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS,efficientnet_b4,1.0,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
5353,shufflenet_v2_x1_5,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.175824101,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5354,regnet_y_800mf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.181643243,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
5355,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.15577432,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
5356,regnet_x_800mf,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.146590996,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
5357,vgg11_bn,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.204106722,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
5358,quantization.shufflenet_v2_x1_0,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.134131917,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5359,convnext_base,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.088037815,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5360,quantization.mobilenet_v3_large,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.125332098,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5361,shufflenet_v2_x1_0,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.165964731,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5362,segmentation.deeplabv3_mobilenet_v3_large,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.133531221,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5363,regnet_x_1_6gf,They use option 2 for increasing dimensions,resnet50,0.195764838,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5364,regnet_x_32gf,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.151503673,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5365,convnext_small,2D max pooling in between the weight layers,vgg11,0.100135686,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5366,regnet_y_3_2gf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.151528473,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5367,quantization.shufflenet_v2_x1_0,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.183470536,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5368,convnext_large,"Dense blocks, Batch Normalization(BN)",densenet121,0.124639458,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5369,detection.keypointrcnn_resnet50_fpn,They use option 2 for increasing dimensions,resnet50,0.14565436,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5370,regnet_x_3_2gf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.148636392,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5371,video.r2plus1d_18,Constructor for 18 layer Mixed Convolution network,video.r2plus1d_18,1.0,1x32x32,video classification,Null,TV
5372,quantization.googlenet,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.15806458,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5373,squeezenet1_1,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.148275794,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5374,segmentation.fcn_resnet101,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.034106303,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5375,densenet169,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.219464044,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5376,regnet_y_400mf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.202650589,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5377,shufflenet_v2_x1_0,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.100402675,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5378,efficientnet_b2,adopted from BERT,vit_b_16,0.0,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
5379,detection.maskrcnn_resnet50_fpn,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
5380,detection.maskrcnn_resnet50_fpn,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.127078387,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
5381,quantization.resnet18,adopted from BERT,vit_b_16,0.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
5382,convnext_tiny,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.143799837,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5383,googlenet,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.16686209,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5384,densenet201,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.148559002,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5385,convnext_large,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.140946235,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5386,convnext_base,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.220698938,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5387,mnasnet1_3,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.16473567,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5388,vit_l_32,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,shufflenet_v2_x0_5,0.128131477,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5389,detection.ssdlite320_mobilenet_v3_large,"VGG19_bn is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19_bn,0.158930947,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5390,regnet_x_32gf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.129547556,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5391,quantization.shufflenet_v2_x2_0,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.115679517,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5392,regnet_x_400mf,They use option 2 for increasing dimensions,resnet50,0.167293595,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
5393,alexnet,AlexNet is a classic convolutional neural network architecture,alexnet,1.0,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5394,detection.retinanet_resnet50_fpn,It is the simplest of all other vgg versions,vgg11,0.120751242,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
5395,mnasnet0_75,They stack residual blocks ontop of each other to form network,vgg19_bn,0.239427288,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5396,segmentation.fcn_resnet101,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.030334364,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5397,resnet152,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.045309411,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5398,resnext101_32x8d,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.20480625,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5399,convnext_tiny,They stack residual blocks on top of each other to form network: for example  a ResNet-18 has 18 layers using these blocks,resnet18,0.177831037,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5400,mnasnet0_75,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.214432652,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5401,mnasnet0_5,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.11904542,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5402,efficientnet_b2,It is the simplest of all other vgg versions,vgg11,0.176553483,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
5403,vgg13_bn,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.172612466,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5404,segmentation.deeplabv3_mobilenet_v3_large,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.179004224,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5405,resnet152,Note that this model does not include any fine-tuned heads,vit_b_16,0.183599343,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5406,regnet_y_8gf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.096414655,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
5407,vit_b_16,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.101631412,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5408,vgg11,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.138552057,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
5409,convnext_small,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.18146429,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5410,quantization.inception_v3,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.205779211,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5411,shufflenet_v2_x2_0,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.162799452,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
5412,mnasnet0_75,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.150947014,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5413,quantization.shufflenet_v2_x0_5,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",quantization.shufflenet_v2_x0_5,1.0,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5414,regnet_x_16gf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.210025274,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5415,quantization.shufflenet_v2_x1_0,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.145551582,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5416,convnext_tiny,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.16404997,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5417,googlenet,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.115080091,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5418,regnet_x_32gf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.125894858,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5419,video.r3d_18,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.192604046,1x32x32,video classification,Null,TV
5420,alexnet,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.130118301,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5421,efficientnet_b2,Replacing ReLU with GeLU,convnext_tiny,0.083142404,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
5422,vit_l_16,It works similarly to Faster R-CNN with ResNet-50 FPN backbone,detection.fasterrcnn_mobilenet_v3_large_fpn,0.144621964,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5423,detection.ssdlite320_mobilenet_v3_large,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.095608536,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5424,quantization.googlenet,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.056710039,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5425,segmentation.fcn_resnet101,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.170157839,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5426,mnasnet0_75,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.255274688,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5427,vgg19_bn,ResNet-152 can obtain 4.49% error rate,resnet152,0.205924206,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
5428,densenet121,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.254137834,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
5429,quantization.inception_v3,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.170763611,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5430,resnet152,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.099683865,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5431,regnet_y_3_2gf,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_3_2gf,1.0,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5432,regnet_y_8gf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.216598327,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
5433,efficientnet_b6,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.154915983,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
5434,quantization.resnet18,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.195259412,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
5435,mnasnet1_3,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.21412384,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5436,detection.ssdlite320_mobilenet_v3_large,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.18682413,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5437,detection.ssd300_vgg16,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.194230812,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
5438,vit_l_32,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.089684059,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5439,convnext_tiny,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.16672932,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5440,segmentation.fcn_resnet50,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.151154204,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
5441,squeezenet1_1,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.108755662,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5442,quantization.resnet50,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.1040944,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
5443,squeezenet1_1,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.155951247,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5444,googlenet,The number of parameters is 3.4 million,mobilenet_v2,0.099234841,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5445,vit_l_16,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.104709675,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5446,regnet_y_400mf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.130446734,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5447,convnext_small,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.102402152,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5448,convnext_base,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.255796775,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5449,vgg11,"It has 11 weight layers in total, so the name VGG11",vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
5450,convnext_base,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.119215774,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5451,convnext_base,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.062265572,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5452,convnext_large,Constructs a DeepLabV3 model with a MobileNetV3-Large backbone,segmentation.deeplabv3_mobilenet_v3_large,0.156301062,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5453,mnasnet0_75,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.179412295,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5454,regnet_y_32gf,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.123774854,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
5455,resnet18,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.074585147,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5456,shufflenet_v2_x1_5,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.160275385,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5457,quantization.shufflenet_v2_x1_5,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.119485103,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5458,regnet_x_800mf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.17151024,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
5459,densenet201,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.05623851,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5460,regnet_y_32gf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.183039559,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
5461,wide_resnet101_2,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.179149435,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
5462,googlenet,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.209780599,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5463,efficientnet_b0,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.104103991,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
5464,convnext_large,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_large,1.0,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5465,detection.keypointrcnn_resnet50_fpn,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.145712996,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5466,convnext_base,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.075244751,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5467,mnasnet1_3,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.080973578,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5468,resnet101,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.149572254,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5469,inception_v3,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.104721451,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5470,shufflenet_v2_x1_5,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.226383993,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5471,detection.fasterrcnn_mobilenet_v3_large_fpn,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.07849613,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
5472,regnet_y_400mf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.164114444,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5473,convnext_small,"Each layer from the FPN is passed to the subnets, enabling it to detect objects as various scales",detection.retinanet_resnet50_fpn,0.202518265,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5474,detection.ssdlite320_mobilenet_v3_large,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.187879232,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5475,detection.maskrcnn_resnet50_fpn,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.069154643,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
5476,densenet161,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.219747233,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
5477,regnet_x_16gf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_x_16gf,1.0,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5478,vgg13,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.172439067,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5479,video.mc3_18,Constructor for 18 layer Mixed Convolution network,video.mc3_18,1.0,1x32x32,video classification,Null,TV
5480,regnet_y_400mf,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.125843925,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5481,resnet152,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.14519551,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5482,shufflenet_v2_x1_5,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.125373615,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5483,squeezenet1_1,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,1.0,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5484,quantization.shufflenet_v2_x1_5,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.224381508,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5485,detection.keypointrcnn_resnet50_fpn,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.157322644,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5486,segmentation.fcn_resnet101,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.150806542,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5487,mobilenet_v3_large,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.228451896,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
5488,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.052718377,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
5489,shufflenet_v2_x2_0,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.1889634,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
5490,inception_v3,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.171073928,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5491,vit_l_16,Constructs a RegNetY_1.6GF architecture,regnet_y_1_6gf,0.184838265,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5492,vit_b_16,Network architecture of the 3D ResNet,video.r3d_18,0.144091712,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5493,vit_b_16,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.138196507,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5494,efficientnet_b3,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.058246562,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
5495,regnet_x_800mf,"ssd300_vgg16 is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.ssd300_vgg16,0.089392456,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
5496,detection.fasterrcnn_resnet50_fpn,The original dataset contained images of size 512x512x1,resnet101,0.161314141,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
5497,efficientnet_b5,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.193780068,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
5498,vgg11,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.146372217,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
5499,detection.fasterrcnn_mobilenet_v3_large_fpn,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.071506869,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
5500,inception_v3,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.199230671,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5501,quantization.inception_v3,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.150443826,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5502,detection.retinanet_resnet50_fpn,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.102091759,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
5503,squeezenet1_1,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.092884028,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5504,vgg19_bn,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.170178063,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
5505,vgg11_bn,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.16026037,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
5506,convnext_tiny,ResNet-152 can obtain 4.49% error rate,resnet152,0.224034111,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5507,shufflenet_v2_x1_5,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.209153844,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5508,vit_b_16,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.06212149,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5509,efficientnet_b4,Replacing ReLU with GeLU,convnext_tiny,0.087510173,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
5510,resnet18,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.097777725,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5511,densenet201,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.202810987,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5512,quantization.shufflenet_v2_x1_5,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.119720393,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5513,vit_b_32,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.127535551,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5514,regnet_y_32gf,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.133477728,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
5515,video.r2plus1d_18,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.178678976,1x32x32,video classification,Null,TV
5516,detection.retinanet_resnet50_fpn,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.214618977,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
5517,quantization.shufflenet_v2_x2_0,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.161729637,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5518,quantization.resnext101_32x8d,The key building block is an Inception Module,inception_v3,0.156775831,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5519,resnet18,They use option 2 for increasing dimensions,resnet50,0.225917071,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5520,resnet50,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.148519679,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
5521,mnasnet1_0,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.197221005,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
5522,vit_l_32,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.057338978,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5523,regnet_y_400mf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.161955035,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5524,wide_resnet50_2,wide_resnet50_2 is used for image classification,wide_resnet50_2,1.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
5525,efficientnet_b1,The key building block is an Inception Module,inception_v3,0.183388107,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
5526,densenet169,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.181963706,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5527,resnet152,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.222754374,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5528,quantization.shufflenet_v2_x1_5,adopted from BERT,vit_b_16,0.003048044,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5529,inception_v3,This issue was later solved by using better backbone architectures like ResNet and other small fixes,detection.ssd300_vgg16,0.168992923,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5530,detection.ssdlite320_mobilenet_v3_large,11 weight layers (convolutional + fully connected),vgg11,0.117295783,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5531,resnet101,resnet101 is used for image classification,resnet101,1.0,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5532,detection.fasterrcnn_mobilenet_v3_large_320_fpn,Faster R-CNN builds a network that has only a single stage,detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
5533,quantization.shufflenet_v2_x2_0,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.17580986,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5534,regnet_y_1_6gf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.175745936,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5535,wide_resnet101_2,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.238274338,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
5536,convnext_small,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.095950931,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5537,segmentation.deeplabv3_resnet101,Note that this model does not include any fine-tuned heads,vit_b_16,0.14246674,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
5538,mobilenet_v3_small,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.159171795,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
5539,video.r2plus1d_18,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.043085791,1x32x32,video classification,Null,TV
5540,segmentation.lraspp_mobilenet_v3_large,Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone,segmentation.lraspp_mobilenet_v3_large,1.0,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5541,resnet50,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.138169349,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
5542,regnet_x_1_6gf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.184247679,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5543,detection.fasterrcnn_mobilenet_v3_large_320_fpn,adopted from BERT,vit_b_16,0.003134111,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
5544,segmentation.deeplabv3_mobilenet_v3_large,VGG19 has 19.6 billion FLOPs,vgg19,0.112861815,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5545,quantization.resnet50,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.136528616,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
5546,quantization.inception_v3,"The authors demonstrate its efficacy with the help of a simple, single stage detector, called RetinaNet, which predicts objects by dense sampling of the input image in location, scale and aspect ratio",detection.retinanet_resnet50_fpn,0.183220822,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5547,detection.retinanet_resnet50_fpn,VGG19 has 19.6 billion FLOPs,vgg19,0.080691839,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
5548,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
5549,mnasnet1_3,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.202471162,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5550,vgg13_bn,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.17212901,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5551,quantization.shufflenet_v2_x1_0,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.175630691,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5552,vgg16_bn,Replacing ReLU with GeLU,convnext_tiny,0.157570573,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
5553,wide_resnet50_2,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.195626704,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
5554,vgg13,ResNet-152 can obtain 4.49% error rate,resnet152,0.210180987,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5555,vit_l_16,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_400mf,0.128473595,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5556,detection.fasterrcnn_mobilenet_v3_large_fpn,The required minimum input size of the model is 29x29,densenet121,0.192490628,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
5557,vit_l_16,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.136928568,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5558,video.r3d_18,"It has 11 weight layers in total, so the name VGG11",vgg11,0.229282537,1x32x32,video classification,Null,TV
5559,resnet152,Replacing ReLU with GeLU,convnext_tiny,0.173794814,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5560,quantization.inception_v3,DenseNet-121 has 120 Convolutions and 4 AvgPool,densenet121,0.208988523,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5561,resnet152,It is the simplest of all other vgg versions,vgg11,0.12261888,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5562,detection.ssdlite320_mobilenet_v3_large,"A ConvNet which has the design simplicity of Convolutional Neural Networks while having better accuracy, performance and scalability than Vision Transformers",convnext_tiny,0.1658979,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5563,regnet_x_1_6gf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.152735276,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5564,quantization.googlenet,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.092717626,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5565,vgg13,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.165218043,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5566,segmentation.deeplabv3_mobilenet_v3_large,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.169095569,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5567,segmentation.fcn_resnet50,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels",vit_b_16,0.198447163,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
5568,regnet_x_16gf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.092052258,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5569,quantization.shufflenet_v2_x1_0,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.106207361,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5570,densenet201,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.164342386,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5571,convnext_tiny,The densenet-201 is larger at over 77MB in size vs the densenet-121 model's roughly 31MB size,densenet201,0.168423053,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5572,segmentation.deeplabv3_resnet50,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.15451871,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5573,efficientnet_b1,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.214188677,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
5574,video.mc3_18,mc3_18 is used for video classification,video.mc3_18,1.0,1x32x32,video classification,Null,TV
5575,quantization.inception_v3,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.123137594,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5576,segmentation.deeplabv3_resnet50,"Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384",vit_l_32,0.163841185,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5577,densenet201,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.151556857,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5578,regnet_x_3_2gf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.138974338,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5579,detection.retinanet_resnet50_fpn,RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training,detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
5580,segmentation.fcn_resnet101,"In particular, in this section we use the 400 million flops (400MF) regime and train each sampled model for 10 epochs on the ImageNet dataset",regnet_y_400mf,0.16995485,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5581,quantization.resnet50,resnet50 is used for image classification,quantization.resnet50,1.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
5582,efficientnet_b7,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.111625872,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
5583,detection.keypointrcnn_resnet50_fpn,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.09363147,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5584,alexnet,It is the simplest of all other vgg versions,vgg11,0.143851496,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5585,vgg16_bn,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.084088921,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
5586,regnet_x_400mf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.159778452,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
5587,quantization.mobilenet_v3_large,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.12229943,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5588,regnet_y_400mf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.172482472,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5589,mobilenet_v2,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.133139754,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
5590,detection.keypointrcnn_resnet50_fpn,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.131260624,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5591,convnext_large,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.098277664,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5592,quantization.inception_v3,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.139224308,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5593,convnext_small,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.132469432,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5594,convnext_base,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.119330588,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5595,googlenet,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.255270839,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5596,video.mc3_18,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.115279685,1x32x32,video classification,Null,TV
5597,quantization.shufflenet_v2_x0_5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.213020071,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5598,shufflenet_v2_x2_0,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.216855466,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
5599,detection.retinanet_resnet50_fpn,The first subnet performs convolutional object classification on the backbone's output; the second subnet performs convolutional bounding box regression,detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
5600,segmentation.fcn_resnet101,"Mask R-CNN uses anchor boxes to detect multiple objects, objects of different scales, and overlapping objects in an image",detection.maskrcnn_resnet50_fpn,0.222969334,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5601,regnet_x_32gf,in this section we use the 32 billion flops (32GF) regime,regnet_x_32gf,1.0,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5602,squeezenet1_0,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.22750602,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5603,shufflenet_v2_x0_5,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.204982431,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5604,squeezenet1_0,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.220985362,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5605,inception_v3,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.070967691,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5606,quantization.resnet18,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",quantization.resnet18,1.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
5607,resnext101_32x8d,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.04514934,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5608,vgg13_bn,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg13_bn,1.0,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5609,vgg11,It is the simplest of all other vgg versions,vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
5610,resnet34,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.155159339,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
5611,regnet_x_3_2gf,It is the simplest of all other vgg versions,vgg11,0.190404168,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5612,shufflenet_v2_x0_5,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.049671405,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5613,vit_b_32,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.130827355,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5614,mnasnet0_5,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.216006254,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5615,vit_b_16,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.131534409,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5616,squeezenet1_0,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.227075985,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5617,vit_l_32,Constructs a Fully-Convolutional Network model with a ResNet-101 backbone,segmentation.fcn_resnet101,0.104158748,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5618,resnet101,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.057708791,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5619,mnasnet0_75,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.098995686,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5620,inception_v3,Constructs a RegNetY_16GF architecture,regnet_y_16gf,0.2381663,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5621,segmentation.lraspp_mobilenet_v3_large,"A ConvNet which has the design simplicity of Convolutional Neural Networks while having better accuracy, performance and scalability than Vision Transformers",convnext_tiny,0.194271907,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5622,detection.ssdlite320_mobilenet_v3_large,"For object detection, the authors used MobileNetv2 as the feature extractor of a computationally efficient variant of the SSD",detection.ssdlite320_mobilenet_v3_large,1.0,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5623,quantization.resnext101_32x8d,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.214611287,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5624,wide_resnet50_2,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.168895489,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
5625,regnet_x_1_6gf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.11230737,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5626,detection.fasterrcnn_mobilenet_v3_large_fpn,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
5627,densenet169,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.137911158,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5628,quantization.shufflenet_v2_x2_0,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.143833293,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5629,regnet_y_800mf,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.138270217,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
5630,regnet_x_400mf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_x_400mf,1.0,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
5631,squeezenet1_0,SqueezeNet 1.0 achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters,squeezenet1_0,1.0,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5632,squeezenet1_1,The key building block is an Inception Module,inception_v3,0.233153711,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5633,segmentation.fcn_resnet101,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.250867186,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5634,efficientnet_b7,It is the simplest of all other vgg versions,vgg11,0.200486905,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
5635,video.mc3_18,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.205800231,1x32x32,video classification,Null,TV
5636,resnet50,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.147506808,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
5637,vgg19,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
5638,regnet_x_16gf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.186423156,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5639,resnet18,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.099517228,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5640,resnext50_32x4d,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.216673985,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5641,shufflenet_v2_x1_5,"1x1 Convolution, Channel Shuffle, Depthwise Convolution, Squeeze-and-Excitation Block, ShuffleNet V2 Downsampling Block, Batch Normalization, Convolution, Global Average Pooling, ShuffleNet V2 Block, Residual Connection, ReLU, Max Pooling, Softmax",shufflenet_v2_x1_5,1.0,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5642,inception_v3,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.112838188,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5643,alexnet,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.071160742,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5644,mnasnet0_5,The required minimum input size of the model is 21x21,squeezenet1_0,0.20265766,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5645,efficientnet_b0,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.170554328,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
5646,regnet_y_1_6gf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.055604786,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5647,mnasnet1_3,It is the simplest of all other vgg versions,vgg11,0.166726458,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5648,efficientnet_b3,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.161818069,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
5649,resnext50_32x4d,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.178906202,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5650,detection.fasterrcnn_resnet50_fpn,Constructs a Faster R-CNN model with a ResNet-50-FPN backbone,detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
5651,video.r2plus1d_18,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.218457566,1x32x32,video classification,Null,TV
5652,regnet_y_32gf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.148086901,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
5653,densenet121,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.128789201,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
5654,regnet_x_400mf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.155245512,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
5655,squeezenet1_1,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.184279819,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5656,regnet_y_400mf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.08530376,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5657,regnet_y_1_6gf,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.160477764,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5658,resnext50_32x4d,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.105333425,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5659,regnet_x_8gf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.16843904,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5660,mnasnet0_75,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.063441304,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5661,segmentation.deeplabv3_resnet101,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.114093843,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
5662,googlenet,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.224901045,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5663,regnet_y_1_6gf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.140738757,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5664,mnasnet1_3,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.154237268,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5665,regnet_x_3_2gf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.14951965,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5666,efficientnet_b5,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.217397932,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
5667,vit_l_32,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.143340229,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5668,vgg11_bn,11 weight layers (convolutional + fully connected),vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
5669,googlenet,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.098972293,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5670,regnet_y_800mf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.163737444,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
5671,vgg11,ResNet-152 can obtain 4.49% error rate,resnet152,0.172776372,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
5672,mnasnet1_3,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.233048057,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5673,regnet_x_16gf,2D max pooling in between the weight layers,vgg11,0.157980061,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5674,detection.ssdlite320_mobilenet_v3_large,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.192153268,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5675,squeezenet1_1,"The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection",detection.fasterrcnn_resnet50_fpn,0.227554008,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5676,densenet169,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.141634555,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5677,regnet_y_16gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.189488678,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
5678,convnext_small,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.193931352,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5679,googlenet,The required minimum input size of the model is 32x32,vgg11,0.1156239,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5680,regnet_y_8gf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.107842785,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
5681,detection.ssdlite320_mobilenet_v3_large,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.184454622,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5682,convnext_base,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.048289001,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5683,densenet201,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.208840283,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5684,shufflenet_v2_x0_5,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.217946528,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5685,squeezenet1_1,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.242886802,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5686,quantization.shufflenet_v2_x2_0,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.160872181,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5687,quantization.shufflenet_v2_x0_5,"It builds upon ShuffleNet v1, which utilised pointwise group convolutions, bottleneck-like structures, and a channel shuffle operation",quantization.shufflenet_v2_x0_5,1.0,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5688,quantization.resnext101_32x8d,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.182247467,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5689,vgg11_bn,"It has 11 weight layers in total, so the name VGG11",vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
5690,quantization.mobilenet_v3_large,ReLU non-linearity as activation functions,vgg11,0.120051729,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5691,resnext101_32x8d,Replacing ReLU with GeLU,convnext_tiny,0.220679004,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5692,vgg16,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.198294837,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
5693,shufflenet_v2_x0_5,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.154028435,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5694,vit_b_32,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.127443673,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5695,shufflenet_v2_x1_0,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.198404906,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5696,detection.ssdlite320_mobilenet_v3_large,Note that this model does not include any fine-tuned heads,vit_b_16,0.159880366,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5697,alexnet,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.162764074,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5698,quantization.inception_v3,"Inception v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using Label Smoothing, Factorized 7 x 7 convolutions, and the use of an auxiliary classifier to propagate label information lower down the network (along with the use of batch normalization for layers in the side head)",quantization.inception_v3,1.0,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5699,segmentation.deeplabv3_mobilenet_v3_large,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.118530855,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5700,regnet_y_32gf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.160733823,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
5701,shufflenet_v2_x0_5,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.131439508,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5702,regnet_y_1_6gf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.189143497,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5703,regnet_x_400mf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.087270972,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
5704,vit_b_16,"It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block",googlenet,0.167615701,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5705,mnasnet0_5,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.122620222,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5706,googlenet,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.172447092,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5707,mnasnet1_0,adopted from BERT,vit_b_16,0.0,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
5708,regnet_x_1_6gf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.190338574,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5709,vgg13_bn,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,1.0,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5710,vit_l_32,"1x1 Convolution, Batch Normalization, Convolution, Depth wise Separable Convolution, Dropout, Global Average Pooling, Inverted Residual Block, Residual Connection, ReLU, Max Pooling, Softmax, Squeeze-and-Excitation Block",mnasnet1_0,0.149554573,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5711,wide_resnet101_2,The required minimum input size of the model is 29x29,densenet121,0.180103632,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
5712,resnext50_32x4d,They use option 2 for increasing dimensions,resnet50,0.26164759,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5713,quantization.mobilenet_v3_large,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.147768803,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5714,regnet_y_1_6gf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.181942636,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5715,segmentation.deeplabv3_resnet50,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.18782971,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5716,convnext_tiny,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.043319285,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5717,convnext_tiny,The required minimum input size of the model is 29x29,densenet121,0.168665863,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5718,detection.ssdlite320_mobilenet_v3_large,Proposed a new layer called ROI Pooling that extracts equal-length feature vectors from all proposals (  ROIs) in the same image,detection.fasterrcnn_resnet50_fpn,0.136478312,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5719,mnasnet1_3,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.201689065,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5720,resnet101,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.177784296,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5721,vgg16_bn,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.188598418,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
5722,quantization.shufflenet_v2_x1_5,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.113073106,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5723,regnet_y_1_6gf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.140741311,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5724,vgg16,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.173774607,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
5725,regnet_x_8gf,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.177147694,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5726,resnext101_32x8d,"Keypoint RCNN  slightly modifies the existing Mask RCNN, by one-hot encoding a keypoint (instead of the whole mask) of the detected object",detection.keypointrcnn_resnet50_fpn,0.208918389,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5727,squeezenet1_0,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.07417252,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5728,quantization.shufflenet_v2_x1_0,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.121318844,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5729,resnext50_32x4d,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.151714245,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5730,segmentation.fcn_resnet50,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.200739413,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
5731,squeezenet1_0,adopted from BERT,vit_b_16,0.018446294,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5732,regnet_x_3_2gf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.183438867,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5733,detection.ssdlite320_mobilenet_v3_large,There are 4 convolutional layers in each module (excluding the 1x1 convolutional layer),resnet18,0.102900336,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5734,regnet_x_32gf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.16802855,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5735,quantization.shufflenet_v2_x0_5,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.127634997,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5736,resnet34,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.115715309,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
5737,regnet_x_1_6gf,Replacing ReLU with GeLU,convnext_tiny,0.139336374,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5738,regnet_x_8gf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_x_8gf,1.0,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5739,quantization.shufflenet_v2_x0_5,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.133093423,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
5740,convnext_large,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.124641834,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5741,convnext_tiny,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.186756739,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5742,regnet_y_3_2gf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.190245827,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5743,quantization.mobilenet_v3_large,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.204247068,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5744,resnext101_32x8d,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.232503928,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5745,detection.fasterrcnn_mobilenet_v3_large_fpn,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.08126085,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
5746,inception_v3,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.224980188,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5747,regnet_x_400mf,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.10399164,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
5748,vgg11_bn,The convolutional layers will have a 3x3 kernel size with a stride of 1 and padding of 1,vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
5749,resnext50_32x4d,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.170019116,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5750,segmentation.fcn_resnet101,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.204078842,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5751,squeezenet1_0,followed by Rectified linear unit(ReLu) to introduce non-linearity to make the model classify better and to improve computational time as the previous models used tanh or sigmoid functions this proved much better than those,vgg19,0.143111759,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5752,quantization.googlenet,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.060638669,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5753,quantization.inception_v3,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.11471634,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5754,convnext_large,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.162269461,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5755,video.r2plus1d_18,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.201112444,1x32x32,video classification,Null,TV
5756,quantization.shufflenet_v2_x2_0,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.148942519,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5757,quantization.inception_v3,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.184553944,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5758,mnasnet0_5,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.232014408,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5759,detection.keypointrcnn_resnet50_fpn,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.134796998,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5760,squeezenet1_0,"The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions",detection.fasterrcnn_resnet50_fpn,0.207692238,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5761,detection.fasterrcnn_mobilenet_v3_large_fpn,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.063381583,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
5762,densenet201,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.156319474,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5763,mnasnet1_3,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.096472954,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5764,detection.keypointrcnn_resnet50_fpn,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.069766698,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5765,segmentation.fcn_resnet50,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.178130268,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
5766,regnet_x_3_2gf,"The RegNet is composed of a stem, body and head",regnet_x_3_2gf,1.0,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5767,efficientnet_b1,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.184850044,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
5768,regnet_y_1_6gf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.201714724,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5769,shufflenet_v2_x1_5,"In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly",inception_v3,0.151779977,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5770,video.r2plus1d_18,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.152821355,1x32x32,video classification,Null,TV
5771,vgg13,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.081633948,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5772,vit_l_16,MNASNet with depth multiplier of 1.3,mnasnet1_3,0.203503141,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5773,vgg13,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.153263535,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5774,alexnet,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.116049813,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5775,regnet_x_800mf,Constructs a RegNetX_800MF architecture,regnet_x_800mf,1.0,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
5776,efficientnet_b1,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.188860316,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
5777,efficientnet_b5,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.163027689,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
5778,quantization.shufflenet_v2_x2_0,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.115076248,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5779,vit_b_16,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.168057889,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5780,segmentation.deeplabv3_mobilenet_v3_large,The dense prediction is achieved by simply up-sampling the output of the last convolution layer and computing pixel-wise loss,segmentation.deeplabv3_mobilenet_v3_large,1.0,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5781,quantization.inception_v3,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.224895589,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5782,mnasnet1_3,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.120596198,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5783,vit_b_32,"ViT-B-32 is slightly faster than ResNet50 and it performs much worse on the 9M subset, but better on 90M+ subsets",vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5784,inception_v3,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.101109528,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5785,regnet_x_16gf,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.126805132,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5786,mnasnet0_5,Note that this model does not include any fine-tuned heads,vit_b_16,0.158030142,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5787,regnet_y_3_2gf,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.078628378,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5788,quantization.mobilenet_v3_large,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",vgg19,0.199097149,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5789,regnet_x_32gf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.174479292,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5790,quantization.resnext101_32x8d,VGG19 has 19.6 billion FLOPs,vgg19,0.116068978,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5791,quantization.shufflenet_v2_x1_0,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.11291699,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5792,inception_v3,Constructs a DeepLabV3 model with a MobileNetV3-Large backbone,segmentation.deeplabv3_mobilenet_v3_large,0.193630114,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5793,densenet169,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.112689563,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5794,vgg13_bn,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,0.185540055,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5795,convnext_tiny,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.143775795,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5796,segmentation.fcn_resnet101,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet101,1.0,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5797,mnasnet0_5,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.189942673,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5798,video.r2plus1d_18,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.16771527,1x32x32,video classification,Null,TV
5799,shufflenet_v2_x1_5,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.15506344,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
5800,quantization.resnext101_32x8d,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.235876148,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5801,vgg13,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.168211691,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5802,regnet_y_400mf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.149214373,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5803,resnet101,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.204918042,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5804,regnet_y_8gf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.192848095,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
5805,regnet_x_16gf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.144336111,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5806,resnet18,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.184849608,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5807,vit_l_16,It uses ResNet augmented by Feature Pyramid Network (FPN) as the backbone and two similar subnets - classification and bounding box regressor (regression),detection.retinanet_resnet50_fpn,0.213156823,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5808,vit_b_32,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.083194791,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5809,regnet_y_3_2gf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.128536314,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5810,regnet_x_3_2gf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.127288605,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5811,vit_l_16,"1x1 Convolution, Batch Normalization, Convolution, Depth wise Separable Convolution, Dropout, Global Average Pooling, Inverted Residual Block, Residual Connection, ReLU, Max Pooling, Softmax, Squeeze-and-Excitation Block",mnasnet1_0,0.154020759,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
5812,densenet169,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.175069759,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5813,quantization.shufflenet_v2_x2_0,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.162259418,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5814,squeezenet1_1,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.271303629,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5815,vgg13,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.215327673,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5816,vit_b_32,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.133955417,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5817,resnext101_32x8d,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.115767548,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5818,convnext_tiny,Replacing ReLU with GeLU,convnext_tiny,1.0,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5819,quantization.shufflenet_v2_x1_5,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.173168199,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5820,resnet18,It is the simplest of all other vgg versions,vgg11,0.116687803,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5821,efficientnet_b3,adopted from BERT,vit_b_16,0.0,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
5822,regnet_y_32gf,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_32gf,1.0,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
5823,googlenet,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.155652562,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5824,mobilenet_v3_small,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,1.0,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
5825,regnet_y_400mf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.174078773,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5826,segmentation.fcn_resnet101,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.148256256,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5827,efficientnet_b7,"The rationale behind this design is that motion modeling is a low/mid-level operation that can be implemented via 3D convolutions in the early layers of a network, and spatial reasoning over these mid-level motion features (implemented by 2D convolutions in the top layers) leads to accurate action recognition",video.mc3_18,0.203788704,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
5828,detection.retinanet_resnet50_fpn,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.187674488,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
5829,regnet_y_800mf,The required minimum input size of the model is 17x17,squeezenet1_1,0.208211598,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
5830,quantization.resnext101_32x8d,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.213768199,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5831,resnet101,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.134633831,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5832,regnet_x_3_2gf,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.174187143,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5833,googlenet,This model has 3.8 billion FLOPs,resnet50,0.123573045,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5834,regnet_y_16gf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.182163105,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
5835,regnet_y_16gf,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.124032726,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
5836,resnet101,They use option 2 for increasing dimensions,resnet50,0.211094401,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5837,wide_resnet50_2,"By widening Residual Network (ResNet), the network can be shallower with the same accuracy or improved accuracy",wide_resnet50_2,1.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
5838,googlenet,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.063558802,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5839,mobilenet_v3_small,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.165576755,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
5840,regnet_x_32gf,Replacing ReLU with GeLU,convnext_tiny,0.131421272,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5841,regnet_x_800mf,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.15466748,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
5842,mnasnet1_3,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.107274517,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5843,regnet_x_400mf,2D max pooling in between the weight layers,vgg11,0.15419407,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
5844,regnet_y_8gf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.138711199,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
5845,segmentation.fcn_resnet101,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.021541383,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5846,regnet_x_8gf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.089739444,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5847,inception_v3,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.141454088,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5848,mnasnet0_5,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.251224592,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5849,shufflenet_v2_x0_5,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.169141627,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5850,mobilenet_v2,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.178235887,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
5851,convnext_tiny,"A ConvNet which has the design simplicity of Convolutional Neural Networks while having better accuracy, performance and scalability than Vision Transformers",convnext_tiny,1.0,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
5852,mnasnet0_75,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.014050705,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5853,quantization.shufflenet_v2_x2_0,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.128344795,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
5854,video.r2plus1d_18,VGG19 has 19.6 billion FLOPs,vgg19,0.098612886,1x32x32,video classification,Null,TV
5855,quantization.googlenet,VGG19 has 19.6 billion FLOPs,vgg19,0.056720925,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5856,squeezenet1_1,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.224652174,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5857,densenet161,adopted from BERT,vit_b_16,0.0,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
5858,detection.fasterrcnn_resnet50_fpn,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.079570842,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
5859,quantization.resnet50,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.155505944,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
5860,resnext50_32x4d,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.09723435,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5861,regnet_y_3_2gf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.114767686,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5862,segmentation.deeplabv3_resnet50,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.084221222,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5863,alexnet,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.085796893,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5864,mnasnet1_3,This model has 3.8 billion FLOPs,resnet50,0.143494107,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5865,resnext50_32x4d,"By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder",vit_b_16,0.123791805,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5866,wide_resnet101_2,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.114118408,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
5867,resnet101,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.158034059,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5868,regnet_y_1_6gf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_1_6gf,1.0,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5869,vit_b_16,GoogLeNet is a type of convolutional neural network based on the Inception architecture,googlenet,0.168459386,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
5870,quantization.mobilenet_v2,adopted from BERT,vit_b_16,0.0,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
5871,efficientnet_b5,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.150608502,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
5872,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.079573621,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
5873,quantization.shufflenet_v2_x1_5,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.222018328,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5874,regnet_x_16gf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.20275156,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5875,inception_v3,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.181117338,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5876,regnet_x_32gf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.109858102,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
5877,resnext101_32x8d,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.127055541,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5878,regnet_x_3_2gf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.076718293,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5879,shufflenet_v2_x0_5,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.114299421,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5880,resnet34,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.235048839,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
5881,video.mc3_18,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.128842199,1x32x32,video classification,Null,TV
5882,vgg16_bn,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.149255173,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
5883,quantization.mobilenet_v3_large,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.21240828,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
5884,quantization.inception_v3,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.099555582,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5885,vgg13_bn,vgg13_bn is used for image classification,vgg13_bn,1.0,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
5886,mnasnet0_5,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.121114111,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5887,vgg13,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.128395673,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
5888,mobilenet_v3_large,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.23259981,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
5889,quantization.shufflenet_v2_x1_5,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.143530646,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5890,regnet_x_1_6gf,"One major difference was the use of RoIAlign layer, instead of RoIPool layer, to avoid pixel level misalignment due to spatial quantization",detection.maskrcnn_resnet50_fpn,0.13270501,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5891,detection.ssdlite320_mobilenet_v3_large,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.164505836,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5892,mobilenet_v3_large,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.16415236,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
5893,regnet_y_1_6gf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.16553399,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5894,regnet_y_400mf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_400mf,1.0,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
5895,inception_v3,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.116686275,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
5896,quantization.googlenet,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.158387464,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5897,quantization.resnext101_32x8d,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.133662773,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5898,wide_resnet50_2,It is the simplest of all other vgg versions,vgg11,0.160538986,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
5899,regnet_y_3_2gf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.176867988,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5900,quantization.shufflenet_v2_x1_0,adopted from BERT,vit_b_16,0.001247584,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5901,segmentation.fcn_resnet101,It is the simplest of all other vgg versions,vgg11,0.126296351,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5902,regnet_x_16gf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.164534964,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5903,quantization.inception_v3,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.144737416,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5904,segmentation.deeplabv3_mobilenet_v3_large,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.191854881,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
5905,googlenet,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.150917103,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
5906,detection.ssdlite320_mobilenet_v3_large,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.15227943,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5907,resnet101,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.132579476,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
5908,efficientnet_b4,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.169813262,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
5909,mnasnet0_75,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.170462156,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
5910,segmentation.fcn_resnet50,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.033298081,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
5911,regnet_y_3_2gf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.165230719,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5912,regnet_x_16gf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.08138428,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
5913,detection.fasterrcnn_resnet50_fpn,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.073422539,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
5914,video.mc3_18,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.195461626,1x32x32,video classification,Null,TV
5915,shufflenet_v2_x0_5,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.174591316,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
5916,wide_resnet50_2,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.214600328,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
5917,regnet_x_1_6gf,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.166208411,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5918,segmentation.fcn_resnet101,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.14931106,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
5919,quantization.shufflenet_v2_x1_5,"It has 11 weight layers in total, so the name VGG11",vgg11,0.180233544,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5920,quantization.inception_v3,Replacing ReLU with GeLU,convnext_tiny,0.081463679,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5921,regnet_x_8gf,"SSD detects smaller objects earlier in the network when the image features are not too crude, while the deeper layers were responsible for offset of the default boxes and aspect ratios",detection.ssd300_vgg16,0.15864226,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5922,segmentation.deeplabv3_resnet50,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.117998954,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5923,resnet34,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.232995708,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
5924,squeezenet1_1,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.212235423,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
5925,video.mc3_18,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.076856763,1x32x32,video classification,Null,TV
5926,mnasnet1_3,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.204000682,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
5927,convnext_small,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.155283984,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5928,regnet_x_1_6gf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.161956421,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
5929,regnet_x_8gf,"ssd300_vgg16 is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.ssd300_vgg16,0.082801741,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
5930,squeezenet1_0,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.190418976,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
5931,resnet50,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.065739358,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
5932,densenet201,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.145432321,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5933,vgg16_bn,RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training,detection.retinanet_resnet50_fpn,0.187777649,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
5934,regnet_y_3_2gf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.103600077,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
5935,quantization.resnet18,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.129385022,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
5936,densenet201,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.198093155,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
5937,mnasnet1_0,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.102556615,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
5938,vit_l_32,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.14274213,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5939,densenet161,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.14334899,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
5940,detection.ssdlite320_mobilenet_v3_large,is a convolutional neural network that is 101 layers deep,resnet101,0.177005338,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5941,quantization.googlenet,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.128505717,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5942,quantization.inception_v3,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.129055338,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5943,resnet152,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.10534384,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5944,vit_b_32,"(2+1)D  convolutional block, which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution",video.r2plus1d_18,0.110385229,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
5945,convnext_large,"This is a model that has been pre-trained on the ImageNet dataset--a dataset that has 100,000+ images across 200 different classes",resnet34,0.152872005,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
5946,resnext50_32x4d,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.138621343,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5947,detection.fasterrcnn_mobilenet_v3_large_fpn,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
5948,resnet18,ResNet-18 is a convolutional neural network that is 18 layers deep,resnet18,1.0,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5949,resnet152,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.150679294,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
5950,convnext_base,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.109665866,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5951,densenet169,"A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other",densenet169,1.0,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
5952,quantization.resnet18,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.184435534,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
5953,detection.keypointrcnn_resnet50_fpn,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.185206163,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
5954,mnasnet0_5,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.119840343,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5955,shufflenet_v2_x1_0,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.199982718,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5956,resnext50_32x4d,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.136580417,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
5957,vgg11_bn,ResNet-152 can obtain 4.49% error rate,resnet152,0.175651435,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
5958,resnet34,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.16080543,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
5959,detection.fasterrcnn_mobilenet_v3_large_fpn,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
5960,regnet_y_1_6gf,"The second potential benefit is that the decomposition facilitates the optimization, yielding in practice both a lower training loss and a lower testing loss",video.r2plus1d_18,0.115378372,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5961,quantization.shufflenet_v2_x1_0,Constructs a ShuffleNetV2 with 1.0x output channels,quantization.shufflenet_v2_x1_0,1.0,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
5962,quantization.inception_v3,"We show that MC ResNets yield roughly a 3-4% gain in clip-level accuracy over 2D ResNets of comparable capacity and they match the performance of 3D ResNets, which have 3 times as many parameters",video.mc3_18,0.17935398,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
5963,alexnet,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.130355841,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5964,resnet18,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",resnet18,1.0,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
5965,regnet_y_800mf,regnet_y_800mf is used for image classification,regnet_y_800mf,1.0,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
5966,segmentation.deeplabv3_resnet50,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.086376314,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
5967,efficientnet_b4,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.095709617,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
5968,convnext_base,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.141001906,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5969,segmentation.deeplabv3_resnet101,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.18759,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
5970,densenet121,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.207488446,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
5971,detection.ssdlite320_mobilenet_v3_large,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.111366961,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
5972,mnasnet0_5,The key building block is an Inception Module,inception_v3,0.181841234,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
5973,resnet34,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.188220822,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
5974,efficientnet_b3,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.179235521,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
5975,vit_l_32,"By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder",vit_l_32,1.0,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5976,regnet_y_1_6gf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.166905735,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
5977,efficientnet_b4,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.179623757,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
5978,shufflenet_v2_x1_0,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.127349377,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5979,video.r2plus1d_18,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.078167909,1x32x32,video classification,Null,TV
5980,vit_l_32,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,1.0,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
5981,resnext101_32x8d,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.226539206,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5982,efficientnet_b3,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.153475039,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
5983,shufflenet_v2_x1_0,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.217164923,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
5984,mnasnet1_0,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.219153324,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
5985,regnet_x_3_2gf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.081093376,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
5986,convnext_small,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_small,1.0,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5987,vgg16_bn,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.16022564,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
5988,regnet_x_800mf,The required minimum input size of the model is 17x17,squeezenet1_1,0.210446599,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
5989,convnext_small,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.082150702,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
5990,convnext_base,"Inception network of googlenet stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid",googlenet,0.168667658,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
5991,quantization.shufflenet_v2_x1_5,"During training, SSD match each ground truth box with the default boxes with the best jaccard overlap and train the network accordingly, similar to Multibox",detection.ssd300_vgg16,0.18913745,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
5992,segmentation.lraspp_mobilenet_v3_large,lraspp_mobilenet_v3_large is used for semantic segmentation,segmentation.lraspp_mobilenet_v3_large,1.0,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
5993,efficientnet_b6,"VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 fully connected layer, 5 MaxPool layers and 1 SoftMax layer)",vgg19,0.232242033,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
5994,resnext101_32x8d,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.136100139,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
5995,vgg19_bn,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.120623679,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
5996,mobilenet_v3_large,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.145954406,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
5997,alexnet,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.150577938,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
5998,quantization.googlenet,Replacing ReLU with GeLU,convnext_tiny,0.056136904,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
5999,segmentation.deeplabv3_mobilenet_v3_large,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.126601923,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
6000,resnext50_32x4d,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.219012264,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
6001,detection.fasterrcnn_mobilenet_v3_large_fpn,"The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions",detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
6002,detection.retinanet_resnet50_fpn,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.041136065,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
6003,inception_v3,Relative fast due to no fully connected layers or a refinement block,segmentation.fcn_resnet50,0.13392016,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
6004,regnet_x_8gf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.153041762,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
6005,quantization.inception_v3,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.189605804,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
6006,regnet_x_8gf,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.121105626,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
6007,video.mc3_18,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.171894386,1x32x32,video classification,Null,TV
6008,vit_b_16,Constructs a RegNetX_800MF architecture,regnet_x_800mf,0.145546791,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
6009,quantization.resnet50,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.161222726,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
6010,densenet161,The model output for densenet-201 is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database,densenet161,1.0,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
6011,resnet18,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.101634317,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
6012,convnext_tiny,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.094357718,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
6013,regnet_y_16gf,The required minimum input size of the model is 32x32,vgg11,0.184969877,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
6014,regnet_y_400mf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.119032287,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
6015,detection.keypointrcnn_resnet50_fpn,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.17850796,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
6016,shufflenet_v2_x2_0,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.14901475,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
6017,convnext_tiny,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.074069194,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
6018,mnasnet0_75,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.093225698,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
6019,efficientnet_b4,"It has 11 weight layers in total, so the name VGG11",vgg11,0.194955972,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
6020,segmentation.fcn_resnet101,"The authors demonstrate its efficacy with the help of a simple, single stage detector, called RetinaNet, which predicts objects by dense sampling of the input image in location, scale and aspect ratio",detection.retinanet_resnet50_fpn,0.219652557,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
6021,vgg16,The key building block is an Inception Module,inception_v3,0.171820931,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
6022,regnet_y_16gf,Constructs a RegNetY_16GF architecture,regnet_y_16gf,1.0,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
6023,regnet_x_16gf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.070360377,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
6024,resnet34,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.135043842,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
6025,vgg11,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.118168381,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
6026,segmentation.lraspp_mobilenet_v3_large,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.204583369,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
6027,quantization.inception_v3,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,1.0,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
6028,regnet_x_16gf,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.21330736,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
6029,efficientnet_b4,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.220388818,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
6030,quantization.shufflenet_v2_x1_5,"It builds upon ShuffleNet v1, which utilised pointwise group convolutions, bottleneck-like structures, and a channel shuffle operation",quantization.shufflenet_v2_x1_5,1.0,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
6031,quantization.inception_v3,"One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image",vit_b_16,0.199911654,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
6032,mnasnet1_3,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.234771694,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
6033,efficientnet_b7,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.150261142,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
6034,detection.ssd300_vgg16,This issue was later solved by using better backbone architectures like ResNet and other small fixes,detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
6035,quantization.googlenet,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.167595505,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
6036,densenet161,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.204585499,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
6037,densenet161,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.15449329,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
6038,vgg19,ResNet-152 can obtain 4.49% error rate,resnet152,0.206767301,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
6039,convnext_large,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.126278224,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
6040,wide_resnet101_2,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.150850491,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
6041,detection.retinanet_resnet50_fpn,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.060861068,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
6042,efficientnet_b7,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.156279586,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
6043,regnet_y_400mf,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.164135573,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
6044,wide_resnet101_2,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.08664004,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
6045,mnasnet1_3,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.185422614,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
6046,squeezenet1_0,It is the simplest of all other vgg versions,vgg11,0.143292086,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
6047,resnet101,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.054848609,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
6048,vit_b_16,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.111394449,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
6049,resnet18,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.167263518,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
6050,vgg16_bn,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.188234858,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
6051,resnext50_32x4d,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.101793444,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
6052,regnet_y_16gf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.172825953,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
6053,segmentation.deeplabv3_resnet50,The required minimum input size of the model is 32x32,vgg11,0.187535974,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
6054,resnext101_32x8d,It is the simplest of all other vgg versions,vgg11,0.145866606,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
6055,segmentation.fcn_resnet50,Constructs a Fully-Convolutional Network model with a ResNet-50 backbone,segmentation.fcn_resnet50,1.0,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
6056,vit_b_16,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.105534876,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
6057,quantization.shufflenet_v2_x0_5,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.186310682,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
6058,resnet18,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.122143055,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
6059,efficientnet_b3,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.178828639,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
6060,regnet_y_32gf,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.117785034,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
6061,segmentation.deeplabv3_resnet101,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.099532859,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
6062,wide_resnet101_2,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.184381674,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
6063,mnasnet0_5,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.144931254,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
6064,vgg13,"Even though SSD was significantly faster and more accurate than both state-of-art networks like YOLO and Faster R-CNN, it had difficulty in detecting small objects",detection.ssd300_vgg16,0.225371592,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
6065,mnasnet1_3,The number of parameters is 3.4 million,mobilenet_v2,0.118419846,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
6066,convnext_large,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.122630964,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
6067,detection.retinanet_resnet50_fpn,"The squeeze layer and expand layer keep the same feature map size, while the former reduce the depth to a smaller number, the later increase it",squeezenet1_0,0.238752849,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
6068,vit_b_32,Constructs a RegNetX_8GF architecture,regnet_x_8gf,0.167977959,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
6069,inception_v3,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.154278555,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
6070,vgg13,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.10331155,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
6071,segmentation.deeplabv3_mobilenet_v3_large,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.18279758,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
6072,regnet_x_3_2gf,Replacing ReLU with GeLU,convnext_tiny,0.137662395,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
6073,segmentation.fcn_resnet101,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.185994018,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
6074,vit_l_32,"Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet)",squeezenet1_0,0.152734113,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
6075,efficientnet_b1,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b1,1.0,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
6076,segmentation.deeplabv3_resnet50,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.10898663,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
6077,regnet_x_8gf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_x_8gf,1.0,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
6078,wide_resnet101_2,The required minimum input size of the model is 17x17,squeezenet1_1,0.200156932,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
6079,convnext_large,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.074154508,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
6080,shufflenet_v2_x1_5,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.125554262,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
6081,regnet_y_1_6gf,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.162431964,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
6082,shufflenet_v2_x1_5,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.11785427,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
6083,detection.retinanet_resnet50_fpn,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.216371814,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
6084,wide_resnet101_2,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.240299126,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
6085,squeezenet1_0,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.157019922,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
6086,vgg11,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.225895741,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
6087,resnext101_32x8d,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.120532729,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
6088,vit_l_16,"Using the DenseNet-121 architecture to understand the table, we can see that every dense block has varying number of layers (repetitions) featuring two convolutions each; a 1x1 sized kernel as the bottleneck layer and 3x3 kernel to perform the convolution operation",densenet121,0.162781009,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
6089,detection.keypointrcnn_resnet50_fpn,2D max pooling in between the weight layers,vgg11,0.115309072,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
6090,resnext101_32x8d,resnext101_32x8d is used for image classification,resnext101_32x8d,1.0,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
6091,regnet_x_1_6gf,The required minimum input size of the model is 21x21,squeezenet1_0,0.210229094,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
6092,quantization.shufflenet_v2_x1_0,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.128837011,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
6093,densenet169,The new ConvNet which is dubbed as ConvNext does not only have higher accuracy as compared to their vision transformer counterparts but is also more scalable.,convnext_tiny,0.207297214,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
6094,googlenet,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.160147502,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
6095,efficientnet_b6,"For each ground truth box, SSD selects the best default box that most overlap with this specific ground truth box by finding the one that has the highest IoU (intersection over union), also called the Jaccard index.",detection.ssd300_vgg16,0.14517968,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
6096,regnet_y_16gf,adopted from BERT,vit_b_16,0.015236046,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
6097,resnext101_32x8d,Note that this model does not include any fine-tuned heads,vit_b_16,0.170783484,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
6098,shufflenet_v2_x1_5,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.150368484,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
6099,regnet_y_400mf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.183787952,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
6100,segmentation.deeplabv3_resnet50,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.18464968,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
6101,quantization.shufflenet_v2_x2_0,adopted from BERT,vit_b_16,0.000836525,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
6102,segmentation.lraspp_mobilenet_v3_large,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.169078627,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
6103,vgg19_bn,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.122693183,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
6104,quantization.shufflenet_v2_x0_5,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.115183664,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
6105,quantization.resnet50,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.219884114,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
6106,convnext_large,Constructs a RegNetY_3.2GF architecture,regnet_y_3_2gf,0.18048964,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
6107,densenet169,adopted from BERT,vit_b_16,0.0,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
6108,quantization.inception_v3,It is the simplest of all other vgg versions,vgg11,0.188461165,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
6109,convnext_large,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.135234327,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
6110,segmentation.deeplabv3_resnet50,The key building block is an Inception Module,inception_v3,0.118018247,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
6111,quantization.resnext101_32x8d,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.097770834,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
6112,video.r3d_18,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.10788089,1x32x32,video classification,Null,TV
6113,detection.maskrcnn_resnet50_fpn,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.083291189,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
6114,segmentation.deeplabv3_mobilenet_v3_large,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.120928181,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
6115,vit_l_16,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
6116,vgg13_bn,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.15745739,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
6117,segmentation.deeplabv3_resnet101,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.082516001,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
6118,wide_resnet50_2,"Because the main focus of the two papers was to introduce novel CNN architectures, most of the implementation details of SSDlite were not clarified",detection.ssdlite320_mobilenet_v3_large,0.223536092,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
6119,regnet_x_3_2gf,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_x_3_2gf,1.0,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
6120,convnext_tiny,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.193697874,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
6121,vit_l_32,They stack residual blocks on top of each other to form network: for example  a ResNet-101 has 101 layers using these blocks,resnet101,0.124475382,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
6122,efficientnet_b5,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.175570011,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
6123,regnet_x_16gf,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.180150828,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
6124,convnext_small,MobileNet v3-Large has 5.4 million parameters,mobilenet_v3_large,0.133254025,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
6125,segmentation.fcn_resnet50,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.142594885,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
6126,convnext_small,The SSDlite is an adaptation of SSD which was first briefly introduced on the MobileNetV2 paper and later reused on the MobileNetV3 paper,detection.ssdlite320_mobilenet_v3_large,0.103625223,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
6127,segmentation.fcn_resnet50,LR-ASPP is a simplified version of the Reduced Atrous Spatial Pyramid Pooling model proposed by the author of the MobileNetV3 paper,segmentation.lraspp_mobilenet_v3_large,0.245988597,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
6128,efficientnet_b5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.223514821,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
6129,mnasnet0_5,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.220922853,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
6130,quantization.shufflenet_v2_x2_0,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.124555858,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
6131,quantization.mobilenet_v3_large,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.179339343,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
6132,detection.retinanet_resnet50_fpn,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.222163688,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
6133,resnet34,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.104184449,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
6134,detection.fasterrcnn_resnet50_fpn,Faster R-CNN is an object detection model that improves on Fast R-CNN by utilising a region proposal network (RPN) with the CNN model,detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
6135,regnet_y_3_2gf,adopted from BERT,vit_b_16,0.014049528,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
6136,regnet_y_8gf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.163565256,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
6137,vgg11_bn,It is the simplest of all other vgg versions,vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
6138,segmentation.deeplabv3_resnet101,vit_l_32 is used for image classification,vit_l_32,0.140647667,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
6139,vit_b_16,"The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels",vit_b_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
6140,regnet_x_16gf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.145070824,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
6141,efficientnet_b3,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.201643753,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
6142,vgg13_bn,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.086440922,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
6143,mnasnet0_75,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.188600401,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
6144,mobilenet_v3_small,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.125770638,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
6145,segmentation.fcn_resnet50,vit_l_16 is used for image classification,vit_l_16,0.138206279,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
6146,efficientnet_b5,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.18439617,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
6147,vit_l_32,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.10971826,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
6148,efficientnet_b3,obtained by scaling up the baseline network,efficientnet_b3,1.0,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
6149,vgg13_bn,"Differences are shown in the model Figure, including a new channel split operation and moving the channel shuffle operation further down the block",shufflenet_v2_x0_5,0.13549463,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
6150,mnasnet0_5,It is the simplest of all other vgg versions,vgg11,0.168144388,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
6151,mobilenet_v3_small,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.1275429,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
6152,mnasnet0_5,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.079625497,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
6153,vit_b_16,"EfficientNet-B7 achieves new state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy, while being 8.4x smaller than the best existing CNN",efficientnet_b7,0.157037655,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
6154,regnet_y_800mf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.099558073,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
6155,regnet_y_32gf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.136673897,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
6156,alexnet,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.21798567,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
6157,regnet_x_3_2gf,"DeepLabv3 outperforms DeepLabv1 and DeepLabv2, even with the post-processing step Conditional Random Field (CRF) removed, which is originally used in DeepLabv1 and DeepLabv2",segmentation.deeplabv3_resnet50,0.145452429,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
6158,inception_v3,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.199704203,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
6159,video.mc3_18,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.069204931,1x32x32,video classification,Null,TV
6160,vit_l_32,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.133288026,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
6161,detection.retinanet_resnet50_fpn,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.129225699,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
6162,shufflenet_v2_x1_0,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.140025354,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
6163,regnet_x_8gf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.113210681,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
6164,vit_b_32,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.159715591,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
6165,alexnet,It was designed to classify images,alexnet,1.0,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
6166,detection.fasterrcnn_mobilenet_v3_large_fpn,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.109158621,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
6167,shufflenet_v2_x2_0,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.163249583,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
6168,convnext_tiny,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.180305272,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
6169,regnet_y_8gf,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.211727077,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
6170,mobilenet_v3_large,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.189243896,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
6171,detection.fasterrcnn_mobilenet_v3_large_fpn,The number of parameters is 3.4 million,mobilenet_v2,0.099562915,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
6172,regnet_x_16gf,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.205592055,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
6173,quantization.shufflenet_v2_x1_5,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.164444696,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
6174,resnext50_32x4d,adopted from BERT,vit_b_16,0.0,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
6175,segmentation.deeplabv3_mobilenet_v3_large,"Rather than using pyramids of images (  multiple instances of the image but at different scales) or pyramids of filters (  multiple filters with different sizes), this method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.168103729,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
6176,resnet34,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.096559358,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
6177,convnext_small,Constructs a DeepLabV3 model with a ResNet-101 backbone,segmentation.deeplabv3_resnet101,0.210542973,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
6178,convnext_base,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.04890397,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
6179,regnet_x_8gf,The required minimum input size of the model is 32x32,vgg11,0.179276106,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
6180,regnet_y_8gf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.11052288,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
6181,shufflenet_v2_x1_5,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.166004136,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
6182,resnet34,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.137174802,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
6183,detection.fasterrcnn_mobilenet_v3_large_320_fpn,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.073375,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
6184,vit_b_16,This issue was later solved by using better backbone architectures like ResNet and other small fixes,detection.ssd300_vgg16,0.10953694,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
6185,detection.fasterrcnn_mobilenet_v3_large_fpn,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.164026605,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
6186,vgg11_bn,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.218409007,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
6187,inception_v3,densenet169 is More acurate than DenseNet-121,densenet169,0.145994165,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
6188,quantization.shufflenet_v2_x1_0,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.138438892,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
6189,regnet_y_1_6gf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.108031505,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
6190,quantization.inception_v3,adopted from BERT,vit_b_16,0.008796611,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
6191,shufflenet_v2_x1_0,Replacing ReLU with GeLU,convnext_tiny,0.095965976,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
6192,efficientnet_b0,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.151168656,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
6193,regnet_y_400mf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.167937338,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
6194,quantization.shufflenet_v2_x1_0,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.132672384,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
6195,detection.keypointrcnn_resnet50_fpn,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.11557329,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
6196,inception_v3,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.183721048,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
6197,vit_l_32,"A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other",densenet121,0.173655242,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
6198,densenet169,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.148917923,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
6199,vit_l_16,ResNet-152 can obtain 4.49% error rate,resnet152,0.099848652,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
6200,vgg11_bn,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.178066342,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
6201,detection.maskrcnn_resnet50_fpn,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.070126844,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
6202,regnet_y_3_2gf,SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location,detection.ssd300_vgg16,0.181019598,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
6203,resnext50_32x4d,It is the simplest of all other vgg versions,vgg11,0.136368331,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
6204,regnet_x_16gf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.160350377,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
6205,mnasnet0_75,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.208266988,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
6206,segmentation.fcn_resnet50,"Unlike other segmentation models in , LR-ASPP does not use auxiliary loss, but uses low-level and high-level features, with output steps of 8 and 16, respectively",segmentation.lraspp_mobilenet_v3_large,0.197951593,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
6207,convnext_tiny,"Dense blocks, Batch Normalization(BN)",densenet121,0.100665169,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
6208,detection.ssdlite320_mobilenet_v3_large,ResNet Mixed Convolution is a variation of ResNet 3D is a type of model for video that employs 3D convolutions,video.mc3_18,0.203139524,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
6209,regnet_y_16gf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_16gf,1.0,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
6210,vit_b_32,"mixed convolution (MC) and consists in employing 3D convolutions only in the early layers of the network, with 2D convolutions in the top layers",video.mc3_18,0.144476014,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
6211,vgg19_bn,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
6212,densenet161,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.154572316,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
6213,vgg11_bn,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.126811119,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
6214,regnet_x_800mf,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.047469182,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
6215,convnext_large,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.046383906,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
6216,google/t5-efficient-tiny-nl24,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0695330444723367,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
6217,google/t5-efficient-tiny-nl32,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0950343757867812,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
6218,malay-huggingface/xlnet-base-bahasa-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0124269152681033,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
6219,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0045264959335327,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
6220,IDEA-CCNL/Erlangshen-Longformer-110M,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0341224893927574,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
6221,google/t5-efficient-small-dm768,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0243102777749299,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
6222,dbmdz/convbert-base-german-europeana-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
6223,1one5ome/gpt2-chinese-gulong,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0642954856157302,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
6224,google/t5-efficient-base-kv32,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0614074245095252,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
6225,DemangeJeremy/4-sentiments-with-flaubert,"This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens",macedonizer/al-roberta-base,0.0822926200926303,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
6226,google/t5-efficient-tiny-nl12,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.072121575474739,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
6227,lgrobol/electra-minuscule-generator,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0262981848791241,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
6228,google/t5-efficient-small-nl22,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0021021533757447,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
6229,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
6230,TJMUCH/transcriptome-iseeek,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0346232540905475,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
6231,mrm8488/xlm-multi-finetuned-xquadv1,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.053960071876645,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
6232,eli4s/Bert-L12-h256-A4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0335846953094005,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
6233,hf-internal-testing/tiny-random-rembert,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0822351053357124,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
6234,IDEA-CCNL/Erlangshen-Longformer-110M,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0002296864986419,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
6235,hfl/rbtl3,T5-Efficient-SMALL-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl40,0.0553291244432329,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
6236,microsoft/deberta-v2-xxlarge-mnli,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0516299093142151,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
6237,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0377075523138046,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
6238,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0787170454859733,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
6239,ToToKr/kobigbird-bert-base-finetuned-klue,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0491771325469017,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
6240,saibo/random-roberta-tiny,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0148931834846734,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
6241,google/t5-efficient-small-kv128,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0603902488946914,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
6242,M-FAC/bert-mini-finetuned-stsb,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
6243,google/t5-efficient-xl-nl4,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0887951701879501,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
6244,jkeruotis/LitBERTa-uncased,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,1.0,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
6245,google/t5-efficient-large-el6,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0233977446332574,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
6246,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0258460926512876,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
6247,stas/t5-very-small-random,"Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context",xlnet-large-cased,0.0427994690835475,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
6248,google/t5-efficient-large-nh32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0009069144725799,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
6249,lgrobol/electra-minuscule-discriminator,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
6250,stas/t5-very-small-random,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0395887345075607,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
6251,phueb/BabyBERTa-2,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,1.0,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
6252,mimi/Waynehills-NLP-doogie,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0441374853253364,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
6253,aditeyabaral/sentencetransformer-bert-hinglish-small,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-mega,0.0929510295391082,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
6254,abdusahmbzuai/aradia-ctc-distilhubert-ft,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0784560665488243,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
6255,google/bert_uncased_L-10_H-128_A-2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
6256,google/t5-efficient-mini,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0262514436617493,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
6257,rinna/japanese-gpt2-xsmall,This is a sentence-transformers model: It maps sentences and paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search,piotr-rybak/poleval2021-task4-herbert-large-encoder,0.0789978876709937,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
6258,GKLMIP/electra-myanmar-small-uncased,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0658217500895261,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
6259,google/t5-efficient-base-kv128,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0462739178910851,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
6260,google/t5-efficient-small-dm768,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0537320040166377,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
6261,inokufu/flaubert-base-uncased-xnli-sts,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
6262,IDEA-CCNL/Erlangshen-Longformer-110M,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0796432867646216,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
6263,google/t5-efficient-tiny-nh32,This does not make a very readable text,pere/DeUnCaser,0.0762790367007255,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
6264,crabz/distil-slovakbert-ner,Its original repository can be found here,tau/splinter-large-qass,0.0320856198668479,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
6265,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0288179740309715,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
6266,M-FAC/bert-mini-finetuned-stsb,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0774331036955118,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
6267,google/t5-efficient-base-nh24,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0112946406006813,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
6268,fnlp/bart-base-chinese,"This model has been fine-tuned by the HuggingSound tool, and thanks to the GPU credits generously given by the OVHcloud :)Using the HuggingSound library:",jonatasgrosman/wav2vec2-xls-r-1b-english,0.0787153989076614,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
6269,razent/SciFive-base-PMC,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
6270,google/t5-efficient-large-dm256,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0249652480706572,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
6271,MoritzLaurer/MiniLM-L6-mnli,Its original repository can be found here,tau/splinter-large-qass,0.0391845864554246,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
6272,1one5ome/gpt2-chinese-gulong,Evaulation results will be released soon,lassl/gpt2-ko-small,0.042378582060337,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
6273,google/t5-efficient-tiny-el8,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
6274,microsoft/unispeech-sat-base,Check out this blog for more in-detail explanation of how to fine-tune the model,microsoft/unispeech-sat-base,1.0,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
6275,mrm8488/chEMBL_smiles_v1,An approximation to Generative Recurrent Networks for De Novo Drug Design but training a MLM (RoBERTa like) from scratch,mrm8488/chEMBL_smiles_v1,1.0,-1.0,NLP,919d68f1-5654-4b4c-a9c7-f59f67ababe7___chEMBL_smiles_v1,HF
6276,aditeyabaral/sentencetransformer-bert-hinglish-small,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0708710476756095,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
6277,google/t5-efficient-mini,This does not make a very readable text,pere/DeUnCaser,0.0510592376813292,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
6278,rahulacj/bertweet-base-finetuned-sentiment-analysis,py (Download Here) that define the architecture of CPT into your project,fnlp/cpt-base,0.0909787341952323,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
6279,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
6280,mrm8488/deberta-v3-small-finetuned-mrpc,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0788425356149673,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
6281,superspray/electra_large_discriminator_squad2_custom_dataset,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
6282,piotr-rybak/poleval2021-task4-herbert-large-encoder,Both pytorch(pytorch_model bin) and Rust(rust_modelot) models are providedNOTE: Use T5Tokenizer to initiate the tokenizer,jweb/japanese-soseki-gpt2-1b,0.0746556147933006,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
6283,google/t5-efficient-tiny-el8,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0323360078036784,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
6284,google/t5-efficient-base-el16,This does not make a very readable text,pere/DeUnCaser,0.0384344328194856,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
6285,google/t5-efficient-base-nl48,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0528254359960555,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
6286,GKLMIP/electra-myanmar-small-uncased,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
6287,google/t5-large-ssm-nq,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,1.0,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
6288,google/t5-efficient-base-dm2000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0022495612502098,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
6289,hfl/rbt3,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0787255540490149,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
6290,google/t5-efficient-xl-nl8,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0685236230492591,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
6291,DemangeJeremy/4-sentiments-with-flaubert,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0723685845732688,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
6292,google/bigbird-roberta-large,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0050469651818275,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
6293,diper1998/distilgpt2-finetuned-AT,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0549775809049606,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
6294,google/t5-efficient-mini-nl8,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0356101877987384,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
6295,FremyCompany/xls-r-nl-v1-cv8-lm,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0180930737406015,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
6296,fnlp/cpt-base,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
6297,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0705373212695121,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
6298,mimicheng/codeparrot-ds,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.078641802072525,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
6299,dbmdz/bert-medium-historic-multilingual-cased,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0785224884748458,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
6300,amazon/bort,Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer,flaubert/flaubert_small_cased,0.0011043697595596,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
6301,mrp/simcse-model-wangchanberta,Amazon's BORT,amazon/bort,0.0777503438293933,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
6302,nherve/flaubert-oral-asr,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
6303,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv128,0.0985776185989379,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
6304,superspray/electra_large_discriminator_squad2_custom_dataset,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.0970642492175102,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
6305,mwesner/bart-mlm,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0600588917732238,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
6306,google/t5-efficient-large-el2,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.046288102865219,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
6307,IDEA-CCNL/Erlangshen-Longformer-330M,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0896321758627891,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
6308,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,1.0,-1.0,NLP,6493ad33-df30-4985-946f-a59823df46ee___wav2vec2-large-robust-12-ft-emotion-msp-dim,HF
6309,google/t5-efficient-small-nl2,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0720208976417779,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
6310,google/t5-efficient-large-el6,T5-Efficient-LARGE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el6,1.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
6311,hfl/chinese-electra-180g-small-ex-discriminator,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0476264047125975,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
6312,google/t5-efficient-xl-nl6,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0067766085267066,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
6313,google/t5-efficient-base-nl4,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0072053151670843,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
6314,IDEA-CCNL/Zhouwenwang-Unified-1.3B,"In the pre-training, the model considers LM (Language Model) and MLM (Mask Language Model) tasks uniformly, and adds rotational position coding, so that the model has the ability to generate and understand",IDEA-CCNL/Zhouwenwang-Unified-1.3B,1.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
6315,negfir/distilbert-base-uncased-finetuned-cola,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0795697420835495,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
6316,stas/t5-very-small-random,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0998975783586502,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
6317,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.088239274919033,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
6318,rdenadai/BR_BERTo,T5-Efficient-BASE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv32,0.0234306876858075,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
6319,KoichiYasuoka/roberta-base-ukrainian,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0007275231182575,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
6320,google/t5-efficient-small-ff12000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0522969830781221,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
6321,gilparmentier/pokemon_gptj_model,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
6322,mnaylor/bigbird-base-mimic-mortality,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0223374706692993,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
6323,Fujitsu/pytorrent,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0493356601024667,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
6324,hajime9652/xlnet-japanese,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0929394513368606,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
6325,minimaxir/magic-the-gathering,Most language models are imprecise and Rockbot is no exception,bigjoedata/rockbot,0.0421062903478741,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
6326,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0726024508476257,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
6327,hfl/chinese-electra-180g-small-ex-discriminator,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0558131368209918,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
6328,google/t5-efficient-large-nl16,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0397292282432317,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
6329,google/t5-efficient-large-kv16,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0527059957385062,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
6330,khizon/distilbert-unreliable-news-eng-4L,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.048893071245402,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
6331,kyryl0s/gpt2-uk-xxs,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0407880605198442,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
6332,skplanet/dialog-koelectra-small-generator,ELECTRA is a method for self-supervised language representation learning,skplanet/dialog-koelectra-small-generator,1.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
6333,patrickvonplaten/unispeech-sat-large-timit-ft,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0070547610521316,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
6334,mimi/Waynehills-NLP-doogie,† ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-162M,0.0860102325677871,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
6335,lannelin/bert-imdb-1hidden,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
6336,tau/tavbert-he,"For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github",sarnikowski/convbert-medium-small-da-cased,0.0189114511013031,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6337,microsoft/unihanlm-base,"We exploit Unihan, a ready-made database constructed by linguistic experts to first merge morphologically similar characters into clusters",microsoft/unihanlm-base,1.0,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
6338,hfl/rbtl3,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.062917772680521,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
6339,piotr-rybak/poleval2021-task4-herbert-large-encoder,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0724841579794883,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
6340,google/byt5-large,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
6341,sentence-transformers/paraphrase-MiniLM-L3-v2,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
6342,VietAI/gpt-neo-1.3B-vietnamese-news,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0485159559175371,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
6343,gilparmentier/pokemon_gptj_model,It was introduced in this paper,readerbench/RoBERT-small,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
6344,google/t5-efficient-small-kv256,Its original repository can be found here,tau/splinter-large-qass,0.0647611636668443,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
6345,microsoft/layoutlm-base-uncased,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0988756418228149,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
6346,google/t5-efficient-base-ff6000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0222348142415285,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
6347,google/t5-efficient-xl-nl28,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0262583959847688,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
6348,google/t5-efficient-base-dm1000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0356625961139797,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
6349,sentence-transformers/paraphrase-MiniLM-L3-v2,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0033792816102504,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
6350,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm1000,0.0555867006381352,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
6351,google/t5-efficient-base-el6,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0377493854612111,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
6352,google/t5-efficient-mini,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0613829176872968,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
6353,microsoft/layoutlm-base-cased,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.083603411912918,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
6354,google/t5-efficient-tiny-ff3000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0592703241854905,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
6355,microsoft/deberta-v2-xxlarge-mnli,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0500797480344771,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
6356,ErykWdowiak/GPTalian,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
6357,stas/pegasus-cnn_dailymail-tiny-random,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0567586347460746,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
6358,malay-huggingface/t5-super-tiny-bahasa-cased,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0645134188234805,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
6359,mgreenbe/bertlet-base-uncased-for-sequence-classification,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
6360,google/t5-efficient-small-el16-dl4,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.055933728814125,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
6361,KoichiYasuoka/roberta-base-ukrainian,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,0.090909942984581,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
6362,ncduy/phobert-large-finetuned-vietnamese_students_feedback,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.081921711564064,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
6363,google/t5-efficient-small-dm768,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0761263333261012,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
6364,anas-awadalla/bert-tiny-finetuned-squad,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0189164020121097,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
6365,TJMUCH/transcriptome-iseeek,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
6366,google/t5-efficient-base-ff12000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
6367,google/t5-efficient-large-nl36,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0381046012043952,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
6368,google/t5-efficient-large-nl12,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0370564833283424,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
6369,google/t5-efficient-base-el16,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
6370,microsoft/layoutlm-base-cased,It is a two-stage approach consisting of a generator and a scorer,andrejmiscic/simcls-scorer-billsum,0.0665763467550277,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
6371,mrm8488/deberta-v3-small-finetuned-mrpc,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0823876783251762,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
6372,thu-coai/LongLM-large,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
6373,malay-huggingface/xlnet-base-bahasa-cased,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0641704065104325,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
6374,filco306/gpt2-switchboard-paraphraser,"If you found this model useful, please cite the original work:",filco306/gpt2-switchboard-paraphraser,1.0,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
6375,mrm8488/deberta-v3-small-finetuned-mrpc,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0346103459596633,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
6376,negfir/distilbert-base-uncased-finetuned-cola,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0861373320221901,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
6377,monsoon-nlp/dv-muril,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0177373091379801,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
6378,hfl/chinese-legal-electra-base-generator,"For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA",hfl/chinese-legal-electra-base-generator,1.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
6379,skt/ko-gpt-trinity-1.2B-v0.5,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
6380,google/t5-efficient-small-nl2,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0488041481003165,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
6381,stas/mt5-tiny-random,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0406742058694362,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
6382,stas/pegasus-cnn_dailymail-tiny-random,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
6383,google/t5-efficient-base-nl40,This does not make a very readable text,pere/DeUnCaser,0.0337918289005756,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
6384,google/t5-efficient-large-el4,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
6385,google/t5-efficient-large-kv256,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0258500408381223,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
6386,google/t5-efficient-xl-nl2,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0482715051621198,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
6387,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0567379556596278,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
6388,google/t5-efficient-xl-nl28,T5-Efficient-XL-NL28 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl28,1.0,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
6389,rinna/japanese-gpt-1b,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
6390,stas/t5-very-small-random,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
6391,patrickvonplaten/unispeech-sat-large-timit-ft,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
6392,google/t5-efficient-large-nh8-nl32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0232856906950473,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
6393,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0922915637493133,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
6394,aliosm/ComVE-gpt2-large,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.023528249701485,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
6395,VietAI/gpt-neo-1.3B-vietnamese-news,ProtBert is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion,Rostlab/prot_bert,0.0682309493422507,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
6396,google/t5-efficient-large-nh2,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0251871701329946,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
6397,nherve/flaubert-oral-asr,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.055366131166617,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
6398,google/t5-efficient-base-kv128,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0503880064934491,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
6399,google/t5-efficient-xl-nl16,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0357503108680248,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
6400,kssteven/ibert-roberta-base,", quantization-aware training) of the quantized model",kssteven/ibert-roberta-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
6401,ian0delond/model-test,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0677045211195945,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
6402,google/t5-efficient-small-ff12000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0431312080472707,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
6403,tau/tavbert-he,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,0.0398414758965373,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6404,tartuNLP/gpt-4-est-large,Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering,tau/splinter-base-qass,0.085873931646347,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
6405,ToToKr/kobigbird-bert-base-finetuned-klue,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0318524911999702,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
6406,google/bert_uncased_L-8_H-128_A-2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0110978856682777,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
6407,IDEA-CCNL/Erlangshen-Longformer-330M,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0829722955822944,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
6408,1one5ome/gpt2-chinese-gulong,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0223768092691898,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
6409,google/t5-efficient-small-el16-dl4,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0954257175326347,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
6410,cross-encoder/nli-deberta-v3-xsmall,net - Pretrained Cross-Encoder,cross-encoder/nli-deberta-v3-xsmall,1.0,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
6411,google/t5-efficient-small-nl24,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0074264984577894,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
6412,negfir/distilbert-base-uncased-finetuned-squad,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
6413,asi/gpt-fr-cased-base,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
6414,google/t5-efficient-base-nh32,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
6415,facebook/mbart-large-50-many-to-one-mmt,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,1.0,-1.0,NLP,1aed0c2f-c6c7-49df-87c0-c3bebcdb63af___mbart-large-50-many-to-one-mmt,HF
6416,rinna/japanese-gpt2-xsmall,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.021559715270996,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
6417,google/t5-efficient-xxl-nl4,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0483533665537833,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
6418,negfir/distilbert-base-uncased-finetuned-cola,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0449165701866149,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
6419,google/t5-efficient-base-dm512,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0091059934347868,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
6420,tuner007/pegasus_qa,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0884657129645347,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
6421,bookbot/distil-wav2vec2-adult-child-cls-52m,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0106764944891134,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
6422,KBLab/megatron-bert-base-swedish-cased-600k,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
6423,google/t5-efficient-large-kv32,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0799249447882175,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
6424,google/t5-efficient-small-el4,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0496648605912923,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
6425,google/bert_uncased_L-8_H-256_A-4,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0280540433401862,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
6426,google/t5-efficient-large-kv16,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.054220100864768,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
6427,hf-internal-testing/tiny-random-rembert,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0897867977619171,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
6428,google/bert_uncased_L-8_H-128_A-2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.012903448815147,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
6429,google/t5-efficient-tiny-nh1,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0671235769987105,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
6430,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0860189050436019,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
6431,tau/tavbert-he,The details of our training data are shown as follows,TsinghuaAI/CPM-Generate,0.0551278758794069,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6432,google/t5-efficient-base-ff6000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0541695337742566,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
6433,stas/t5-very-small-random,"Organization of Language Understanding Evaluation benchmark for Chinese: tasks and datasets, baselines, pre-trained Chinese models, corpus and leaderboard",clue/xlnet_chinese_large,0.0966956913471221,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
6434,google/bert_uncased_L-6_H-256_A-4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
6435,google/t5-efficient-tiny-nh32,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0560964085161685,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
6436,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0998425930738449,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
6437,SupriyaArun/squeezebert-uncased-finetuned-squad,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
6438,Milos/slovak-gpt-j-405M,Splinter-large is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021),tau/splinter-large-qass,0.0440447106957435,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
6439,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0753321945667266,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
6440,google/t5-efficient-large-nl16,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0299569927155971,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
6441,clue/roberta_chinese_3L312_clue_tiny,"Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text)",pszemraj/led-large-book-summary,0.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
6442,anirudh21/xlnet-base-cased-finetuned-rte,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0991344973444938,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
6443,malay-huggingface/xlnet-base-bahasa-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0686320637663204,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
6444,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0846414789557457,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
6445,microsoft/layoutlm-large-uncased,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0767787322402,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
6446,rinna/japanese-gpt2-xsmall,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0433597657829522,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
6447,google/t5-efficient-tiny-nl6,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0673842020332813,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
6448,tartuNLP/gpt-4-est-base,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0909481160342693,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
6449,cross-encoder/stsb-TinyBERT-L-4,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0327762304805219,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
6450,google/t5-efficient-small-ff6000,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0835382118821144,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
6451,google/t5-efficient-large-nh32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0509994775056838,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
6452,google/t5-efficient-base-dm512,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0472791334614157,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
6453,nguyenvulebinh/envibert,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0197762101888656,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
6454,google/t5-efficient-large-nl32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
6455,tobiaslee/bert-2l-768h-uncased,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0184087119996548,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
6456,aubmindlab/aragpt2-large,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
6457,microsoft/layoutlm-base-uncased,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0037249252200126,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
6458,google/t5-efficient-large-el6,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0596104450523853,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
6459,IDEA-CCNL/Wenzhong-GPT2-3.5B,Our model performs well in Chinese continuation generation,IDEA-CCNL/Wenzhong-GPT2-3.5B,1.0,-1.0,NLP,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
6460,google/t5-efficient-large-kv32,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.077373344451189,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
6461,diper1998/distilgpt2-finetuned-AT,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0889444202184677,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
6462,google/t5-efficient-large-nl10,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0610467009246349,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
6463,obss/mt5-base-3task-highlight-tquad2,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0772035270929336,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
6464,google/t5-efficient-small-dm256,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
6465,stas/mt5-tiny-random,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0561184659600257,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
6466,cointegrated/rubert-tiny-bilingual-nli,This is the cointegrated/rubert-tiny model fine-tuned to predict the logical relationship between two short texts: entailment or not entailment,cointegrated/rubert-tiny-bilingual-nli,1.0,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
6467,google/t5-efficient-xl-nl8,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0646016728132962,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
6468,google/t5-efficient-large-dm128,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0569435078650712,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
6469,VietAI/gpt-neo-1.3B-vietnamese-news,", to TensorRT) for integer-only deployment of the model",kssteven/ibert-roberta-large,0.0713972058147191,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
6470,google/t5-efficient-large-el8,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0224367715418339,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
6471,nherve/flaubert-oral-asr,"FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows",nherve/flaubert-oral-asr,1.0,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
6472,Milos/slovak-gpt-j-405M,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
6473,google/t5-efficient-base-dm2000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0298108924180269,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
6474,google/t5-efficient-base-dm512,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0775935649871826,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
6475,google/t5-efficient-small-nl48,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0421028863638639,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
6476,tau/tavbert-he,T5-Efficient-LARGE-EL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el2,0.0121198631823063,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6477,tau/tavbert-he,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0790334157645702,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6478,google/t5-efficient-large-nl32,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0667684450745582,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
6479,stas/mt5-tiny-random,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,0.0950782597064971,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
6480,GKLMIP/electra-myanmar-small-uncased,"Electra_Large Discriminator fine-tuned on SQuAD2.0 and custom QA datasetThis model is ahotrod/electra_large_discriminator_squad2_512  trained on additional custom dataset as:We used Google Colab for training the model,",superspray/electra_large_discriminator_squad2_custom_dataset,0.0213009994477033,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
6481,damlab/GO-language,This model is a useful encapsulation of gene ontology functions,damlab/GO-language,1.0,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
6482,google/t5-efficient-small-kv128,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0119038550183176,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
6483,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0010955650359392,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
6484,uer/pegasus-base-chinese-cluecorpussmall,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0099126128479838,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
6485,SetFit/deberta-v3-large__sst2__train-8-8,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0686646029353141,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
6486,flboehm/reddit-bert-text_5,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
6487,j-hartmann/purchase-intention-english-roberta-large,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
6488,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0944806337356567,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
6489,mnaylor/bigbird-base-mimic-mortality,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,1.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
6490,cammy/led-large-16384-arxiv-100-MDS,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0810886174440383,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
6491,groar/gpt-neo-1.3B-finetuned-escape2,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0374322161078453,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
6492,google/t5-efficient-tiny-nl12,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0781311094760894,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
6493,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,DistilWav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the XLS-R architecture,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,1.0,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
6494,google/t5-efficient-small-kv128,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0617734817788004,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
6495,uer/t5-v1_1-small-chinese-cluecorpussmall,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0463319895789026,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
6496,ml6team/distilbart-tos-summarizer-tosdr,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0555414967238902,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
6497,hf-internal-testing/tiny-random-rembert,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0198020823299884,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
6498,google/t5-efficient-base-nl2,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
6499,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0266957096755504,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
6500,google/t5-efficient-small-el12,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0443052612245082,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
6501,google/t5-efficient-base-nl32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0279001668095588,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
6502,inokufu/flaubert-base-uncased-xnli-sts,It was then fine-tuned on a natural language inference task (XNLI) [3],inokufu/flaubert-base-uncased-xnli-sts,1.0,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
6503,google/t5-efficient-small-kv256,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0145051386207342,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
6504,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0240153186023235,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
6505,sentence-transformers/paraphrase-MiniLM-L3-v2,Model is warm started from distilbert-fa’s checkpoint,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0612833648920059,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
6506,stas/t5-very-small-random,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0626994147896766,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
6507,pszemraj/led-large-book-summary,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0351199572905898,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
6508,google/t5-efficient-base-ff12000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
6509,rinna/japanese-gpt2-xsmall,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0133758174876371,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
6510,aubmindlab/aragpt2-large,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.022620528936386,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
6511,GKLMIP/electra-myanmar-small-uncased,The h5 file for Tensorflow has been generated manually by commands suggested here,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0589500265195965,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
6512,amazon/bort,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.032419946976006,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
6513,sberbank-ai/rugpt3large_based_on_gpt2,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0778674185276031,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
6514,crabz/distil-slovakbert-ner,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
6515,negfir/distilbert-base-uncased-finetuned-cola,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0400117859244346,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
6516,aditeyabaral/sentencetransformer-distilbert-hinglish-big,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0214493852108716,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
6517,google/t5-efficient-base-kv32,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.046595262363553,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
6518,IDEA-CCNL/Erlangshen-Longformer-330M,"The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e",cross-encoder/ms-marco-MiniLM-L-4-v2,0.0386352390050888,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
6519,cammy/led-base-16384-100-MDS,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0543492212891578,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
6520,TJMUCH/transcriptome-iseeek,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0957567021250724,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
6521,google/t5-efficient-large-dm512,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0567265870049595,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
6522,fnlp/bart-base-chinese,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0212641237303615,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
6523,KoichiYasuoka/roberta-base-ukrainian,"This model is the fine-tuned version of ""DeBERTa v2 XLarge""  using CUAD dataset https://huggingface",akdeniz27/deberta-v2-xlarge-cuad,0.0957096293568611,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
6524,mwesner/reformer-clm,T5-Efficient-LARGE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl32,0.0917641744017601,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
6525,aditeyabaral/sentencetransformer-bert-hinglish-small,The training code is available here: SBERT,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0596228167414665,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
6526,mrp/simcse-model-wangchanberta,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0739109851419925,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
6527,google/t5-efficient-large-nl10,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
6528,beston91/gpt2-xl_ft_logits_10k,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
6529,Recognai/selectra_medium,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
6530,ml6team/distilbart-tos-summarizer-tosdr,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0163987604901194,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
6531,google/t5-efficient-tiny-nl24,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.060679331421852,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
6532,nherve/flaubert-oral-asr,"If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:",nherve/flaubert-oral-asr,1.0,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
6533,hfl/rbtl3,T5-Efficient-LARGE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el6,0.0279414067044854,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
6534,google/t5-efficient-large-kv32,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0093225799500942,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
6535,google/t5-efficient-base-nl24,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.090465348213911,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
6536,abdusahmbzuai/ft-tatoeba-ar-en,This model was trained from scratch on the open_subtitles dataset,abdusahmbzuai/ft-tatoeba-ar-en,1.0,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
6537,google/t5-efficient-small-ff9000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0125228073447942,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
6538,hfl/chinese-electra-180g-small-ex-discriminator,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
6539,hfl/rbtl3,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0942337550222873,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
6540,google/t5-efficient-xl-nl8,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0554482974112033,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
6541,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh32,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
6542,Iskaj/hf-challenge-test,"TandC Summarization Model based on sshleifer/distilbart-cnn-6-6, This abstractive summarization model is a part of a bigger end-to-end TandC summarizer pipeline  which is preceded by LSA (Latent Semantic Analysis) extractive summarization",ml6team/distilbart-tos-summarizer-tosdr,0.093106672167778,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
6543,aubmindlab/aragpt2-mega,Pretrained model on 13G Chinese corpus(clue corpus small),junnyu/roformer_base_wwm_cluecorpussmall,0.0502085145562886,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
6544,cammy/led-base-16384-100-MDS,This does not make a very readable text,pere/DeUnCaser,0.0742263868451118,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
6545,Milos/slovak-gpt-j-1.4B,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0634828824549913,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
6546,google/t5-efficient-base-nl40,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0658661592751741,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
6547,flboehm/reddit-bert-text_5,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0182153731584548,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
6548,1one5ome/gpt2-chinese-gulong,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
6549,google/t5-efficient-small-el32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0338292215019464,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
6550,google/t5-efficient-small-kv128,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0812379866838454,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
6551,hfl/chinese-xlnet-mid,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0004262362296382,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
6552,google/t5-efficient-small-dm1000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0458403965458273,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
6553,google/t5-efficient-small-nl22,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0314533431082963,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
6554,google/t5-efficient-large-nh32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0100443847477435,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
6555,skt/ko-gpt-trinity-1.2B-v0.5,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
6556,flboehm/reddit-bert-text_5,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0584701597690582,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
6557,google/t5-efficient-large-nl36,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
6558,google/t5-efficient-large-nh4,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0733651500195264,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
6559,KoichiYasuoka/roberta-small-belarusian,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0423370804637669,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
6560,uer/t5-v1_1-base-chinese-cluecorpussmall,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.023685997352004,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
6561,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0124248076851169,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
6562,google/t5-efficient-large-el4,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0107884826138614,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
6563,google/t5-efficient-large-kv256,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.033097494393587,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
6564,mimi/Waynehills-NLP-doogie,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
6565,TJMUCH/transcriptome-iseeek,Generated from the official Squad2 evaluation scriptMuRIL is specifically trained to work on 18 Indic languages and English,Sindhu/muril-large-squad2,0.0850569829344749,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
6566,hf-internal-testing/tiny-random-rembert,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0378049276769161,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
6567,microsoft/layoutlm-large-uncased,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-768_A-12,0.0504998192191123,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
6568,akdeniz27/deberta-v2-xlarge-cuad,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0491067133843898,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
6569,google/t5-efficient-large-kv32,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0806708596646784,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
6570,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl32,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
6571,google/t5-efficient-large-el2,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.027909142896533,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
6572,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
6573,microsoft/deberta-v2-xxlarge-mnli,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0535036725923418,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
6574,microsoft/layoutlm-base-cased,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0804584771394729,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
6575,everdoubling/byt5-Korean-small,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,1.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
6576,tartuNLP/gpt-4-est-large,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
6577,microsoft/unispeech-sat-base,Microsoft's UniSpeechThe base model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss,microsoft/unispeech-sat-base,1.0,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
6578,google/t5-efficient-base-nl32,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0526662971824407,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
6579,google/bert_uncased_L-10_H-768_A-12,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0245217963432272,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
6580,Iskaj/hf-challenge-test,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0335708931088447,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
6581,stas/mt5-tiny-random,For more information refer to SUPERB: Speech processing Universal PERformance Benchmark,superb/hubert-large-superb-sid,0.0267390869557857,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
6582,google/t5-efficient-tiny-nh8,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0747661069035529,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
6583,dhpollack/distilbert-dummy-sentiment,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0021216790191829,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
6584,hfl/chinese-legal-electra-base-generator,ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants,hfl/chinese-legal-electra-base-generator,1.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
6585,TsinghuaAI/CPM-Generate,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
6586,mmaguero/gn-bert-tiny-cased,We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric,andrejmiscic/simcls-scorer-billsum,0.0196553692221642,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
6587,google/t5-efficient-large-kv256,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0447138361632823,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
6588,google/t5-efficient-large-nh2,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
6589,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.037485122680664,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
6590,ductuan024/AimeLaw,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0588831901550292,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
6591,superspray/electra_large_discriminator_squad2_custom_dataset,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0155317559838294,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
6592,google/t5-efficient-large-el8,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
6593,tuner007/pegasus_qa,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0782410800457,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
6594,google/t5-efficient-base-nl24,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0697080679237842,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
6595,google/t5-efficient-base-ff6000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0397059656679629,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
6596,lgrobol/electra-minuscule-discriminator,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-discriminator,1.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
6597,google/t5-large-ssm-nq,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0466823602716127,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
6598,anas-awadalla/bert-tiny-finetuned-squad,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
6599,google/t5-efficient-base-kv16,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
6600,zoha/wav2vec2-base-timit-demo-colab,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0578634105622768,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
6601,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Trained, validate, and tested using a subset of the NELA-GT-2018 dataset",khizon/distilbert-unreliable-news-eng-4L,0.0924369245767593,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
6602,minimaxir/magic-the-gathering,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0266152643598616,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
6603,google/t5-efficient-mini-nl6,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0672215968370436,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
6604,hfl/chinese-electra-small-ex-discriminator,ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants,hfl/chinese-electra-small-ex-discriminator,1.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
6605,google/t5-efficient-large-nh2,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.036796486005187,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
6606,IDEA-CCNL/Erlangshen-Longformer-330M,The exact expression to filter the dataset was the following:The fine-tuned model was evaluated on 2000 random articles from the validation set,ml6team/mt5-small-german-finetune-mlsum,0.0192800108343363,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
6607,google/t5-efficient-xl-nl4,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0087619889527559,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
6608,minimaxir/magic-the-gathering,"The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card",minimaxir/magic-the-gathering,1.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
6609,FremyCompany/xls-r-nl-v1-cv8-lm,I have made no attempts to censor,bigjoedata/rockbot,0.0395795895407596,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
6610,microsoft/layoutlm-base-cased,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0200758501887321,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
6611,mrm8488/deberta-v3-small-finetuned-mrpc,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
6612,anirudh21/xlnet-base-cased-finetuned-rte,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0145626589655876,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
6613,google/t5-efficient-base-dm256,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
6614,google/t5-efficient-small-el8,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
6615,google/t5-efficient-large-dm128,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0602110661566256,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
6616,Iskaj/hf-challenge-test,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
6617,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0834962055087089,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
6618,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0956671983003616,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
6619,hfl/rbtl3,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0239743497222662,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
6620,allenai/macaw-3b,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-3b,1.0,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
6621,allenai/macaw-answer-11b,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,1.0,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
6622,google/t5-efficient-large-kv32,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0491294208914041,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
6623,Milos/slovak-gpt-j-405M,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0716397166252135,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
6624,brad1141/Longformer-finetuned-norm,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0957734882831573,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
6625,google/t5-efficient-large-el2,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0277439765632152,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
6626,tuner007/pegasus_qa,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0764727592468261,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
6627,google/t5-efficient-mini-nl12,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0848341286182403,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
6628,TJMUCH/transcriptome-iseeek,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0275352075695991,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
6629,google/t5-efficient-large-kv32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0018603838980198,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
6630,mrp/simcse-model-wangchanberta,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
6631,aubmindlab/aragpt2-mega,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
6632,DeepPavlov/distilrubert-tiny-cased-conversational,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0643141269683837,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
6633,google/t5-efficient-large-kv32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0669200345873832,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
6634,google/t5-efficient-large-nh8-nl32,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
6635,aditeyabaral/sentencetransformer-bert-hinglish-small,"In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased ELECTRA base model for Turkish 🎉We release a base ELECTRA model for Turkish, that was trained on the same data as BERTurk",dbmdz/electra-base-turkish-cased-discriminator,0.0837906971573829,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
6636,google/t5-efficient-base-kv16,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0150506664067507,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
6637,mgreenbe/bertlet-base-uncased-for-sequence-classification,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0514534711837768,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
6638,minimaxir/magic-the-gathering,"ByT5 works especially well on noisy text data,e",google/byt5-xl,0.0463532842695713,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
6639,tau/tavbert-he,Pretrained weights based on PyTorrent Dataset which is a curated data from a large official Python packages,Fujitsu/pytorrent,0.0644693616777658,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6640,Rostlab/prot_bert,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
6641,TJMUCH/transcriptome-iseeek,This model is cased: it makes a difference between english and English,google/fnet-large,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
6642,mrm8488/xlm-multi-finetuned-xquadv1,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0637777186930179,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
6643,google/t5-efficient-small-ff6000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0401787892915308,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
6644,google/t5-efficient-base-kv16,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0653981491923331,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
6645,tartuNLP/gpt-4-est-large,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.092116892337799,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
6646,google/t5-efficient-mini,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0173229202628135,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
6647,mwesner/reformer-clm,T5-Efficient-LARGE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el8,0.0472343936562538,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
6648,mrm8488/xlm-multi-finetuned-xquadv1,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0191883519291877,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
6649,uer/t5-v1_1-base-chinese-cluecorpussmall,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0699062980711459,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
6650,piotr-rybak/poleval2021-task4-herbert-large-encoder,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0710942968726158,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
6651,danyaljj/opengpt2_pytorch_forward,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0505441178878148,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
6652,google/t5-efficient-tiny-nh16,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
6653,mmaguero/gn-bert-tiny-cased,The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our Stack Overflow Dataset repository,UWB-AIR/MQDD-duplicates,0.0447368873283266,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
6654,hfl/rbtl3,T5-Efficient-LARGE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh24,0.0688430219888687,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
6655,SupriyaArun/squeezebert-uncased-finetuned-squad,"Experimental results show that ERNIE achieve state-of-the-art results on five Chinese natural language processing tasks including natural language inference,  semantic similarity, named entity recognition, sentiment analysis and question answering",nghuyong/ernie-1.0,0.0895841717720031,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
6656,qarib/bert-base-qarib_far_6500k,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.009270099690184,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
6657,aubmindlab/aragpt2-mega,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0283062849193811,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
6658,antoiloui/belgpt2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
6659,stas/t5-very-small-random,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0143399871885776,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
6660,nghuyong/ernie-tiny,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0408592813958723,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
6661,bigjoedata/rockbot,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0172818386927246,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
6662,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0712986215949058,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
6663,google/t5-efficient-small-dm128,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0544553622603416,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
6664,patrickvonplaten/unispeech-sat-large-timit-ft,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
6665,lgrobol/electra-minuscule-discriminator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0674190782010554,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
6666,google/t5-efficient-small-nl48,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0221105758100747,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
6667,google/t5-efficient-small-nl20,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0018590334802866,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
6668,cammy/led-base-16384-100-MDS,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
6669,tuner007/pegasus_qa,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0490826144814491,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
6670,negfir/distilbert-base-uncased-finetuned-squad,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0689699724316597,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
6671,google/t5-efficient-small-el8,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0177154876291751,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
6672,google/t5-efficient-base-el8,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0638503711670637,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
6673,GKLMIP/electra-myanmar-small-uncased,"This the DeBERTa V2 XXLarge model fine-tuned with MNLI task, 48 layers, 1536 hidden size",microsoft/deberta-v2-xxlarge-mnli,0.0886768661439418,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
6674,microsoft/layoutlm-base-cased,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0091244801878929,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
6675,sarnikowski/convbert-medium-small-da-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0018216564009587,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
6676,dbmdz/convbert-base-german-europeana-cased,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.008891548961401,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
6677,google/t5-efficient-large-dm768,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0083437506109475,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
6678,hfl/rbt3,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.0404477268457412,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
6679,google/t5-efficient-tiny-nl2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0171252372674643,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
6680,google/t5-efficient-large-el6,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0804502964019775,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
6681,microsoft/layoutlm-base-cased,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
6682,Milos/slovak-gpt-j-405M,entity-level masking and phrase-level masking,nghuyong/ernie-1.0,0.0178295268366734,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
6683,google/t5-efficient-small-dm128,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0579509213566779,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
6684,google/t5-efficient-xl-nl12,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
6685,microsoft/layoutlm-base-uncased,A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including Strixhaven and Commander 2021,minimaxir/magic-the-gathering,0.0608553327620029,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
6686,microsoft/layoutlm-base-cased,"Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example",facebook/m2m100-12B-avg-10-ckpt,0.0717812106013298,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
6687,google/t5-efficient-base-ff9000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0613291226327419,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
6688,hfl/rbtl3,T5-Efficient-LARGE-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl20,0.0481765512377023,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
6689,celine98/canine-s-finetuned-sst2,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0812600404024124,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
6690,google/t5-efficient-xl-nl12,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0284668724052607,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
6691,mrm8488/mobilebert-uncased-finetuned-squadv2,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0172105970947692,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
6692,google/t5-efficient-mini-nl8,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0685512851923703,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
6693,allenai/wmt19-de-en-6-6-big,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
6694,google/t5-efficient-base-kv32,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
6695,mnaylor/bigbird-base-mimic-mortality,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0057936869561672,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
6696,aubmindlab/aragpt2-mega,You can find more information in our paper AraGPT2The code in this repository was used to train all GPT2 variants,aubmindlab/aragpt2-mega,1.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
6697,aditeyabaral/sentencetransformer-distilbert-hinglish-big,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
6698,1one5ome/gpt2-chinese-gulong,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
6699,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff1000,0.0822918191552161,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
6700,rdenadai/BR_BERTo,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0209543046851952,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
6701,sentence-transformers/paraphrase-MiniLM-L3-v2,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0652483031153678,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
6702,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0969985648989677,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
6703,google/t5-efficient-large-nh12,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
6704,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0115023218095302,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
6705,xlnet-large-cased,I have made no attempts to censor,bigjoedata/rockbot,0.0305162575095891,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
6706,google/t5-efficient-large-nh24,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.064494926482439,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
6707,tau/tavbert-he,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6708,mrm8488/deberta-v3-small-finetuned-mrpc,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
6709,google/t5-efficient-tiny-nh1,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
6710,google/t5-efficient-large-dm512,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
6711,DemangeJeremy/4-sentiments-with-flaubert,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0845735482871532,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
6712,google/t5-efficient-mini,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0262091923505067,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
6713,hf-internal-testing/tiny-random-rembert,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0332573130726814,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
6714,stas/pegasus-cnn_dailymail-tiny-random,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0584799349308013,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
6715,pere/DeUnCaser,However the current finetuning is only done on Norwegian,pere/DeUnCaser,1.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
6716,kyryl0s/gpt2-uk-xxs,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0552233532071113,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
6717,google/t5-efficient-large-nl32,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0484604779630899,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
6718,hf-internal-testing/tiny-random-rembert,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0551181733608245,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
6719,1one5ome/gpt2-chinese-gulong,"The training data is private Bulgarian text from CNN, DailyMail articles",rmihaylov/pegasus-base-cnn-dailymail-bg,0.0653702616691589,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
6720,anas-awadalla/bert-tiny-finetuned-squad,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0956390872597694,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
6721,malay-huggingface/t5-super-tiny-bahasa-cased,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
6722,google/t5-efficient-base-dm512,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0923442058265208,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
6723,sentence-transformers/paraphrase-MiniLM-L3-v2,This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset,groar/gpt-neo-1.3B-finetuned-escape2,0.0995654016733169,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
6724,mimicheng/codeparrot-ds,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.0789036229252815,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
6725,microsoft/layoutlm-large-uncased,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
6726,microsoft/layoutlm-base-cased,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0994086042046547,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
6727,tau/tavbert-he,"We hope to release other models, such as base/large models, in the future",skplanet/dialog-koelectra-small-generator,0.0732989627867936,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6728,google/t5-efficient-small-el4,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0579963717609643,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
6729,GKLMIP/electra-myanmar-small-uncased,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0296558029949665,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
6730,google/t5-efficient-base-kv256,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0646602045744657,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
6731,Davlan/mbart50-large-eng-yor-mt,"Specifically, this model is a mbart-large-50 model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k",Davlan/mbart50-large-eng-yor-mt,1.0,-1.0,NLP,12474c75-dc49-473e-bcb6-eea8354b1253___mbart50-large-eng-yor-mt,HF
6732,google/t5-efficient-large-nh8-nl32,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
6733,anirudh21/xlnet-base-cased-finetuned-rte,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0850099325180053,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
6734,google/t5-efficient-mini-nl24,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0648777149617671,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
6735,google/t5-efficient-base-nl4,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0033662728965282,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
6736,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0937956050038337,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
6737,uer/t5-v1_1-small-chinese-cluecorpussmall,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.048243710771203,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
6738,DemangeJeremy/4-sentiments-with-flaubert,"In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human",TransQuest/monotransquest-hter-en_lv-it-nmt,0.068964660167694,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
6739,lannelin/bert-imdb-1hidden,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.073025411615769,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
6740,skt/ko-gpt-trinity-1.2B-v0.5,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
6741,google/t5-efficient-large-dm128,This does not make a very readable text,pere/DeUnCaser,0.0509153679013251,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
6742,jnz/electra-ka,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0739796385169029,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
6743,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0417118817567825,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
6744,google/t5-efficient-tiny-nl16,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0448963418602943,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
6745,google/t5-efficient-base-kv128,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
6746,skt/ko-gpt-trinity-1.2B-v0.5,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0233613448217511,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
6747,google/t5-efficient-large-nh32,Its original repository can be found here,tau/splinter-large-qass,0.0634330492466687,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
6748,google/t5-efficient-tiny-el8,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0661662258207797,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
6749,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,For best results use python as below,pszemraj/led-large-book-summary,0.0736108496785163,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
6750,thu-coai/LongLM-large,T5-Efficient-TINY-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh8,0.0327408900484442,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
6751,mldmm/GlassBERTa,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,1.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
6752,hyunwoongko/reddit-9B,We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy,hyunwoongko/reddit-9B,1.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
6753,TJMUCH/transcriptome-iseeek,This model is case-sensitive: it makes a difference between finnish and Finnish,flax-community/RoBERTa-large-finnish,0.0681100413203239,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
6754,tartuNLP/gpt-4-est-large,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,0.0833685472607612,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
6755,diper1998/distilgpt2-finetuned-AT,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.0491793751716613,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
6756,KoichiYasuoka/roberta-small-belarusian,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0278352219611406,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
6757,google/t5-efficient-base-nh24,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
6758,zoha/wav2vec2-base-timit-demo-colab,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0822190642356872,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
6759,google/t5-efficient-large-kv16,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0754567869007587,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
6760,google/t5-efficient-large-kv32,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0042070420458913,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
6761,microsoft/layoutlm-large-uncased,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0723494589328765,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
6762,google/t5-efficient-small-el16-dl4,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0621709525585174,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
6763,google/t5-efficient-large-kv128,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.053996669128537,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
6764,google/t5-efficient-base-ff6000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0547823440283536,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
6765,google/t5-efficient-base-dm2000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0720149874687194,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
6766,google/t5-efficient-large-kv32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.009871962480247,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
6767,suzuki/distilbert-base-uncased-finetuned-squad,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
6768,Milos/slovak-gpt-j-405M,Source language: English,NYTK/translation-marianmt-en-hu,0.0481091961264609,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
6769,ToToKr/kobigbird-bert-base-finetuned-klue,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0784130692481994,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
6770,google/t5-efficient-base-nl36,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0641278810799121,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
6771,google/t5-efficient-small-dm2000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0592771293595432,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
6772,VietAI/gpt-neo-1.3B-vietnamese-news,"Ko-GPT-Trinity refers to the class of models, while 1.2B represents the number of parameters of this particular pre-trained model",skt/ko-gpt-trinity-1.2B-v0.5,0.0470861382782459,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
6773,aditeyabaral/sentencetransformer-bert-hinglish-small,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.087757371366024,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
6774,google/t5-efficient-tiny-el8,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0359329059720038,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
6775,suzuki/distilbert-base-uncased-finetuned-squad,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
6776,stas/pegasus-cnn_dailymail-tiny-random,The ConvBERT discriminator model intented to be used for fine-tuning on downstream tasks like text classification is released here Finnish-NLP/convbert-base-finnishFinnish ConvBERT is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion,Finnish-NLP/convbert-base-generator-finnish,0.0859287418425082,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
6777,suzuki/distilbert-base-uncased-finetuned-squad,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
6778,google/t5-efficient-small-el16-dl4,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0806599780917167,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
6779,mrm8488/mobilebert-uncased-finetuned-squadv2,"MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks",mrm8488/mobilebert-uncased-finetuned-squadv2,1.0,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
6780,SajjadAyoubi/distil-bigbird-fa-zwnj,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,1.0,-1.0,NLP,93bf4f14-6c7a-41bc-854c-061751656605___distil-bigbird-fa-zwnj,HF
6781,superspray/electra_large_discriminator_squad2_custom_dataset,Its original repository can be found here,tau/splinter-large-qass,0.0081860125064849,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
6782,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl16,0.0302487816661596,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
6783,google/t5-efficient-mini-nl8,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0651041604578494,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
6784,superspray/electra_large_discriminator_squad2_custom_dataset,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-768_A-12,0.0705770105123519,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
6785,malay-huggingface/t5-small-bahasa-cased,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0562509763985872,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
6786,google/t5-efficient-small-nl20,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0223369589075446,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
6787,kyryl0s/gpt2-uk-xxs,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0521429777145385,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
6788,stas/mt5-tiny-random,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0846993550658226,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
6789,allenai/wmt19-de-en-6-6-big,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0705930143594741,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
6790,google/t5-efficient-mini-nl8,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
6791,google/t5-efficient-xl-nl2,Its original repository can be found here,tau/splinter-large-qass,0.0546509269624947,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
6792,KBLab/megatron-bert-base-swedish-cased-600k,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
6793,celine98/canine-s-finetuned-sst2,bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:Please refer to this blog post for further detail,bolbolzaban/gpt2-persian,0.0797368362545967,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
6794,google/t5-efficient-xl-nl12,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0046427156776189,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
6795,mrp/simcse-model-wangchanberta,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0708768758922815,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
6796,piotr-rybak/poleval2021-task4-herbert-large-encoder,A distilled version of ProtBert-UniRef100 model,yarongef/DistilProtBert,0.0702124312520027,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
6797,zoha/wav2vec2-base-timit-demo-colab,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0306930262595415,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
6798,google/t5-efficient-tiny-nl12,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0057835187762975,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
6799,lgrobol/electra-minuscule-generator,Source language: English,NYTK/translation-marianmt-en-hu,0.0844872444868087,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
6800,google/t5-efficient-xl-nl16,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0479549057781696,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
6801,google/t5-efficient-base-nl48,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0397904785349964,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
6802,diper1998/distilgpt2-finetuned-AT,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0097256880253553,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
6803,stas/pegasus-cnn_dailymail-tiny-random,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0515081118792295,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
6804,google/reformer-crime-and-punishment,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
6805,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0628869011998176,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
6806,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl16,0.0344105055555701,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
6807,cammy/led-large-16384-arxiv-100-MDS,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0845465287566185,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
6808,google/t5-efficient-large-dm256,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0425127539783716,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
6809,crabz/distil-slovakbert-ner,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0633454620838165,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
6810,google/t5-efficient-small-el32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0387023240327834,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
6811,google/t5-efficient-small-el32,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.039192097261548,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
6812,stas/mt5-tiny-random,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
6813,anirudh21/xlnet-base-cased-finetuned-rte,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
6814,google/t5-efficient-small-ff9000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
6815,google/t5-efficient-large-kv128,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0826471298933028,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
6816,google/t5-efficient-small-el32,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
6817,Davlan/mbart50-large-eng-yor-mt,mbart50-large-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned   facebook/mbart-large-50 model,Davlan/mbart50-large-eng-yor-mt,1.0,-1.0,NLP,12474c75-dc49-473e-bcb6-eea8354b1253___mbart50-large-eng-yor-mt,HF
6818,Palak/microsoft_deberta-base_squad,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.066655233502388,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
6819,tau/tavbert-he,"The 3.5 billion parameter Wenzhong-GPT2-3.5B large model, using 100G chinese common data, 32 A100 training for 28 hours, is the largest open source GPT2 large model of chinese",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0454974751919507,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6820,hfl/rbt3,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0028536673635244,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
6821,google/t5-efficient-xl-nl16,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0786212682723999,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
6822,google/t5-efficient-xl-nl12,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0014283517375588,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
6823,allenai/wmt16-en-de-dist-6-1,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0162908844649792,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
6824,google/t5-efficient-tiny-nl16,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.043225347995758,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
6825,allegro/plt5-large,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0132505086561044,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
6826,malay-huggingface/t5-tiny-bahasa-cased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.012054064621528,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
6827,bigjoedata/rockbot,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0535040919979413,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
6828,IDEA-CCNL/Erlangshen-Longformer-110M,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
6829,malay-huggingface/xlnet-base-bahasa-cased,Its original repository can be found here,tau/splinter-large-qass,0.0309345205314456,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
6830,google/t5-efficient-small-nl40,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0437210816890001,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
6831,krevas/finance-koelectra-base-generator,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,0.0629831235855817,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
6832,google/t5-efficient-base-el6,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0068364562466741,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
6833,google/t5-efficient-small-kv16,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
6834,google/t5-efficient-large-nl36,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0314742252230644,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
6835,stas/pegasus-cnn_dailymail-tiny-random,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
6836,pszemraj/led-large-book-summary,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,0.0434554631356149,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
6837,google/t5-efficient-small-nl2,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
6838,lassl/gpt2-ko-small,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0548186372034251,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
6839,1one5ome/gpt2-chinese-gulong,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
6840,tartuNLP/gpt-4-est-large,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,0.065621830523014,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
6841,hfl/rbtl3,The architecture requires two separate models,enelpol/czywiesz-context,0.0613607801496982,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
6842,sonoisa/byt5-small-japanese,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0709434002637863,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
6843,google/t5-efficient-xl-nl16,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0971532016992569,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
6844,DemangeJeremy/4-sentiments-with-flaubert,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
6845,google/t5-efficient-large-kv16,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
6846,google/t5-efficient-small-ff9000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0319404918700456,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
6847,hfl/rbt3,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
6848,hajime9652/xlnet-japanese,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0734913796186447,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
6849,google/t5-efficient-small-dm128,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
6850,lassl/gpt2-ko-small,"This model was trained from 6,831,079 examples (whose have 3,497,512,448 tokens)",lassl/gpt2-ko-small,1.0,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
6851,google/t5-efficient-base-ff6000,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0862107686698436,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
6852,rinna/japanese-gpt-1b,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
6853,superspray/electra_large_discriminator_squad2_custom_dataset,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,0.0490870922803878,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
6854,tartuNLP/gpt-4-est-large,"This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences",Rostlab/prot_bert,0.0923677906394004,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
6855,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0216470543295144,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
6856,SajjadAyoubi/distil-bigbird-fa-zwnj,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0068631661789757,-1.0,NLP,93bf4f14-6c7a-41bc-854c-061751656605___distil-bigbird-fa-zwnj,HF
6857,dhpollack/distilbert-dummy-sentiment,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
6858,google/t5-efficient-base-kv256,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0348284021019935,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
6859,anas-awadalla/bert-tiny-finetuned-squad,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.084292322397232,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
6860,aliosm/ComVE-gpt2-large,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0389004033058881,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
6861,google/t5-efficient-small-nl2,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-nl2,1.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
6862,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0533226318657397,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
6863,Palak/microsoft_deberta-base_squad,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.045793417841196,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
6864,negfir/distilbert-base-uncased-finetuned-cola,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
6865,google/t5-efficient-small-el16-dl4,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0863216891884803,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
6866,hajime9652/xlnet-japanese,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0369003489613533,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
6867,flax-community/RoBERTa-large-finnish,RoBERTa is a transformers model pretrained on a large corpus of Finnish data in a self-supervised fashion,flax-community/RoBERTa-large-finnish,1.0,-1.0,NLP,bd6a99f8-f445-4aab-803e-729051effeb2___RoBERTa-large-finnish,HF
6868,suzuki/distilbert-base-uncased-finetuned-squad,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0630448758602142,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
6869,ductuan024/AimeLaw,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0368989235721528,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
6870,eli4s/Bert-L12-h240-A12,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0386688229627907,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
6871,google/t5-efficient-base-el8,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0606610383838414,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
6872,negfir/distilbert-base-uncased-finetuned-cola,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
6873,google/t5-efficient-small-kv128,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
6874,google/t5-efficient-mini-nl12,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0103944577276706,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
6875,dbmdz/electra-small-turkish-cased-discriminator,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0185658778063952,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
6876,zoha/wav2vec2-base-timit-demo-colab,reformer-enwik8 was pretrained on the first 90M chars of enwik8 whereas the text was chunked into batches of size 65536 chars (=2^16),google/reformer-enwik8,0.0461197607219219,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
6877,fnlp/bart-large-chinese,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0862952396273612,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
6878,pulp/CHILDES-ParentBERTo,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.02375372312963,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
6879,1one5ome/gpt2-chinese-gulong,A direct export of the model checkpoint was first made available in this repository,google/rembert,0.0550554916262626,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
6880,sberbank-ai/rugpt3large_based_on_gpt2,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0226635346189141,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
6881,SupriyaArun/squeezebert-uncased-finetuned-squad,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0136315030977129,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
6882,google/t5-efficient-large-nh24,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0118087772279977,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
6883,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0311589129269123,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
6884,microsoft/layoutlm-large-uncased,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
6885,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0959812998771667,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
6886,rdenadai/BR_BERTo,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0388380382210016,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
6887,google/t5-efficient-large-el8,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0006964839994907,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
6888,mrm8488/deberta-v3-small-finetuned-mrpc,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0631318092346191,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
6889,dhpollack/distilbert-dummy-sentiment,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0515673290938138,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
6890,VietAI/gpt-neo-1.3B-vietnamese-news,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0761530008167028,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
6891,cammy/led-large-16384-arxiv-100-MDS,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0237931683659553,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
6892,google/t5-efficient-large-dm2000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
6893,1one5ome/gpt2-chinese-gulong,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0807947516441345,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
6894,google/t5-efficient-base-ff6000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0360429848078638,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
6895,sberbank-ai/rugpt3large_based_on_gpt2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
6896,allenai/macaw-answer-11b,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0086796147127945,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
6897,tau/splinter-large-qass,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
6898,tuner007/pegasus_qa,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
6899,google/t5-efficient-base-nh32,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0694633685052394,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
6900,google/t5-efficient-mini-nl24,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-mini-nl24,1.0,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
6901,google/t5-efficient-large-nh4,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
6902,google/t5-efficient-base-kv256,This does not make a very readable text,pere/DeUnCaser,0.0393670131452381,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
6903,mrm8488/chEMBL_smiles_v1,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,919d68f1-5654-4b4c-a9c7-f59f67ababe7___chEMBL_smiles_v1,HF
6904,google/t5-efficient-small-kv32,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0581706315279006,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
6905,rmihaylov/bert-base-pos-theseus-bg,This model is cased: it does make a difference between bulgarian and Bulgarian,rmihaylov/bert-base-pos-theseus-bg,1.0,-1.0,NLP,428758d5-6177-42ba-a879-c30586fca3ab___bert-base-pos-theseus-bg,HF
6906,Palak/microsoft_deberta-base_squad,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0126995015889406,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
6907,mimicheng/codeparrot-ds,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0555100515484809,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
6908,mimicheng/codeparrot-ds,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
6909,google/t5-efficient-tiny-el8,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0673401840031146,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
6910,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0818391218781471,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
6911,ErykWdowiak/GPTalian,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,1.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
6912,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"Then, it was compressed via progressive module replacing",rmihaylov/bert-base-pos-theseus-bg,0.0,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
6913,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,0.0468438956886529,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
6914,google/t5-efficient-tiny-nh1,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0121757667511701,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
6915,hakurei/gpt-j-random-tinier,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0778676625341176,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
6916,allenai/wmt19-de-en-6-6-base,Its original repository can be found here,tau/splinter-large-qass,0.05090793967247,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
6917,google/t5-efficient-base-nh32,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0452523343265056,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
6918,everdoubling/byt5-Korean-small,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0192908139899372,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
6919,gchhablani/fnet-base-finetuned-wnli,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0641259662806987,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
6920,hfl/rbtl3,T5-Efficient-SMALL-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el4,0.0410961247980594,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
6921,fnlp/bart-large-chinese,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0688298549503087,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
6922,google/t5-efficient-xl-nl2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,1.5918165445300003e-05,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
6923,google/t5-efficient-small-el16-dl4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
6924,google/t5-efficient-base-nl40,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
6925,google/t5-efficient-base-nl36,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0240738745778799,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
6926,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0214620009064674,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
6927,google/t5-efficient-large-kv16,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0051474366337061,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
6928,Iskaj/hf-challenge-test,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0149740204215049,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
6929,google/t5-efficient-base-nl2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
6930,google/t5-efficient-small-el32,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0797740370035171,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
6931,gayanin/bart-mlm-pubmed-medterm,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0410991311073303,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
6932,google/t5-efficient-large-nl10,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0540515976026654,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
6933,lgrobol/electra-minuscule-discriminator,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0585309006273746,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
6934,ToToKr/kobigbird-bert-base-finetuned-klue,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
6935,google/t5-efficient-small-el32,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0819183439016341,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
6936,cammy/led-large-16384-arxiv-100-MDS,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0105044487863779,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
6937,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0022745629151662,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
6938,Recognai/zeroshot_selectra_small,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0492021841928362,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
6939,google/t5-efficient-small-ff6000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0527639668434858,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
6940,tartuNLP/gpt-4-est-large,Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer,flaubert/flaubert_small_cased,0.0833654701709747,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
6941,dbmdz/bert-medium-historic-multilingual-cased,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0897176712751388,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
6942,eli4s/Bert-L12-h256-A4,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
6943,google/t5-efficient-small-nl40,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
6944,malay-huggingface/t5-super-super-tiny-bahasa-cased,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0074318001667658,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
6945,mrm8488/deberta-v3-small-finetuned-mrpc,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.095040425658226,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
6946,Palak/microsoft_deberta-base_squad,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0993490517139434,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
6947,google/t5-efficient-large-dm2000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
6948,google/t5-efficient-base-ff1000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0571090672165155,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
6949,google/t5-efficient-large-nh2,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0476782545447349,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
6950,IDEA-CCNL/Erlangshen-Longformer-110M,"In the first stage, a large pre-trained model for abstractive summarization (the generator) is used to generate candidate summaries, whereas, in the second stage, the scorer assigns a score to each candidate given the source document",andrejmiscic/simcls-scorer-billsum,0.0773711875081061,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
6951,hfl/chinese-electra-small-ex-generator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
6952,crabz/distil-slovakbert-ner,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0888838246464729,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
6953,abdusahmbzuai/aradia-ctc-distilhubert-ft,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.025892399251461,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
6954,google/t5-efficient-base-kv128,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0846602283418178,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
6955,jnz/electra-ka,This is an implementation of CPT-Base,fnlp/cpt-base,0.0804355442523956,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
6956,tau/tavbert-he,T5-Efficient-BASE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl32,0.0095913754776119,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6957,google/t5-efficient-base-nl4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
6958,TJMUCH/transcriptome-iseeek,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
6959,google/t5-efficient-large-kv16,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0126740215346216,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
6960,amazon/bort,We use SimCSE here by using mBERT as the baseline model and training the model with Thai Wikipedia hereUsing this model becomes easy when you have sentence-transformers installed:,mrp/simcse-model-wangchanberta,0.0893151797354221,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
6961,akdeniz27/deberta-v2-xlarge-cuad,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0427817516028881,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
6962,google/t5-efficient-base-nh32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0512310154736042,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
6963,google/t5-efficient-base-nl32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0217380272224545,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
6964,mikaelsouza/msft-regular-model,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0961615815758705,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
6965,google/t5-efficient-small-ff9000,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0977244898676872,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
6966,cammy/led-large-16384-arxiv-100-MDS,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0458024144172668,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
6967,pulp/CHILDES-ParentBERTo,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
6968,CuongLD/wav2vec2-large-xlsr-vietnamese,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0310300439596175,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
6969,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0843952894210815,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
6970,Sindhu/muril-large-squad2,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0661459704861044,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
6971,google/t5-efficient-base-nl40,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0504731535911559,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
6972,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0601646192371845,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
6973,google/t5-efficient-xl-nl4,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0681551173329353,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
6974,hfl/rbt3,T5-Efficient-TINY-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff9000,0.0557905547320842,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
6975,DeepPavlov/distilrubert-tiny-cased-conversational,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-mega,0.0392122026532888,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
6976,google/t5-efficient-base-el8,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
6977,google/t5-efficient-tiny-nl6,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
6978,DTAI-KULeuven/robbertje-1-gb-bort,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0121963713318109,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
6979,zoha/wav2vec2-base-timit-demo-colab,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0589607134461402,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
6980,google/t5-efficient-large-nh2,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0677951909601688,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
6981,google/t5-efficient-base-nh32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0689822938293218,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
6982,sentence-transformers/paraphrase-MiniLM-L3-v2,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0888269022107124,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
6983,tartuNLP/gpt-4-est-large,"To improve the capacities of both the encoder and decoder, we propose to train LongLM with two pretraining tasks including text inﬁlling (Raffel et al",thu-coai/LongLM-large,0.0956592410802841,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
6984,google/t5-efficient-base-nl24,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0062199309468269,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
6985,uer/t5-v1_1-base-chinese-cluecorpussmall,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-base-chinese-cluecorpussmall,1.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
6986,google/t5-efficient-xl-nl28,Its original repository can be found here,tau/splinter-large-qass,0.0522977132350205,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
6987,filco306/gpt2-switchboard-paraphraser,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0038008359260857,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
6988,google/t5-efficient-base-nl4,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0540296919643878,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
6989,IDEA-CCNL/Erlangshen-Longformer-110M,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0868695229291915,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
6990,piotr-rybak/poleval2021-task4-herbert-large-encoder,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
6991,hyunwoongko/reddit-9B,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0603291067693914,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
6992,mwesner/reformer-clm,T5-Efficient-LARGE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh24,0.0865205228328704,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
6993,google/t5-efficient-small-el16-dl4,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.045049987733364,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
6994,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0710091441869735,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
6995,zoha/wav2vec2-base-timit-demo-colab,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0474241375923156,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
6996,bookbot/distil-wav2vec2-adult-child-cls-52m,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0396747508396704,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
6997,mrm8488/bert-small-finetuned-squadv2,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0357638499699533,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
6998,ebagdasa/propaganda_positive_bart_large,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.0361284644653399,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
6999,google/t5-efficient-large-nh8,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.085012100636959,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
7000,KBLab/megatron-bert-large-swedish-cased-165k,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
7001,suzuki/distilbert-base-uncased-finetuned-squad,"In our work, we have trained the BigBird model for the Persian language to process texts up to 4096 in the Farsi (Persian) language using sparse attention",SajjadAyoubi/distil-bigbird-fa-zwnj,0.076858639717102,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
7002,FremyCompany/xls-r-nl-v1-cv8-lm,"This model achieves the following results on the evaluation set (of Common Voice 8.0):The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result",FremyCompany/xls-r-nl-v1-cv8-lm,1.0,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
7003,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff9000,0.0740977004170417,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
7004,microsoft/layoutlm-base-cased,It has less than 1% difference in performance compared to the BERT based model while having 2.0x the speed,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
7005,google/t5-efficient-mini,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0691996328532695,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
7006,SetFit/deberta-v3-large__sst2__train-8-8,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0188772976398468,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
7007,Rostlab/prot_bert,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,1.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
7008,google/t5-efficient-small-kv256,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0454664286226033,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
7009,ml6team/mt5-small-german-finetune-mlsum,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0480666495859622,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
7010,google/t5-efficient-small-kv16,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0471378192305564,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
7011,ductuan024/AimeLaw,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0582118332386016,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
7012,flboehm/reddit-bert-text_5,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0762895494699478,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
7013,rinna/japanese-gpt2-xsmall,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
7014,google/t5-efficient-small-nl40,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0408799741417169,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
7015,google/t5-efficient-xl-nl4,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0590670481324195,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
7016,Parsa/BBB_prediction_classification_SMILES,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0418134095768134,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
7017,google/t5-efficient-tiny-ff3000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0067178346216678,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
7018,aditeyabaral/sentencetransformer-bert-hinglish-small,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
7019,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"We exploit Unihan, a ready-made database constructed by linguistic experts to first merge morphologically similar characters into clusters",microsoft/unihanlm-base,0.0610410161316394,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
7020,google/t5-efficient-large-nh4,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
7021,google/t5-efficient-large-kv256,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0081137674860656,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
7022,skt/ko-gpt-trinity-1.2B-v0.5,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0546651352196931,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
7023,amazon/bort,This model used the pre-trained weights of distilbert-base-cased as starting point (only 4 layers) and was able to achieve 84% accuracy on the test set,khizon/distilbert-unreliable-news-eng-4L,0.0688422545790671,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
7024,thu-coai/LongLM-large,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.029498633618156,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
7025,aditeyabaral/sentencetransformer-bert-hinglish-small,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0307406615465879,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
7026,dbmdz/bert-medium-historic-multilingual-cased,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0468582296743988,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
7027,TJMUCH/transcriptome-iseeek,We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric,andrejmiscic/simcls-scorer-billsum,0.0558873042464256,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
7028,razent/SciFive-base-PMC,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0871121808886527,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
7029,nghuyong/ernie-1.0,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0225436652544885,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
7030,ctoraman/RoBERTa-TR-medium-wp-66k,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0045126192271709,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
7031,microsoft/layoutlm-base-cased,This model is cased: it makes a difference between english and English,google/fnet-large,0.0685319453477859,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
7032,mimi/Waynehills-NLP-doogie,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0445375367999076,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
7033,sultan/BioM-ELECTRA-Large-Generator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
7034,gayanin/bart-mlm-pubmed-medterm,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0833138525485992,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
7035,bookbot/distil-wav2vec2-adult-child-cls-52m,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0062870147327582,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
7036,google/t5-efficient-mini-nl24,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0532543566077947,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
7037,SupriyaArun/squeezebert-uncased-finetuned-squad,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7038,google/t5-efficient-small-ff9000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0193082862533628,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
7039,KoichiYasuoka/roberta-small-belarusian,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0874079801142215,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
7040,razent/SciFive-base-PMC,This model is block sparse: the linear layers contains 12.5% of the original weights,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0810306519269942,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
7041,google/t5-efficient-small-nl22,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0841758400201796,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
7042,google/t5-efficient-tiny-nl32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
7043,hakurei/gpt-j-random-tinier,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
7044,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0756668597459793,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
7045,google/t5-efficient-large-dm128,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0503740236163139,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
7046,SupriyaArun/squeezebert-uncased-finetuned-squad,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0316669978201389,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7047,SupriyaArun/squeezebert-uncased-finetuned-squad,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0593448057770729,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7048,Milos/slovak-gpt-j-405M,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
7049,google/t5-efficient-large-nh8-nl32,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0779937729239463,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
7050,google/t5-efficient-base-ff6000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0121252564713358,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
7051,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,0.0768543258309364,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
7052,google/t5-efficient-tiny-nl6,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0849873721599578,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
7053,uer/chinese_roberta_L-6_H-128,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.046756212413311,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
7054,google/t5-efficient-large-nh24,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
7055,microsoft/unispeech-sat-base,"In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data",microsoft/unispeech-sat-base,1.0,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
7056,allegro/plt5-large,Its original repository can be found here,tau/splinter-large-qass,0.0316192470490932,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
7057,khizon/distilbert-unreliable-news-eng-4L,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0306104561313986,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
7058,google/reformer-crime-and-punishment,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0281207193620502,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
7059,malay-huggingface/xlnet-large-bahasa-cased,xlnet-large-bahasa-cased model was pretrained on ~1.4 Billion words,malay-huggingface/xlnet-large-bahasa-cased,1.0,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
7060,saurkulsh/T0pp,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.045005680594061,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
7061,sentence-transformers/paraphrase-MiniLM-L3-v2,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0360095053911209,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
7062,google/t5-efficient-large-dm2000,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0344465170055627,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
7063,microsoft/layoutlm-large-uncased,We do hope our model might be used for both academic and industrial applications,asi/gpt-fr-cased-base,0.0487392172217369,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
7064,lgrobol/electra-minuscule-discriminator,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0097787631675601,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
7065,google/bert_uncased_L-10_H-128_A-2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0110978856682777,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
7066,google/t5-efficient-tiny-nl12,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0598355215042828,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
7067,DemangeJeremy/4-sentiments-with-flaubert,WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation,facebook/wmt21-dense-24-wide-en-x,0.0598574569448828,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7068,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,This model is cased: it makes a difference between english and English,google/fnet-large,0.0135261826217174,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
7069,dbmdz/convbert-base-german-europeana-cased,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0463819814225037,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
7070,allenai/wmt19-de-en-6-6-base,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.0074591562151908,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
7071,uer/t5-v1_1-base-chinese-cluecorpussmall,For best results use python as below,pszemraj/led-large-book-summary,0.084156796336174,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
7072,negfir/distilbert-base-uncased-finetuned-squad,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0630448758602142,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
7073,hfl/chinese-electra-180g-small-ex-generator,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
7074,rahulacj/bertweet-base-finetuned-sentiment-analysis,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0994913354516029,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
7075,google/t5-efficient-base-nl36,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0683166589587926,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
7076,hfl/rbtl3,"Pre-Training with Whole Word Masking for Chinese BERTYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping HuThis repository is developed based on：https://github",hfl/rbtl3,1.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7077,google/t5-efficient-large-el6,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
7078,ml6team/mt5-small-german-finetune-mlsum,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,1.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
7079,UWB-AIR/MQDD-duplicates,"For more information, see the paper",UWB-AIR/MQDD-duplicates,1.0,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
7080,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0786876752972602,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
7081,google/bert_uncased_L-6_H-256_A-4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0481574097648262,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
7082,Iskaj/hf-challenge-test,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0912669152021408,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
7083,google/t5-efficient-large-nh8,This does not make a very readable text,pere/DeUnCaser,0.041775994002819,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
7084,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Check out our paper DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts for more details,yarongef/DistilProtBert,0.0999067574739456,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
7085,IDEA-CCNL/Erlangshen-Longformer-110M,"Important: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch)",pszemraj/led-large-book-summary,0.0665640756487846,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
7086,piotr-rybak/poleval2021-task4-herbert-large-encoder,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0961196199059486,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
7087,uer/bart-chinese-4-768-cluecorpussmall,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0910535566508769,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
7088,zoha/wav2vec2-base-timit-demo-colab,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0219328999519348,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
7089,bigjoedata/rockbot,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0019228126232822,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
7090,google/t5-efficient-large-nl10,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0044873170554637,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
7091,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,The article and  summarization text is reduced via extractive summarization before it is finetuned to the model,ml6team/distilbart-tos-summarizer-tosdr,0.0993154793977737,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
7092,google/t5-efficient-mini-nl24,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0122732445597648,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
7093,SupriyaArun/squeezebert-uncased-finetuned-squad,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-768_A-12,0.0785795599222183,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7094,Milos/slovak-gpt-j-405M,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0133158055444558,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
7095,google/t5-efficient-large-nl12,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
7096,google/t5-efficient-base-dm256,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0439480990171432,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
7097,google/t5-efficient-base-nh32,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0317566711455583,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
7098,google/t5-efficient-base-nl4,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.024177448824048,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
7099,PlanTL-GOB-ES/gpt2-large-bne,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
7100,brad1141/Longformer-finetuned-norm,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
7101,google/t5-efficient-base-nl40,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0108954794704913,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
7102,hfl/rbtl3,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.0404477268457412,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7103,anirudh21/xlnet-base-cased-finetuned-rte,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0889797881245613,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
7104,brad1141/Longformer-finetuned-norm,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
7105,patrickvonplaten/unispeech-sat-large-timit-ft,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
7106,google/t5-efficient-base-kv256,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0528815835714339,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
7107,google/t5-efficient-base-kv16,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0610418617725371,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
7108,sentence-transformers/paraphrase-MiniLM-L3-v2,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
7109,microsoft/layoutlm-base-uncased,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
7110,rinna/japanese-gpt-1b,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0038234057525793,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
7111,google/t5-efficient-large-nh24,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0420236391946673,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
7112,google/t5-efficient-mini-nl12,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0721724033355712,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
7113,gchhablani/fnet-base-finetuned-wnli,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0702494084835052,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
7114,google/t5-efficient-small-el8,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0476588048040866,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
7115,google/t5-efficient-base-nl48,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0425980435684322,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
7116,google/t5-efficient-base-ff6000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.081910241395235,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
7117,groar/gpt-neo-1.3B-finetuned-escape2,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
7118,mrp/simcse-model-wangchanberta,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
7119,google/t5-efficient-small-nl40,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0230954196304082,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
7120,superb/hubert-large-superb-sid,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0097134718671441,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
7121,minimaxir/magic-the-gathering,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0632479358464479,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
7122,Fujitsu/pytorrent,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
7123,rahulacj/bertweet-base-finetuned-sentiment-analysis,The models have been converted to pyTorch using the transformers library by huggingface,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0909951105713844,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
7124,google/t5-efficient-large-nl20,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0138324908912181,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
7125,rinna/japanese-gpt2-xsmall,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0308099687099456,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
7126,google/t5-efficient-xl-nl28,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0417465679347514,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
7127,google/t5-efficient-tiny-nl12,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0438000094145536,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
7128,google/byt5-large,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0141290118917822,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
7129,M-FAC/bert-mini-finetuned-stsb,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0099470037966966,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
7130,dbmdz/bert-medium-historic-multilingual-cased,"their training data source:At the moment, the following models are available on the model hub:We also released smaller models for the multilingual model:",dbmdz/bert-medium-historic-multilingual-cased,1.0,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
7131,cointegrated/rubert-tiny-bilingual-nli,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
7132,Iskaj/hf-challenge-test,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.072443351149559,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
7133,1one5ome/gpt2-chinese-gulong,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
7134,stas/mt5-tiny-random,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0720593184232711,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
7135,soheeyang/rdr-ctx_encoder-single-trivia-base,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0796942301094531,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
7136,negfir/distilbert-base-uncased-finetuned-squad,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0988826751708984,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
7137,clue/xlnet_chinese_large,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.002297542989254,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
7138,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0610053688287735,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
7139,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
7140,IDEA-CCNL/Erlangshen-Longformer-110M,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
7141,piotr-rybak/poleval2021-task4-herbert-large-encoder,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
7142,IDEA-CCNL/Erlangshen-MegatronBert-1.3B,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0385038992390036,-1.0,NLP,3c1adb6c-e774-450e-967f-c26c07931f49___Erlangshen-MegatronBert-1.3B,HF
7143,brad1141/Longformer-finetuned-norm,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0150022264569997,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
7144,phueb/BabyBERTa-2,"It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed",phueb/BabyBERTa-2,1.0,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
7145,tau/tavbert-he,T5-Efficient-TINY-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh8,0.0288928318768739,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
7146,tuner007/pegasus_qa,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0119883771985769,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
7147,google/t5-efficient-base-nl36,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0685262717306613,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
7148,google/t5-efficient-tiny-nl6,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
7149,mimi/Waynehills-NLP-doogie,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0998498350381851,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
7150,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0971260368824005,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
7151,google/t5-efficient-large-kv32,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0464057037606835,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
7152,DemangeJeremy/4-sentiments-with-flaubert,"Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example",facebook/m2m100-12B-avg-10-ckpt,0.0607339236885309,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7153,google/t5-efficient-base-el16,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0750718005001544,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
7154,google/t5-efficient-large-el4,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0444230530411004,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
7155,DemangeJeremy/4-sentiments-with-flaubert,This model is a rather small instance of GPT-2 trained on TristanBehrens/js-fakes-4bars,TristanBehrens/js-fakes-4bars,0.0575777534395456,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7156,uer/pegasus-base-chinese-cluecorpussmall,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0658683311194181,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
7157,lgrobol/electra-minuscule-generator,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.062496431171894,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
7158,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0439131192862987,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
7159,1one5ome/gpt2-chinese-gulong,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0006446614861488,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
7160,allenai/wmt16-en-de-12-1,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0136142522096634,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
7161,google/t5-efficient-large-kv32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0102587807923555,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
7162,google/t5-efficient-large-el6,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0152501501142978,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
7163,Rostlab/prot_bert,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,1.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
7164,SetFit/deberta-v3-large__sst2__train-8-8,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0669125393033027,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
7165,stas/t5-very-small-random,"The quality estimation can be done at different levels: document level, sentence level and word level",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0605695284903049,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
7166,beston91/gpt2-xl_ft_logits_10k,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0720190703868866,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
7167,bigjoedata/rockbot,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0078261143838365,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
7168,allenai/wmt16-en-de-dist-6-1,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0101363472640514,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
7169,Cedille/fr-boris,Cedille is a project to bring large language models to non-English languages,Cedille/fr-boris,1.0,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
7170,KoichiYasuoka/roberta-base-ukrainian,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0528135262429714,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
7171,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,0.0854532718658447,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
7172,DeepPavlov/distilrubert-tiny-cased-conversational-v1,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0354354698210954,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
7173,IlyaGusev/rugpt3medium_sum_gazeta,T5-Efficient-LARGE-NH2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh2,0.092648297548294,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
7174,ncduy/phobert-large-finetuned-vietnamese_students_feedback,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0748584643006324,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
7175,zoha/wav2vec2-base-timit-demo-colab,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0488528907299041,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
7176,funnel-transformer/small,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0423409778624772,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
7177,mgreenbe/bertlet-base-uncased-for-sequence-classification,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0487412437796592,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
7178,google/t5-efficient-large-nh8,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
7179,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0788625571876763,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
7180,clue/roberta_chinese_3L312_clue_tiny,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0765222720801829,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
7181,DemangeJeremy/4-sentiments-with-flaubert,"Macaw (Multi-angle c(q)uestion answering) is a ready-to-use model capable of  general question answering,  showing robustness outside the domains it was trained on",allenai/macaw-answer-11b,0.0587037345394492,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7182,google/t5-efficient-mini-nl12,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0531778912991284,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
7183,ToToKr/kobigbird-bert-base-finetuned-klue,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
7184,google/t5-efficient-base-nh32,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
7185,mgreenbe/bertlet-base-uncased-for-sequence-classification,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
7186,Milos/slovak-gpt-j-1.4B,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0071955784223973,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
7187,rahulacj/bertweet-base-finetuned-sentiment-analysis,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0793474242091178,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
7188,minimaxir/magic-the-gathering,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0478071998804807,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
7189,piotr-rybak/poleval2021-task4-herbert-large-encoder,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0796153992414474,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
7190,mmaguero/gn-bert-tiny-cased,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.054348973557353,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
7191,google/t5-efficient-large-dm768,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0581966824829578,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
7192,DemangeJeremy/4-sentiments-with-flaubert,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0790998600423335,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7193,flboehm/reddit-bert-text_5,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0158014614135026,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
7194,mikaelsouza/msft-regular-model,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0547222346067428,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
7195,aubmindlab/aragpt2-mega,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
7196,stas/t5-very-small-random,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0656056106090545,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
7197,google/t5-efficient-xl-nl28,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.061717165634036,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
7198,stas/pegasus-cnn_dailymail-tiny-random,"With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in WMT 2020",TransQuest/monotransquest-hter-en_lv-it-nmt,0.06818438321352,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
7199,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,0.0533894207328557,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
7200,liaad/srl-pt_bertimbau-large,This model is the neuralmind/bert-large-portuguese-cased fine-tuned on Portuguese semantic role labeling data,liaad/srl-pt_bertimbau-large,1.0,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
7201,rmihaylov/bert-base-pos-theseus-bg,"The training data is Bulgarian text from OSCAR, Chitanka and Wikipedia",rmihaylov/bert-base-pos-theseus-bg,1.0,-1.0,NLP,428758d5-6177-42ba-a879-c30586fca3ab___bert-base-pos-theseus-bg,HF
7202,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0743382424116134,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
7203,TJMUCH/transcriptome-iseeek,mdThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-tiny,0.0862267017364502,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
7204,google/t5-efficient-large-dm512,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0240024644881487,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
7205,cammy/led-base-16384-100-MDS,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0905526131391525,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
7206,DTAI-KULeuven/robbertje-1-gb-bort,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0481955800205468,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
7207,google/t5-efficient-base-nh32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0650815926492213,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
7208,gilparmentier/pokemon_gptj_model,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
7209,google/t5-efficient-base-kv256,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0202603265643119,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
7210,nyu-mll/roberta-base-10M-3,"We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens)",nyu-mll/roberta-base-10M-3,1.0,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
7211,superspray/electra_large_discriminator_squad2_custom_dataset,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0960000455379486,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
7212,flboehm/reddit-bert-text_5,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
7213,google/t5-11b-ssm-nqo,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0370050122340519,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
7214,suzuki/distilbert-base-uncased-finetuned-squad,This project was funded by the Language Technology Programme for Icelandic 2019-2023,jonfd/convbert-small-igc-is,0.0705056264996528,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
7215,google/t5-efficient-large-nh4,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0761052295565605,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
7216,google/t5-efficient-small-nl40,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0541558945551514,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
7217,google/t5-efficient-xl-nl2,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0008559199050068,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
7218,abdusahmbzuai/ft-tatoeba-ar-en,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.049409806728363,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
7219,google/t5-efficient-small-dm2000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
7220,castorini/afriberta_small,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0279185252729802,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
7221,lgrobol/electra-minuscule-generator,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.0723733901977538,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
7222,google/t5-efficient-tiny-nl16,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0648048222064971,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
7223,stas/mt5-tiny-random,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0415601357817649,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
7224,dbmdz/bert-medium-historic-multilingual-cased,Our Historic Language Models Zoo contains support for the following languages - incl,dbmdz/bert-medium-historic-multilingual-cased,1.0,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
7225,google/bert_uncased_L-4_H-128_A-2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0110978856682777,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
7226,allenai/macaw-3b,Slovak GPT-J-162M is the first model released in Slovak GPT-J series and the very first publicly available transformer trained predominantly on Slovak corpus,Milos/slovak-gpt-j-162M,0.0439049638807773,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
7227,saibo/random-roberta-mini,com/peterwestuw/GPT2ForwardBackward,danyaljj/opengpt2_pytorch_forward,0.0195553456433117,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
7228,tau/splinter-large-qass,"For the model without those weights, see tau/splinter-large",tau/splinter-large-qass,1.0,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
7229,mrm8488/deberta-v3-small-finetuned-mrpc,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.042203988879919,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
7230,j-hartmann/purchase-intention-english-roberta-large,The models have been converted to pyTorch using the transformers library by huggingface,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
7231,microsoft/deberta-v2-xxlarge-mnli,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0438361940905451,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
7232,google/t5-efficient-base-dm512,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0517967157065867,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
7233,google/t5-efficient-large-el2,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0163427554070949,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
7234,microsoft/layoutlm-base-cased,This model is case-sensitive: it makes a difference between tirana and Tirana,macedonizer/al-roberta-base,0.0514089576900005,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
7235,google/t5-efficient-base-el16,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.048905961215496,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
7236,hfl/rbt3,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.0320176091045141,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
7237,google/t5-efficient-small-dm256,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0486105475574731,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
7238,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0974537283182144,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
7239,superspray/electra_large_discriminator_squad2_custom_dataset,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.040614265948534,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
7240,IDEA-CCNL/Wenzhong-GPT2-3.5B,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
7241,prajjwal1/bert-tiny,"This is one of the smaller pre-trained BERT variants, together with bert-mini bert-small and bert-medium",prajjwal1/bert-tiny,1.0,-1.0,NLP,55d35131-34b4-4b75-b6b3-69d02d502e5a___bert-tiny,HF
7242,google/t5-efficient-small-dm1000,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0744235105812549,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
7243,malay-huggingface/xlnet-large-bahasa-cased,I have made no attempts to censor,bigjoedata/rockbot,0.0572866474588711,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
7244,pulp/CHILDES-ParentBERTo,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0400241017341614,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
7245,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0393245257437229,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
7246,google/t5-efficient-large-el8,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0699748694896697,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
7247,microsoft/layoutlm-large-uncased,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
7248,IDEA-CCNL/Zhouwenwang-Unified-1.3B,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0668694663792847,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
7249,uer/bart-chinese-4-768-cluecorpussmall,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.013936941511929,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
7250,malay-huggingface/t5-super-tiny-bahasa-cased,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
7251,microsoft/layoutlm-base-uncased,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0520309619605541,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
7252,ml6team/mt5-small-german-finetune-mlsum,Amazon's BORT,amazon/bort,0.0534786023199558,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
7253,anas-awadalla/bert-tiny-finetuned-squad,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
7254,nghuyong/ernie-tiny,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0266931702693303,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
7255,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0732176527380943,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
7256,microsoft/layoutlm-large-uncased,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
7257,celine98/canine-s-finetuned-sst2,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0606768876314163,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
7258,google/t5-efficient-large-nh2,This does not make a very readable text,pere/DeUnCaser,0.0255629308521747,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
7259,google/bert_uncased_L-6_H-256_A-4,"We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large",google/bert_uncased_L-6_H-256_A-4,1.0,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
7260,google/t5-efficient-small-dm768,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0682491045445203,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
7261,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el6,0.0325490981340408,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
7262,anirudh21/xlnet-base-cased-finetuned-rte,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0872511640191078,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
7263,google/t5-efficient-base-nh32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
7264,rmihaylov/bert-base-pos-theseus-bg,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0257541580746571,-1.0,NLP,428758d5-6177-42ba-a879-c30586fca3ab___bert-base-pos-theseus-bg,HF
7265,google/t5-efficient-mini-nl24,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
7266,VietAI/gpt-neo-1.3B-vietnamese-news,"As we all know, the single direction language model based on decoder structure has strong generation ability, such as GPT model",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0418860763311386,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
7267,google/t5-efficient-small-dm128,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0477321688085794,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
7268,google/t5-efficient-large-nh4,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0606388822197913,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
7269,razent/SciFive-large-Pubmed,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
7270,DemangeJeremy/4-sentiments-with-flaubert,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7271,google/t5-efficient-base-nl36,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
7272,beston91/gpt2-xl_ft_logits_10k,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0794974938035011,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
7273,gayanin/bart-mlm-pubmed-medterm,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0131208440288901,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
7274,DeepPavlov/distilrubert-tiny-cased-conversational,It can be considered as small copy of Conversational DistilRuBERT-base,DeepPavlov/distilrubert-tiny-cased-conversational,1.0,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
7275,google/t5-efficient-large-dm2000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0311126988381147,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
7276,Norod78/hebrew_poetry-gpt_neo-tiny,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0491491435095667,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
7277,malay-huggingface/t5-super-tiny-bahasa-cased,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0769618637859821,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
7278,mimi/Waynehills-NLP-doogie,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0475096032023429,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
7279,hajime9652/xlnet-japanese,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
7280,superspray/electra_large_discriminator_squad2_custom_dataset,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0921114385128021,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
7281,google/t5-efficient-large-kv32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0064024869352579,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
7282,deep-learning-analytics/triviaqa-t5-base,The input to the model is a Trivia type question,deep-learning-analytics/triviaqa-t5-base,1.0,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
7283,Fujitsu/pytorrent,"The trained model, along with the dataset, aims to help researchers to easily and efficiently work on a large dataset of Python packages using only 5 lines of codes to load the transformer-based model",Fujitsu/pytorrent,1.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
7284,hf-internal-testing/tiny-random-rembert,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0445071198046207,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
7285,google/t5-efficient-tiny-nl24,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0053983442485332,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
7286,flaubert/flaubert_small_cased,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0197385074570774,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
7287,TJMUCH/transcriptome-iseeek,For example usage or to easily use the model you can check our colab notebook: NotebookGenji is a transformer model finetuned on EleutherAI's GPT-J 6B model,NovelAI/genji-python-6B,0.0999500751495361,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
7288,malay-huggingface/t5-tiny-bahasa-cased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0157431631038586,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
7289,antoiloui/belgpt2,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0598463527858256,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
7290,microsoft/deberta-v2-xxlarge-mnli,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0137735386379062,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
7291,stas/pegasus-cnn_dailymail-tiny-random,"Namely, we use a summary level Rouge-L score",andrejmiscic/simcls-scorer-billsum,0.0522529240697621,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
7292,google/t5-efficient-small-el32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-el32,1.0,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
7293,Milos/slovak-gpt-j-162M,Model is based on GPT-J and has over 162M trainable parameters,Milos/slovak-gpt-j-162M,1.0,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
7294,google/t5-efficient-base-dm256,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0748100616037845,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
7295,aditeyabaral/sentencetransformer-distilbert-hinglish-big,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0806409269571304,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
7296,mimicheng/codeparrot-ds,Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering,tau/splinter-base-qass,0.0994292050600051,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
7297,DeepPavlov/distilrubert-tiny-cased-conversational,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
7298,amazon/bort,"The quality estimation can be done at different levels: document level, sentence level and word level",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0264898533932865,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
7299,rmihaylov/pegasus-base-cnn-dailymail-bg,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,1.0,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
7300,anirudh21/xlnet-base-cased-finetuned-rte,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0629865080118179,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
7301,groar/gpt-neo-1.3B-finetuned-escape2,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0913357883691787,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
7302,google/t5-efficient-large-el8,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0147835938259959,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
7303,rmihaylov/bert-base-pos-theseus-bg,It was finetuned on public part-of-speech Bulgarian data,rmihaylov/bert-base-pos-theseus-bg,1.0,-1.0,NLP,428758d5-6177-42ba-a879-c30586fca3ab___bert-base-pos-theseus-bg,HF
7304,fnlp/bart-large-chinese,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0753343999385832,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
7305,google/t5-efficient-small-ff3000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0651930943131446,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
7306,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl24,0.0245603872463106,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7307,razent/SciFive-base-PMC,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0541423372924327,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
7308,google/t5-efficient-base-ff6000,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0538043603301048,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
7309,ductuan024/AimeLaw,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0357217211276292,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
7310,google/t5-efficient-base-dm512,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
7311,mmaguero/gn-bert-tiny-cased,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0586543111130595,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
7312,allenai/wmt16-en-de-dist-6-1,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
7313,google/t5-efficient-base-nl40,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0681621097028255,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
7314,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7315,google/t5-efficient-small-kv16,Its original repository can be found here,tau/splinter-large-qass,0.0737285967916249,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
7316,rinna/japanese-gpt2-xsmall,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0060100369155406,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
7317,hfl/rbtl3,T5-Efficient-SMALL-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl20,0.071111612021923,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7318,stas/t5-very-small-random,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0864907801151275,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
7319,kyryl0s/gpt2-uk-xxs,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
7320,stas/t5-very-small-random,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
7321,facebook/wmt21-dense-24-wide-en-x,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
7322,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0387065112590789,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
7323,stas/mt5-tiny-random,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0943463146686554,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
7324,filco306/gpt2-switchboard-paraphraser,This is the trained Switchboard-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K,filco306/gpt2-switchboard-paraphraser,1.0,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
7325,skt/ko-gpt-trinity-1.2B-v0.5,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0084956157952546,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
7326,google/t5-efficient-small-el12,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0287855546921491,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
7327,uer/pegasus-base-chinese-cluecorpussmall,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0489092739298939,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
7328,celine98/canine-s-finetuned-sst2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0453142970800399,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
7329,microsoft/layoutlm-base-uncased,"MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks",mrm8488/mobilebert-uncased-finetuned-squadv2,0.0287247318774461,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
7330,TJMUCH/transcriptome-iseeek,IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model,indobenchmark/indobert-base-p1,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
7331,tucan9389/kcbert-base-finetuned-squad,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0425369925796985,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
7332,Milos/slovak-gpt-j-405M,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
7333,google/t5-efficient-tiny-nh32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0702360067516565,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
7334,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
7335,google/t5-efficient-small-nl20,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0410312972962855,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
7336,google/t5-efficient-large-dm768,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0106544140726327,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
7337,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.063492015004158,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
7338,tuner007/pegasus_qa,† ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-162M,0.0859560221433639,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
7339,groar/gpt-neo-1.3B-finetuned-escape2,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0405161380767822,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
7340,malay-huggingface/xlnet-large-bahasa-cased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0119829351703325,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
7341,diper1998/distilgpt2-finetuned-AT,This model is a fine-tuned version of distilgpt2 on the None dataset,diper1998/distilgpt2-finetuned-AT,1.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
7342,google/t5-efficient-small-nl24,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
7343,IDEA-CCNL/Zhouwenwang-Unified-110M,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
7344,microsoft/deberta-v2-xxlarge-mnli,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.025412913877517,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
7345,google/t5-efficient-base-el6,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.060721669346094,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
7346,FremyCompany/xls-r-nl-v1-cv8-lm,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
7347,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
7348,google/t5-efficient-small-nl20,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0629547405987977,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
7349,hf-internal-testing/tiny-random-rembert,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0997876897454261,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
7350,IDEA-CCNL/Zhouwenwang-Unified-1.3B,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0362393546849488,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
7351,google/t5-efficient-base-nl4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0287184324115514,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
7352,1one5ome/gpt2-chinese-gulong,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0106029072776436,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
7353,anirudh21/xlnet-base-cased-finetuned-rte,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0240932311862707,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
7354,DeepPavlov/distilrubert-tiny-cased-conversational,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0539480832715828,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
7355,minimaxir/magic-the-gathering,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0005137287080287,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
7356,google/t5-efficient-base-el8,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0231392756104469,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
7357,Iskaj/hf-challenge-test,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0917336344718933,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
7358,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0462622232735157,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
7359,sonoisa/byt5-small-japanese,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0948260203003883,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
7360,celine98/canine-s-finetuned-sst2,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0416882634162902,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
7361,jweb/japanese-soseki-gpt2-1b,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
7362,SupriyaArun/squeezebert-uncased-finetuned-squad,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0843714550137519,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7363,google/t5-efficient-small-kv16,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0011328216642141,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
7364,hfl/rbt3,T5-Efficient-XL-NL28 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl28,0.0523104444146155,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
7365,aubmindlab/aragpt2-mega,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
7366,gayanin/bart-mlm-pubmed-medterm,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0502577647566795,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
7367,google/t5-efficient-small-nl2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0141898104920983,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
7368,patrickvonplaten/unispeech-sat-large-timit-ft,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.022510677576065,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
7369,sentence-transformers/paraphrase-MiniLM-L3-v2,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0736103057861328,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
7370,hfl/rbtl3,T5-Efficient-LARGE-NH2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh2,0.0317381834611296,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7371,tobiaslee/bert-2l-768h-uncased,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0064419899135828,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
7372,cammy/led-base-16384-100-MDS,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
7373,google/bigbird-roberta-large,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0235470701009035,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
7374,DemangeJeremy/4-sentiments-with-flaubert,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.0821062698960304,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7375,mimi/Waynehills-NLP-doogie,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.0749981850385665,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
7376,KBLab/megatron-bert-base-swedish-cased-600k,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
7377,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl2,0.0170276975259185,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7378,allegro/plt5-large,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0637168499330678,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
7379,google/t5-efficient-large-kv16,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
7380,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0904406383633613,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
7381,abdusahmbzuai/ft-tatoeba-ar-en,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0285652875900268,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
7382,IDEA-CCNL/Zhouwenwang-Unified-110M,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
7383,suzuki/distilbert-base-uncased-finetuned-squad,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0839645788073539,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
7384,cammy/led-large-16384-arxiv-100-MDS,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0317298658192157,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
7385,fnlp/cpt-base,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
7386,stas/pegasus-cnn_dailymail-tiny-random,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
7387,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0982211828231811,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
7388,ian0delond/model-test,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0443289466202259,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
7389,SetFit/deberta-v3-large__sst2__train-8-8,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0716357082128524,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
7390,pere/DeUnCaser,It is using based on the multi-lingual base model,pere/DeUnCaser,1.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
7391,google/t5-efficient-base-dm1000,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0529557988047599,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
7392,jnz/electra-ka,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0487776696681976,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
7393,mikaelsouza/msft-regular-model,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0568872019648551,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
7394,saibo/random-roberta-tiny,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0219696424901484,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
7395,amazon/bort,Such configuration may be used for tasks such as automatic summary or question answering,asi/gpt-fr-cased-base,0.0644110068678855,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
7396,gayanin/bart-mlm-pubmed-medterm,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0257852207869291,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
7397,nghuyong/ernie-tiny,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
7398,suzuki/distilbert-base-uncased-finetuned-squad,It was introduced in this paper,readerbench/RoBERT-small,0.0020536361262202,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
7399,aubmindlab/aragpt2-large,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0091884157154708,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
7400,sentence-transformers/paraphrase-MiniLM-L3-v2,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0462622232735157,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
7401,google/t5-efficient-small-kv32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0427126064896583,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
7402,stas/mt5-tiny-random,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
7403,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0046126600354909,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
7404,nghuyong/ernie-tiny,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0452953049292166,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
7405,celine98/canine-s-finetuned-sst2,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0661486759781837,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
7406,google/t5-efficient-base-nh8,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0722873844206333,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
7407,sentence-transformers/paraphrase-MiniLM-L3-v2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0973472148180008,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
7408,google/t5-efficient-base-nl40,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0321916406974196,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
7409,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0082807429134845,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
7410,zoha/wav2vec2-base-timit-demo-colab,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0646452382206916,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
7411,damlab/GO-language,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0229999204683634,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
7412,IDEA-CCNL/Erlangshen-Longformer-330M,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0583125036209821,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
7413,google/t5-efficient-xl-nl2,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0437085591256618,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
7414,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.087757371366024,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
7415,mwesner/reformer-clm,Colab: linkEvaluation script: evaluate,IlyaGusev/rugpt3medium_sum_gazeta,0.0987233072519302,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
7416,allenai/wmt19-de-en-6-6-base,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0686275288462638,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
7417,jonfd/convbert-small-igc-is,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,1.0,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
7418,google/t5-efficient-base-ff6000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
7419,KBLab/megatron-bert-base-swedish-cased-600k,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.029886204128464,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
7420,google/t5-efficient-small-el32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0414451383985579,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
7421,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0509164258837699,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
7422,google/t5-efficient-tiny-ff3000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0256671141833066,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
7423,CuongLD/wav2vec2-large-xlsr-vietnamese,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
7424,VietAI/gpt-neo-1.3B-vietnamese-news,"Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words",macedonizer/al-roberta-base,0.0530754830688237,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
7425,amazon/bort,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
7426,google/t5-efficient-base-dm512,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.00095478259027,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
7427,Iskaj/hf-challenge-test,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.08577561378479,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
7428,kssteven/ibert-roberta-base,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
7429,cross-encoder/nli-deberta-v3-xsmall,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0184093989431858,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
7430,aliosm/ComVE-gpt2-large,You can use this model directly to generate reasons why the given statement is against commonsense using generate,aliosm/ComVE-gpt2-large,1.0,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
7431,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.0614658538252114,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
7432,google/t5-efficient-mini,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
7433,microsoft/layoutlm-large-uncased,"CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationYunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu",fnlp/cpt-base,0.0389067530632019,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
7434,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh24,0.06109656766057,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
7435,IDEA-CCNL/Erlangshen-Longformer-110M,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0462231198325752,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
7436,google/t5-efficient-xl-nl6,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0755810737609863,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
7437,mwesner/reformer-clm,T5-Efficient-SMALL-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv32,0.0746063590049743,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
7438,google/t5-efficient-base-dm2000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.029312661383301,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
7439,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
7440,everdoubling/byt5-Korean-small,"This model has been fine-tuned by the HuggingSound tool, and thanks to the GPU credits generously given by the OVHcloud :)Using the HuggingSound library:",jonatasgrosman/wav2vec2-xls-r-1b-english,0.051458515971899,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
7441,google/t5-efficient-large-el2,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.086310364305973,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
7442,google/t5-efficient-base-ff12000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.040610060095787,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
7443,clue/xlnet_chinese_large,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.026267122477293,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
7444,jorge-henao/gpt2-small-spanish-disco-poetry-wt,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0851453468203544,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
7445,Davlan/mbart50-large-eng-yor-mt,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,12474c75-dc49-473e-bcb6-eea8354b1253___mbart50-large-eng-yor-mt,HF
7446,mmaguero/gn-bert-tiny-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0034072073176503,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
7447,rdenadai/BR_BERTo,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0531128458678721,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
7448,google/t5-efficient-tiny-nl2,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0425658114254474,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
7449,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"As shown in the Metrics section, the small version (5 times fewer parameters) performs slightly worse, while the medium version (3 times fewer parameters) outperforms the BETO based zero-shot classifier",Recognai/zeroshot_selectra_small,0.0867523252964019,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
7450,google/t5-efficient-small-el12,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0818193107843399,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
7451,amazon/bort,Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective,Finnish-NLP/convbert-base-generator-finnish,0.0773968622088432,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
7452,google/t5-efficient-large-nl36,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
7453,suzuki/distilbert-base-uncased-finetuned-squad,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.047788456082344,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
7454,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
7455,anas-awadalla/bert-tiny-finetuned-squad,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0947480052709579,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
7456,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0428745858371257,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
7457,SetFit/deberta-v3-large__sst2__train-8-8,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0400517135858535,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
7458,KBLab/megatron-bert-base-swedish-cased-600k,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,1.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
7459,IDEA-CCNL/Wenzhong-GPT2-3.5B,"As we all know, the single direction language model based on decoder structure has strong generation ability, such as GPT model",IDEA-CCNL/Wenzhong-GPT2-3.5B,1.0,-1.0,NLP,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
7460,google/t5-efficient-small-nl40,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0406451188027858,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
7461,google/t5-efficient-large-nh24,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0679967179894447,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
7462,tuner007/pegasus_qa,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0098936930298805,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
7463,google/t5-efficient-large-nl36,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0768405981361865,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
7464,google/t5-efficient-base-el8,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0025492841377854,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
7465,google/t5-efficient-large-kv256,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0477957893162965,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
7466,google/t5-efficient-base-nl2,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.04894077219069,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
7467,google/byt5-xl,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,1.0,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
7468,google/t5-efficient-small-ff12000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0600948967039584,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
7469,google/t5-efficient-small-nl48,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0304601993411779,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
7470,amazon/bort,Only use this model for retraining and mask-filling,jcblaise/electra-tagalog-small-cased-generator,0.0369954332709312,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
7471,DemangeJeremy/4-sentiments-with-flaubert,"This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection",hfl/chinese-xlnet-mid,0.0778038371354341,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7472,eli4s/Bert-L12-h384-A6,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.033453308045864,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
7473,google/t5-efficient-tiny-ff9000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0283448831178248,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
7474,google/t5-efficient-tiny-el8,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.017167080193758,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
7475,google/t5-efficient-base-dm512,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0511250272393226,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
7476,google/t5-efficient-xl-nl4,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0586360096931457,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
7477,hfl/rbtl3,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0242930487729608,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7478,mwesner/reformer-clm,T5-Efficient-XXL-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xxl-nl4,0.0833581387996673,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
7479,google/t5-efficient-base-nh24,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0090519031509756,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
7480,malay-huggingface/t5-super-super-tiny-bahasa-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0435369197900096,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
7481,cammy/led-large-16384-arxiv-100-MDS,They used a generative recurrent neural network (RNN) containing long short‐term memory (LSTM) cell to capture the syntax of molecular representations in terms of SMILES strings,mrm8488/chEMBL_smiles_v1,0.0738726407289505,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
7482,google/t5-efficient-small-ff9000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0572173949331045,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
7483,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh24,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7484,google/t5-efficient-xl-nl8,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0271176588721573,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
7485,google/t5-efficient-small-dm1000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0448228390887378,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
7486,uer/t5-v1_1-small-chinese-cluecorpussmall,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0175708401948214,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
7487,allenai/macaw-answer-11b,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0714192303518453,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
7488,nherve/flaubert-oral-asr,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
7489,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0207571387290954,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
7490,stas/mt5-tiny-random,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0677297338843345,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
7491,google/t5-efficient-small-kv16,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0809921883046626,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
7492,google/t5-efficient-small-dm128,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
7493,google/t5-efficient-base-nl32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
7494,hfl/rbtl3,T5-Efficient-BASE-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff1000,0.0403637932613491,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7495,bolbolzaban/gpt2-persian,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0677170604467391,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
7496,google/t5-efficient-small-ff1000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.025180336087942,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
7497,dhpollack/distilbert-dummy-sentiment,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0376618462614715,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
7498,google/t5-efficient-small-el8,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0019157268106937,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
7499,google/t5-efficient-base-nh32,T5-Efficient-BASE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh32,1.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
7500,google/t5-efficient-large-nl20,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0605889931321143,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
7501,google/t5-efficient-mini-nl24,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0540953911840915,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
7502,kyryl0s/gpt2-uk-xxs,The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1,nyu-mll/roberta-med-small-1M-3,0.054667180404067,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
7503,ErykWdowiak/GPTalian,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0029338356107473,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
7504,stas/t5-very-small-random,The corpus was tokenized for pretraining with MeCab,ptaszynski/yacis-electra-small-japanese,0.0980265289545059,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
7505,Iskaj/hf-challenge-test,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0371036008000373,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
7506,skplanet/dialog-koelectra-small-generator,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,1.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
7507,hfl/rbtl3,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,0.054982422851026,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7508,google/t5-efficient-base-ff1000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0404513711109756,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
7509,google/t5-efficient-small-ff9000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0639441609382629,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
7510,brad1141/Longformer-finetuned-norm,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0851853638887405,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
7511,google/t5-efficient-xl-nl4,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0962671339511871,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
7512,uer/pegasus-base-chinese-cluecorpussmall,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0619337335228919,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
7513,hfl/chinese-legal-electra-base-generator,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0521869336565335,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
7514,cmarkea/distilcamembert-base-qa,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
7515,microsoft/layoutlm-base-cased,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0218782499432563,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
7516,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0777615532279014,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
7517,google/t5-efficient-small-el12,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0946395583450794,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
7518,ian0delond/model-test,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0987539738416671,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
7519,google/t5-efficient-small-dm768,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0755980536341666,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
7520,TJMUCH/transcriptome-iseeek,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0994026586413383,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
7521,hfl/chinese-electra-small-ex-discriminator,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
7522,google/t5-efficient-small-kv32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0263169948011636,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
7523,google/t5-efficient-tiny-nl6,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.071037134155631,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
7524,mwesner/bart-mlm,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0933466553688049,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
7525,mrm8488/xlm-multi-finetuned-xquadv1,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0959670580923557,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
7526,google/t5-efficient-large-el4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0305222815368324,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
7527,tartuNLP/gpt-4-est-base,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0729137510061263,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
7528,google/t5-efficient-mini-nl12,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0424316292628645,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
7529,superb/hubert-large-superb-sid,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
7530,negfir/distilbert-base-uncased-finetuned-cola,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0250639487057924,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
7531,google/t5-efficient-xl-nl8,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0475619677454232,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
7532,piotr-rybak/poleval2021-task4-herbert-large-encoder,These models were trained using the lamb optimizer and follow the same architecture as gpt2 and are fully compatible with the transformers library,aubmindlab/aragpt2-mega,0.0960486307740211,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
7533,asi/gpt-fr-cased-base,The model might be used through the astonishing 🤗 Transformers librairie,asi/gpt-fr-cased-base,1.0,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
7534,malay-huggingface/t5-super-tiny-bahasa-cased,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0457289179321378,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
7535,beston91/gpt2-xl_ft_logits_10k,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0979820638895034,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
7536,google/t5-efficient-base-nh8,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.00764836743474,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
7537,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv32,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7538,Davlan/m2m100_418M-eng-yor-mt,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0063721887767314,-1.0,NLP,22200502-47bf-477d-b7e3-dec71a3aae90___m2m100_418M-eng-yor-mt,HF
7539,piotr-rybak/poleval2021-task4-herbert-large-encoder,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0580649226903915,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
7540,google/t5-efficient-small-nl20,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0514580141752957,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
7541,google/t5-efficient-base-nl36,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0028360858559608,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
7542,amazon/bort,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0090932734310627,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
7543,brad1141/Longformer-finetuned-norm,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.03379388153553,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
7544,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0364352703094481,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
7545,KoichiYasuoka/roberta-base-ukrainian,"For the model without those weights, see tau/splinter-large",tau/splinter-large-qass,0.0654965937137603,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
7546,mimi/Waynehills-NLP-doogie,ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-1.4B,0.0966576635837555,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
7547,eli4s/Bert-L12-h256-A4,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0443019103258847,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
7548,Iskaj/hf-challenge-test,The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1,nyu-mll/roberta-med-small-1M-3,0.0736008435487747,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
7549,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,1.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
7550,google/t5-efficient-small-nl40,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0627804491668939,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
7551,mimicheng/codeparrot-ds,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
7552,IDEA-CCNL/Erlangshen-Longformer-110M,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
7553,monsoon-nlp/dv-muril,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0633425340056419,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
7554,piotr-rybak/poleval2021-task4-herbert-large-encoder,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
7555,lgrobol/electra-minuscule-generator,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,1.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
7556,Palak/microsoft_deberta-base_squad,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0355241224169731,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
7557,obss/mt5-base-3task-highlight-tquad2,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.047474866732955,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
7558,hfl/rbt3,T5-Efficient-SMALL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl2,0.0669981501996516,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
7559,hfl/chinese-electra-180g-small-ex-discriminator,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0542604761819044,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
7560,google/t5-efficient-base-el8,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
7561,google/t5-efficient-tiny-nh32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0057470425963402,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
7562,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.0490262880921363,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
7563,obss/mt5-base-3task-highlight-tquad2,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0241188853979111,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
7564,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.09659044444561,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
7565,negfir/distilbert-base-uncased-finetuned-cola,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0500139817595481,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
7566,ncduy/phobert-large-finetuned-vietnamese_students_feedback,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0337502658367157,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
7567,DemangeJeremy/4-sentiments-with-flaubert,"The model is trained on around ~11B tokens (64 size batch, 512 tokens, 350k steps)",flax-community/bengali-t5-base,0.0475522363558411,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7568,lannelin/bert-imdb-1hidden,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0420852055152257,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
7569,KBLab/megatron-bert-base-swedish-cased-600k,For best results use python as below,pszemraj/led-large-book-summary,0.0139998017499843,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
7570,google/t5-efficient-large-nl16,T5-Efficient-LARGE-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl16,1.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
7571,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-XL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl2,0.036559933796525,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7572,IlyaGusev/rugpt3medium_sum_gazeta,T5-Efficient-TINY-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh8,0.0872632190585136,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
7573,razent/SciFive-large-Pubmed,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0865097381174564,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
7574,google/t5-efficient-tiny-nl32,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0693804882466792,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
7575,google/t5-efficient-large-nh4,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0915814377367496,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
7576,google/t5-efficient-small-kv256,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0479567302390932,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
7577,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0536758154630661,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
7578,mimicheng/codeparrot-ds,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0640570744872093,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
7579,VietAI/gpt-neo-1.3B-vietnamese-news,XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective,xlnet-large-cased,0.0910547748208045,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
7580,gayanin/bart-mlm-pubmed-medterm,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0714460909366607,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
7581,superspray/electra_large_discriminator_squad2_custom_dataset,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0665444955229759,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
7582,superb/hubert-base-superb-er,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0227535255253315,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
7583,allenai/wmt16-en-de-dist-6-1,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
7584,google/t5-efficient-large-nl16,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
7585,mrm8488/deberta-v3-small-finetuned-mrpc,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0097214225679636,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
7586,google/t5-efficient-base-nh16,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0704439505934715,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
7587,google/t5-efficient-small-ff3000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0605426914989947,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
7588,google/t5-efficient-large-dm512,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0781776830554007,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
7589,negfir/distilbert-base-uncased-finetuned-squad,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0538432151079177,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
7590,gchhablani/fnet-base-finetuned-wnli,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.047585841268301,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
7591,stas/t5-very-small-random,Its original repository can be found here,tau/splinter-large-qass,0.0089332610368728,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
7592,google/t5-efficient-large-el6,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.084860224276781,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
7593,mrm8488/bert-small-finetuned-squadv2,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0398205826058983,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
7594,Milos/slovak-gpt-j-405M,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0478422644858558,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
7595,KoichiYasuoka/roberta-base-ukrainian,"They were trained with the FlauBERT software using the same parameters as the flaubert-base-uncased model (12 layers, 12 attention heads, 768 dims, 137M parameters, uncased)",nherve/flaubert-oral-ft,0.0934295207262039,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
7596,google/t5-efficient-small-nl2,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0475339405238627,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
7597,facebook/blenderbot-3B,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
7598,sberbank-ai/rugpt3large_based_on_gpt2,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.0428049052134156,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
7599,google/t5-efficient-small-kv32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0676857158541679,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
7600,negfir/distilbert-base-uncased-finetuned-squad,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.047788456082344,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
7601,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0340879783034324,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
7602,microsoft/layoutlm-base-uncased,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0605222806334495,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
7603,microsoft/layoutlm-base-uncased,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0656346306204795,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
7604,rdenadai/BR_BERTo,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0166233504811922,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
7605,nherve/flaubert-oral-ft,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0638026148080825,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
7606,google/t5-efficient-mini-nl6,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
7607,google/t5-efficient-base-nl48,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
7608,nherve/flaubert-oral-ft,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0187643598765134,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
7609,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0747238844633102,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
7610,lassl/gpt2-ko-small,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0436799088492989,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
7611,SupriyaArun/squeezebert-uncased-finetuned-squad,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0174486711621284,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7612,IlyaGusev/rugpt3medium_sum_gazeta,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
7613,stas/mt5-tiny-random,WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation,facebook/wmt21-dense-24-wide-en-x,0.0983696579933166,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
7614,google/t5-efficient-small-ff1000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0352137694135308,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
7615,mimicheng/codeparrot-ds,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0157067831605672,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
7616,google/t5-efficient-large-nl20,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0705559346824884,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
7617,google/t5-efficient-base-kv256,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0486698821187018,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
7618,zoha/wav2vec2-base-timit-demo-colab,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0869917646050453,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
7619,google/t5-efficient-large-nh32,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0598142687231302,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
7620,google/t5-efficient-xl-nl4,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.053769439458847,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
7621,google/t5-efficient-small-nl22,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0483181346207856,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
7622,SupriyaArun/squeezebert-uncased-finetuned-squad,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7623,tucan9389/kcbert-base-finetuned-squad,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
7624,google/t5-efficient-large-nl20,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0742161832749843,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
7625,google/t5-efficient-base-nl36,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
7626,nyu-mll/roberta-med-small-1M-3,The hyperparameters and validation perplexities corresponding to each model are as follows:The hyperparameters corresponding to model sizes mentioned above are as follows:,nyu-mll/roberta-med-small-1M-3,1.0,-1.0,NLP,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
7627,microsoft/layoutlm-base-uncased,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0663190186023712,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
7628,google/t5-efficient-large-kv16,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0551464185118674,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
7629,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0831720381975174,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
7630,Milos/slovak-gpt-j-405M,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0674688505629697,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
7631,superspray/electra_large_discriminator_squad2_custom_dataset,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0415670946240425,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
7632,stas/t5-very-small-random,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0088687557727098,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
7633,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0870294868946075,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
7634,tuner007/pegasus_qa,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0435649640858173,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
7635,allenai/macaw-answer-11b,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0563135134677091,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
7636,mimicheng/codeparrot-ds,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.021810470148921,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
7637,hfl/rbtl3,T5-Efficient-TINY-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff12000,0.0634988211095332,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7638,superspray/electra_large_discriminator_squad2_custom_dataset,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0036080628633499,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
7639,patrickvonplaten/unispeech-sat-large-timit-ft,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
7640,Vilnius-Lithuania-iGEM/Albumin,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
7641,uer/pegasus-base-chinese-cluecorpussmall,"You can download the set of Chinese PEGASUS models either from the UER-py Modelzoo page, or via HuggingFace from the links below:You can use this model directly with a pipeline for text2text generation (take the case of PEGASUS-Base):",uer/pegasus-base-chinese-cluecorpussmall,1.0,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
7642,mmaguero/gn-bert-tiny-cased,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0783276371657848,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
7643,google/t5-efficient-large-nl36,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0323654450476169,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
7644,google/t5-efficient-small-ff9000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0318284546956419,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
7645,dbmdz/electra-base-turkish-cased-discriminator,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0065520112402736,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
7646,microsoft/layoutlm-base-uncased,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
7647,VietAI/gpt-neo-1.3B-vietnamese-news,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0346597228199243,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
7648,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0802391767501831,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
7649,google/t5-efficient-small-nl48,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0639516133815049,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
7650,anas-awadalla/bert-tiny-finetuned-squad,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0765395909547805,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
7651,mwesner/bart-mlm,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.086286686360836,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
7652,google/t5-efficient-tiny-nh8,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0855924077332019,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
7653,allenai/macaw-3b,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
7654,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,0.0589910745620727,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
7655,google/t5-efficient-large-nh32,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0451399125158786,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
7656,KBLab/megatron-bert-large-swedish-cased-165k,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
7657,google/t5-efficient-base-kv16,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0290465038269758,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
7658,google/t5-efficient-large-dm512,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0486945994198322,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
7659,tuner007/pegasus_qa,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0634094178676605,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
7660,google/t5-efficient-xl-nl28,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0623475238680839,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
7661,google/t5-efficient-base-el16,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0052859098650515,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
7662,fnlp/bart-large-chinese,"The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019",PlanTL-GOB-ES/gpt2-large-bne,0.0872203595936298,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
7663,piotr-rybak/poleval2021-task4-herbert-large-encoder,"This model is the fine-tuned version of ""DeBERTa v2 XLarge""  using CUAD dataset https://huggingface",akdeniz27/deberta-v2-xlarge-cuad,0.0615733489394187,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
7664,aditeyabaral/sentencetransformer-bert-hinglish-small,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,aditeyabaral/sentencetransformer-bert-hinglish-small,1.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
7665,google/t5-efficient-small-kv256,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
7666,Recognai/selectra_medium,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0152606237679719,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
7667,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"BERT model with 2 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16)",aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,1.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
7668,google/t5-efficient-base-nl2,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0743673630058765,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
7669,google/t5-efficient-small-kv256,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0107305273413657,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
7670,google/t5-efficient-small-el16-dl4,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0711811445653438,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
7671,google/t5-efficient-base-ff1000,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0876859612762927,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
7672,hfl/chinese-xlnet-mid,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0095427843431631,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
7673,tartuNLP/gpt-4-est-large,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0838677138090133,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
7674,groar/gpt-neo-1.3B-finetuned-escape2,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0805856138467788,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
7675,google/t5-efficient-tiny-ff12000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0362333571538328,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
7676,stas/t5-very-small-random,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
7677,mrm8488/xlm-multi-finetuned-xquadv1,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0205181930214167,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
7678,google/t5-efficient-base-nh16,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0170254372060298,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
7679,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
7680,beston91/gpt2-xl_ft_logits_10k,For more details please refer to the official website,flaubert/flaubert_small_cased,0.052945002913475,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
7681,uer/t5-v1_1-base-chinese-cluecorpussmall,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0257771909236908,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
7682,aubmindlab/aragpt2-large,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.018760279752314,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
7683,google/t5-efficient-large-nh12,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0200072173029183,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
7684,gilparmentier/pokemon_gptj_model,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
7685,ml6team/mt5-small-german-finetune-mlsum,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
7686,razent/SciFive-base-PMC,It has less than 1% difference in performance compared to the BERT based model while having 2.0x the speed,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
7687,google/t5-efficient-large-nh24,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.070670086890459,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
7688,abdusahmbzuai/aradia-ctc-distilhubert-ft,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0792267099022865,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
7689,microsoft/layoutlm-large-uncased,"This is one of the smaller pre-trained BERT variants, together with bert-tiny, bert-mini and bert-small",prajjwal1/bert-medium,0.0120594892650842,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
7690,superspray/electra_large_discriminator_squad2_custom_dataset,I have made no attempts to censor,bigjoedata/rockbot,0.0959591642022132,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
7691,microsoft/layoutlm-base-cased,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0429590754210948,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
7692,SetFit/deberta-v3-large__sst2__train-8-8,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0410471856594085,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
7693,google/t5-efficient-small-ff1000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0567387491464614,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
7694,google/t5-efficient-base-dm2000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
7695,superb/hubert-large-superb-sid,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0451762173324823,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
7696,ductuan024/AimeLaw,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.034153969027102,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
7697,KBLab/megatron-bert-base-swedish-cased-600k,"The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden",KBLab/megatron-bert-base-swedish-cased-600k,1.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
7698,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0238567925989627,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
7699,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.083972230553627,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
7700,malay-huggingface/bert-tiny-bahasa-cased,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0715104701618353,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
7701,jonfd/convbert-small-igc-is,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0555950410664081,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
7702,google/t5-efficient-small-dm768,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
7703,google/t5-efficient-large-kv256,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0502813728526234,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
7704,kyryl0s/gpt2-uk-xxs,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
7705,allenai/wmt19-de-en-6-6-base,Final perplexity on test set is 13.6,sberbank-ai/rugpt3large_based_on_gpt2,0.0475920382887124,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
7706,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,This does not make a very readable text,pere/DeUnCaser,0.0889205783605575,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
7707,mrm8488/bert-small-finetuned-squadv2,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0778596252202987,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
7708,google/t5-efficient-tiny-nh16,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0289739239960908,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
7709,allenai/wmt16-en-de-12-1,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0610625930130481,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
7710,uer/t5-v1_1-small-chinese-cluecorpussmall,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.025027111172676,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
7711,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0320279896259307,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
7712,google/t5-efficient-mini-nl8,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0203552003949879,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
7713,bolbolzaban/gpt2-persian,bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:Please refer to this blog post for further detail,bolbolzaban/gpt2-persian,1.0,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
7714,google/t5-efficient-mini-nl12,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.066466899588704,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
7715,malay-huggingface/bert-tiny-bahasa-cased,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0710369062920411,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
7716,mrm8488/deberta-v3-small-finetuned-mrpc,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0890209451317787,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
7717,ml6team/mt5-small-german-finetune-mlsum,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,1.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
7718,DemangeJeremy/4-sentiments-with-flaubert,Zhouwenwang-Unified-110M is the largest model for LM and MLM tasks in the Chinese field,IDEA-CCNL/Zhouwenwang-Unified-110M,0.0553785935044288,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7719,google/t5-efficient-base-kv256,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0641356231644749,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
7720,google/t5-efficient-xl-nl16,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0225469730794429,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
7721,TJMUCH/transcriptome-iseeek,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0677963867783546,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
7722,Palak/microsoft_deberta-base_squad,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.083612322807312,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
7723,google/t5-efficient-large-nh4,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.038379842415452,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
7724,allenai/wmt19-de-en-6-6-big,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0797216705977916,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
7725,hfl/chinese-electra-180g-small-ex-generator,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
7726,google/t5-efficient-xl-nl6,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0971532016992569,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
7727,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff9000,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7728,tucan9389/kcbert-base-finetuned-squad,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0065389964729547,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
7729,beston91/gpt2-xl_ft_logits_10k,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0891536548733711,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
7730,google/t5-efficient-xl-nl16,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0692196562886238,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
7731,yarongef/DistilProtBert,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0542067891607681,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
7732,SupriyaArun/squeezebert-uncased-finetuned-squad,It was introduced in this paper,readerbench/RoBERT-small,0.0772494226694107,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7733,malay-huggingface/t5-super-tiny-bahasa-cased,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0309789385646581,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
7734,negfir/distilbert-base-uncased-finetuned-cola,Pretrained XLNET large language model for Malay,malay-huggingface/xlnet-large-bahasa-cased,0.0916408225893974,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
7735,google/t5-efficient-small-el32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0575530249625443,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
7736,microsoft/layoutlm-large-uncased,"Hebrew poetry text generation model, fined tuned upon hebrew-gpt_neo-tiny which was trained using EleutherAI's gpt-neo",Norod78/hebrew_poetry-gpt_neo-tiny,0.0490710325539112,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
7737,google/t5-efficient-base-nl32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.011047899723053,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
7738,google/t5-efficient-large-dm768,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.011075478978455,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
7739,google/t5-efficient-base-kv16,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0561476796865463,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
7740,amazon/bort,"This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",tau/splinter-base-qass,0.0195796969346701,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
7741,google/t5-efficient-mini-nl24,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0727171376347541,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
7742,pulp/CHILDES-ParentBERTo,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0543413273990153,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
7743,google/t5-efficient-tiny-nl6,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0396277671679854,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
7744,google/t5-efficient-xxl-nl4,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0445391908288001,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
7745,Recognai/zeroshot_selectra_small,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
7746,DemangeJeremy/4-sentiments-with-flaubert,"Its sister model used the same setup, but was instead trained for only 125k steps",KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7747,aditeyabaral/sentencetransformer-bert-hinglish-small,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0567079186439514,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
7748,TJMUCH/transcriptome-iseeek,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0756454467773437,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
7749,google/t5-efficient-tiny-nl12,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0601887442171573,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
7750,mimi/Waynehills-NLP-doogie,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0300201177597045,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
7751,hfl/chinese-electra-180g-small-ex-generator,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0521869336565335,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
7752,google/t5-efficient-base-nl48,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0587448365986346,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
7753,google/t5-efficient-tiny-nl16,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0200936850160359,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
7754,google/t5-efficient-small-nl40,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0102452589198947,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
7755,KoichiYasuoka/roberta-base-ukrainian,This model used the pre-trained weights of distilbert-base-cased as starting point (only 4 layers) and was able to achieve 84% accuracy on the test set,khizon/distilbert-unreliable-news-eng-4L,0.0878077447414398,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
7756,aditeyabaral/sentencetransformer-distilbert-hinglish-big,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0917815119028091,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
7757,google/t5-efficient-large-nh8-nl32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0621421877294778,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
7758,google/t5-efficient-large-dm512,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0530208274722098,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
7759,pulp/CHILDES-ParentBERTo,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
7760,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0653170943260192,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
7761,kyryl0s/gpt2-uk-xxs,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
7762,obss/mt5-base-3task-highlight-tquad2,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0534000992774962,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
7763,VietAI/gpt-neo-1.3B-vietnamese-news,"This model CANNOT be used without using nn_pruning optimize_model function, as it uses NoNorms instead of LayerNorms and this is not currently supported by the Transformers library",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
7764,google/t5-efficient-large-kv256,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0109878256917,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
7765,superspray/electra_large_discriminator_squad2_custom_dataset,"The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (eretrieved with ElasticSearch)",cross-encoder/ms-marco-MiniLM-L-2-v2,0.0923464819788932,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
7766,google/t5-efficient-xl-nl8,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
7767,TsinghuaAI/CPM-Generate,The details of our training data are shown as follows,TsinghuaAI/CPM-Generate,1.0,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
7768,google/t5-efficient-large-dm768,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0464778379537164,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
7769,uer/pegasus-base-chinese-cluecorpussmall,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0533454408869147,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
7770,mimi/Waynehills-NLP-doogie,PaperUse it like how you use XLM :)The training corpus is solely from Wikipedia so the model may perform worse on informal text data,microsoft/unihanlm-base,0.0869340300559997,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
7771,VietAI/gpt-neo-1.3B-vietnamese-news,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0513160321861504,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
7772,google/t5-efficient-small-el32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0475377878174185,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
7773,google/t5-efficient-base-nh16,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0876296311616897,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
7774,google/t5-efficient-small-ff9000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0229980200529098,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
7775,FremyCompany/xls-r-nl-v1-cv8-lm,"This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus",FremyCompany/xls-r-nl-v1-cv8-lm,1.0,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
7776,cmarkea/distilcamembert-base-qa,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
7777,brad1141/Longformer-finetuned-norm,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0610758140683174,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
7778,sberbank-ai/rugpt3large_based_on_gpt2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
7779,thu-coai/LongLM-large,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,1.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
7780,microsoft/layoutlm-base-uncased,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0091244801878929,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
7781,hakurei/gpt-j-random-tinier,This model has been initialized with random values,hakurei/gpt-j-random-tinier,1.0,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
7782,google/t5-efficient-xl-nl6,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0155161470174789,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
7783,mimi/Waynehills-NLP-doogie,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0950965136289596,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
7784,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0919421017169952,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
7785,IDEA-CCNL/Erlangshen-Longformer-330M,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
7786,google/t5-efficient-large-kv32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
7787,google/t5-efficient-small-nl48,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0747024789452552,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
7788,minimaxir/magic-the-gathering,"We used  Oscar dataset, which is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus",flax-community/gpt2-medium-persian,0.047220505774021,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
7789,google/t5-efficient-xl-nl8,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0125801507383585,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
7790,fnlp/bart-base-chinese,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0621454007923602,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
7791,saibo/random-roberta-mini,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0160007253289222,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
7792,tau/tavbert-he,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
7793,google/bert_uncased_L-8_H-768_A-12,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
7794,obss/mt5-base-3task-highlight-tquad2,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0797862671315669,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
7795,google/t5-efficient-mini-nl12,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
7796,tau/tavbert-he,"You can see the detailed process of gathering the data, preprocess it and train the LSTM in their repo",mrm8488/chEMBL_smiles_v1,0.0353964231908321,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
7797,google/t5-efficient-base-nl4,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
7798,google/t5-efficient-large-nh32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0593123082071542,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
7799,nghuyong/ernie-tiny,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0571752438942591,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
7800,negfir/distilbert-base-uncased-finetuned-squad,I have made no attempts to censor,bigjoedata/rockbot,0.0255927704274654,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
7801,google/t5-efficient-large-nl32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0511898882687091,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
7802,tobiaslee/bert-2l-768h-uncased,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0957527048885822,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
7803,google/t5-efficient-base-dm2000,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0440597254782915,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
7804,google/t5-efficient-small-el64,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0426007751375436,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
7805,mgreenbe/bertlet-base-uncased-for-sequence-classification,It was introduced in this paper,readerbench/RoBERT-small,0.071912407875061,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
7806,everdoubling/byt5-Korean-small,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0671702109277248,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
7807,amazon/bort,XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective,xlnet-large-cased,0.0057369284331798,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
7808,IDEA-CCNL/Erlangshen-Longformer-110M,This model is a useful encapsulation of gene ontology functions,damlab/GO-language,0.0619041733443736,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
7809,stas/mt5-tiny-random,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0890663191676139,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
7810,google/t5-efficient-small-ff6000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0166240204125642,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
7811,NovelAI/genji-python-6B,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0441309927652279,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
7812,TJMUCH/transcriptome-iseeek,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0662241876125335,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
7813,google/t5-efficient-base-kv16,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0460037495940923,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
7814,mwesner/reformer-clm,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0496774986386299,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
7815,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0841844603419303,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
7816,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
7817,google/t5-efficient-tiny-ff9000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0678770840167999,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
7818,kyryl0s/gpt2-uk-xxs,And there's a lot of English in this dataset too,ErykWdowiak/GPTalian,0.0567265637218951,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
7819,google/t5-efficient-small-dm256,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-dm256,1.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
7820,kssteven/ibert-roberta-base,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
7821,hfl/rbt3,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0387086570262908,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
7822,google/t5-efficient-tiny-nl32,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0446470696479081,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
7823,DemangeJeremy/4-sentiments-with-flaubert,"This is one of the smaller pre-trained BERT variants, together with bert-mini bert-small and bert-medium",prajjwal1/bert-tiny,0.0490093044936656,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7824,google/t5-efficient-large-dm256,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0177633902058005,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
7825,rahulacj/bertweet-base-finetuned-sentiment-analysis,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0409403219819068,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
7826,bookbot/distil-wav2vec2-adult-child-cls-37m,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0062870147327582,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
7827,google/t5-efficient-large-nh2,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0304890386760234,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
7828,gilparmentier/pokemon_gptj_model,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0815825480967759,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
7829,google/t5-efficient-small-kv128,T5-Efficient-SMALL-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv128,1.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
7830,pszemraj/led-large-book-summary,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0451662023551761,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
7831,hf-internal-testing/tiny-random-rembert,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0288830958306789,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
7832,superspray/electra_large_discriminator_squad2_custom_dataset,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
7833,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0543968230485916,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
7834,DemangeJeremy/4-sentiments-with-flaubert,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0689290314912795,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7835,google/t5-efficient-small-nl22,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0711627844721078,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
7836,malay-huggingface/xlnet-base-bahasa-cased,I have made no attempts to censor,bigjoedata/rockbot,0.0582104350129763,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
7837,google/t5-efficient-large-el4,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0724674016237258,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
7838,microsoft/layoutlm-large-uncased,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0336305350065231,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
7839,Iskaj/hf-challenge-test,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0731056034564971,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
7840,google/t5-efficient-base-el8,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0241304580122232,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
7841,mrm8488/deberta-v3-small-finetuned-mrpc,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0845892429351806,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
7842,Milos/slovak-gpt-j-1.4B,ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-1.4B,1.0,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
7843,negfir/distilbert-base-uncased-finetuned-cola,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0213348753750324,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
7844,tau/tavbert-he,T5-Efficient-TINY-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff9000,0.0307190278545021,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
7845,asi/gpt-fr-cased-base,We train the model on a very large and heterogeneous French corpus,asi/gpt-fr-cased-base,1.0,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
7846,google/t5-efficient-large-nh24,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0358469514176249,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
7847,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0583214461803436,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
7848,google/t5-efficient-large-kv256,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0321795102208852,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
7849,hfl/rbtl3,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0228299004957079,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7850,google/t5-efficient-small-ff12000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
7851,TJMUCH/transcriptome-iseeek,"Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example",facebook/m2m100-12B-avg-10-ckpt,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
7852,cammy/led-large-16384-arxiv-100-MDS,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
7853,allenai/wmt19-de-en-6-6-big,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0132713904604316,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
7854,IDEA-CCNL/Zhouwenwang-Unified-110M,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0673562999814748,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
7855,abdusahmbzuai/aradia-ctc-distilhubert-ft,This model is a fine-tuned version of ntu-spml/distilhubert on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_SM - NA dataset,abdusahmbzuai/aradia-ctc-distilhubert-ft,1.0,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
7856,jpcorb20/pegasus-large-reddit_tifu-samsum-512,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0633010317881902,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
7857,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0040279552340507,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
7858,google/t5-efficient-large-nl12,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
7859,hfl/rbt3,Model is based on GPT-J and has over 1.4B trainable parameters,Milos/slovak-gpt-j-1.4B,0.0754808895289897,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
7860,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
7861,SupriyaArun/squeezebert-uncased-finetuned-squad,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0751810520887374,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7862,gayanin/bart-mlm-pubmed-medterm,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.097020149230957,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
7863,beston91/gpt2-xl_ft_logits_10k,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
7864,SetFit/deberta-v3-large__sst2__train-8-8,ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-1.4B,0.0962422639131546,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
7865,tucan9389/kcbert-base-finetuned-squad,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0704395771026611,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
7866,google/t5-efficient-large-nl12,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
7867,mimicheng/codeparrot-ds,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0865485370159149,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
7868,ian0delond/model-test,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
7869,google/t5-efficient-large-nl20,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0237314971163868,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
7870,microsoft/layoutlm-large-uncased,A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string,Parsa/BBB_prediction_classification_SMILES,0.0710782930254936,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
7871,zoha/wav2vec2-base-timit-demo-colab,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0763609781861305,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
7872,1one5ome/gpt2-chinese-gulong,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0756327211856842,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
7873,beston91/gpt2-xl_ft_logits_10k,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0938624069094657,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
7874,Davlan/m2m100_418M-eng-yor-mt,"Specifically, this model is a facebook/m2m100_418M model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k",Davlan/m2m100_418M-eng-yor-mt,1.0,-1.0,NLP,22200502-47bf-477d-b7e3-dec71a3aae90___m2m100_418M-eng-yor-mt,HF
7875,bigjoedata/rockbot,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
7876,DTAI-KULeuven/robbertje-1-gb-bort,RobBERTje is a collection of distilled models based on RobBERT,DTAI-KULeuven/robbertje-1-gb-bort,1.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
7877,google/t5-efficient-small-kv128,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0354589112102985,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
7878,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0949881598353385,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
7879,google/t5-efficient-xl-nl28,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0261281598359346,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
7880,GKLMIP/electra-myanmar-small-uncased,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0870103053748607,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
7881,IlyaGusev/rugpt3medium_sum_gazeta,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0601523220539092,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
7882,hakurei/gpt-j-random-tinier,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0660214014351367,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
7883,mgreenbe/bertlet-base-uncased-for-sequence-classification,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0874321386218071,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
7884,google/t5-efficient-tiny-ff12000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.072422444820404,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
7885,google/t5-efficient-tiny-ff6000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.027112946845591,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
7886,rmihaylov/bert-base-pos-theseus-bg,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0733992798874775,-1.0,NLP,428758d5-6177-42ba-a879-c30586fca3ab___bert-base-pos-theseus-bg,HF
7887,lannelin/bert-imdb-1hidden,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0438941301157076,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
7888,lassl/gpt2-ko-small,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0020259418524801,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
7889,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0613348111510275,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
7890,cosmoquester/bart-ko-base,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0305798798799514,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
7891,rdenadai/BR_BERTo,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.026693871555229,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
7892,suzuki/distilbert-base-uncased-finetuned-squad,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0776250585913658,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
7893,IDEA-CCNL/Erlangshen-Longformer-110M,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0578050278127193,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
7894,mrm8488/deberta-v3-small-finetuned-mrpc,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0168924257159233,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
7895,SetFit/deberta-v3-large__sst2__train-8-8,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0952923223376274,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
7896,hfl/chinese-electra-small-ex-generator,ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants,hfl/chinese-electra-small-ex-generator,1.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
7897,google/t5-efficient-small-nl48,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0630024354904889,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
7898,mrm8488/deberta-v3-small-finetuned-mrpc,This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE MRPC dataset,mrm8488/deberta-v3-small-finetuned-mrpc,1.0,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
7899,akdeniz27/deberta-v2-xlarge-cuad,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0977167263627052,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
7900,google/t5-efficient-large-kv128,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
7901,microsoft/layoutlm-base-cased,The model might be used through the astonishing 🤗 Transformers librairie,asi/gpt-fr-cased-base,0.0225430261343717,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
7902,google/bigbird-roberta-large,I have made no attempts to censor,bigjoedata/rockbot,0.0244710359722375,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
7903,google/t5-efficient-large-kv16,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0087874042801558,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
7904,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0208755740895867,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
7905,google/t5-efficient-small-el64,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0006404984742402,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
7906,mimi/Waynehills-NLP-doogie,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.04191804677248,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
7907,google/t5-efficient-tiny-nl6,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0527646150439976,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
7908,groar/gpt-neo-1.3B-finetuned-escape2,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0718799829483032,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
7909,SupriyaArun/squeezebert-uncased-finetuned-squad,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0058060111477971,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7910,superb/hubert-base-superb-er,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0190496197901666,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
7911,google/t5-efficient-base-nl2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0268768612295389,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
7912,google/t5-efficient-base-nl36,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0317220930010079,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
7913,google/t5-efficient-mini-nl8,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0725765600800514,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
7914,google/t5-efficient-small-kv32,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0566481165587901,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
7915,eli4s/Bert-L12-h384-A6,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0223879007001717,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
7916,google/t5-efficient-base-nl24,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0842655338346957,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
7917,nherve/flaubert-oral-ft,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0337062015508611,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
7918,google/t5-efficient-tiny-ff3000,Its original repository can be found here,tau/splinter-large-qass,0.0334302950650453,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
7919,google/t5-efficient-large-dm768,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
7920,bookbot/distil-wav2vec2-adult-child-cls-37m,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0396747508396704,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
7921,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
7922,SupriyaArun/squeezebert-uncased-finetuned-squad,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
7923,google/t5-efficient-tiny-nl6,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0634053628891706,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
7924,jonfd/convbert-small-igc-is,"The model uses a Unigram tokenizer with a vocabulary size of 96,000",jonfd/convbert-small-igc-is,1.0,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
7925,dbmdz/electra-small-turkish-cased-discriminator,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0321635925211011,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
7926,sentence-transformers/paraphrase-MiniLM-L3-v2,mdThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-tiny,0.0225622225552797,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
7927,ToToKr/kobigbird-bert-base-finetuned-klue,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0589666441082954,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
7928,google/bert_uncased_L-4_H-128_A-2,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-4_H-128_A-2,1.0,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
7929,akdeniz27/deberta-v2-xlarge-cuad,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0024263076484203,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
7930,google/t5-efficient-base-dm256,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0437046978622674,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
7931,google/t5-efficient-small-ff3000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-ff3000,1.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
7932,brad1141/Longformer-finetuned-norm,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0849712938070297,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
7933,google/t5-efficient-base-nl24,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0182647332549095,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
7934,prajjwal1/bert-tiny,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,55d35131-34b4-4b75-b6b3-69d02d502e5a___bert-tiny,HF
7935,stas/mt5-tiny-random,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0263189412653446,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
7936,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0919998437166214,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
7937,obss/mt5-base-3task-highlight-tquad2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0230893050320446,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
7938,eli4s/Bert-L12-h384-A6,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,1.0,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
7939,hfl/rbtl3,T5-Efficient-TINY-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff3000,0.0594905316829681,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7940,mrm8488/bert-small-finetuned-squadv2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0236112065613269,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
7941,stas/t5-very-small-random,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,0.0995460376143455,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
7942,hajime9652/xlnet-japanese,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.015798307955265,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
7943,allenai/wmt16-en-de-12-1,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0630596112459897,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
7944,negfir/distilbert-base-uncased-finetuned-squad,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,0.0942725762724876,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
7945,hfl/chinese-legal-electra-base-generator,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0476264047125975,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
7946,minimaxir/magic-the-gathering,"Thus, you need to use the sw_KE for language code when evaluating the model",Davlan/mbart50-large-eng-yor-mt,0.0554514862596988,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
7947,stas/pegasus-cnn_dailymail-tiny-random,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0098380083218217,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
7948,microsoft/layoutlm-base-uncased,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0859142318367958,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
7949,google/t5-efficient-small-el32,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0362842381000518,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
7950,mimi/Waynehills-NLP-doogie,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
7951,allenai/wmt16-en-de-dist-6-1,Final perplexity on test set is 13.6,sberbank-ai/rugpt3large_based_on_gpt2,0.0362865533679723,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
7952,akdeniz27/deberta-v2-xlarge-cuad,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0509575232863426,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
7953,flboehm/reddit-bert-text_5,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0350613445043563,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
7954,google/t5-efficient-small-ff12000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0324864303693174,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
7955,CuongLD/wav2vec2-large-xlsr-vietnamese,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0400746539235115,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
7956,google/t5-efficient-small-nl2,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.052057608962059,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
7957,MoritzLaurer/MiniLM-L6-mnli,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0239020586013793,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
7958,google/t5-efficient-small-el12,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0161622045561671,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
7959,tartuNLP/gpt-4-est-large,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0313759297132492,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
7960,dhpollack/distilbert-dummy-sentiment,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0460100639611482,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
7961,hfl/chinese-electra-180g-small-ex-discriminator,This does not make a very readable text,pere/DeUnCaser,0.0353077209244171,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
7962,google/t5-efficient-large-nh2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
7963,khizon/distilbert-unreliable-news-eng-4L,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
7964,google/t5-efficient-small-kv32,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0084588006138801,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
7965,cammy/led-large-16384-arxiv-100-MDS,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0845817625522613,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
7966,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0595872695557772,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
7967,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0454253746817509,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
7968,google/t5-efficient-base-dm1000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0530681163072585,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
7969,microsoft/layoutlm-large-uncased,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0546437948942184,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
7970,hfl/chinese-legal-electra-base-generator,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
7971,DemangeJeremy/4-sentiments-with-flaubert,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0889827832579612,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
7972,hfl/rbtl3,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0360971670597791,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7973,google/t5-efficient-large-nl20,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0723826177418231,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
7974,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0866514891386032,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
7975,mrm8488/deberta-v3-small-finetuned-mrpc,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0827861130237579,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
7976,aditeyabaral/sentencetransformer-distilbert-hinglish-big,ipynb and the weights were converted to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead,google/reformer-crime-and-punishment,0.0712910592555999,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
7977,macedonizer/al-roberta-base,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,1.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
7978,google/t5-efficient-base-ff12000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0331260561943053,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
7979,hfl/rbtl3,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0397389139980077,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
7980,malay-huggingface/bert-tiny-bahasa-cased,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0829896926879882,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
7981,groar/gpt-neo-1.3B-finetuned-escape2,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0842662900686264,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
7982,sberbank-ai/rugpt3medium_based_on_gpt2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
7983,TJMUCH/transcriptome-iseeek,This is a ported version of  S3PRL's Hubert for the SUPERB Speaker Identification task,superb/hubert-large-superb-sid,0.0476062223315238,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
7984,VietAI/gpt-neo-1.3B-vietnamese-news,The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0597552098333835,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
7985,google/t5-efficient-small-kv16,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
7986,aditeyabaral/sentencetransformer-bert-hinglish-small,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
7987,1one5ome/gpt2-chinese-gulong,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0345537140965461,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
7988,malay-huggingface/t5-small-bahasa-cased,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0602962784469127,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
7989,tau/tavbert-he,T5-Efficient-SMALL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl2,0.0251997159793972,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
7990,sentence-transformers/paraphrase-MiniLM-L3-v2,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.0871801674365997,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
7991,jonfd/convbert-small-igc-is,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0047876343131065,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
7992,microsoft/layoutlm-base-cased,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0409513339400291,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
7993,uer/t5-v1_1-base-chinese-cluecorpussmall,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0310287848114967,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
7994,google/t5-efficient-base-dm2000,Its original repository can be found here,tau/splinter-large-qass,0.0620530527085065,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
7995,liaad/srl-pt_xlmr-base,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
7996,allenai/wmt16-en-de-12-1,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
7997,IDEA-CCNL/Erlangshen-Longformer-330M,And there's a lot of English in this dataset too,ErykWdowiak/GPTalian,0.0522316135466098,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
7998,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0539807230234146,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
7999,skt/ko-gpt-trinity-1.2B-v0.5,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0655217170715331,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
8000,google/t5-efficient-mini-nl6,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0030945111066103,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
8001,aditeyabaral/sentencetransformer-bert-hinglish-small,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0625724196434021,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
8002,mwesner/reformer-clm,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,0.0589081533253192,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
8003,hfl/rbtl3,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,0.0503606861457228,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
8004,ian0delond/model-test,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
8005,google/t5-efficient-base-ff1000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
8006,google/t5-efficient-base-el16,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
8007,hfl/rbt3,T5-Efficient-BASE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh32,0.0564331486821174,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8008,camembert/camembert-base-oscar-4gb,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
8009,google/t5-efficient-large-nl12,This does not make a very readable text,pere/DeUnCaser,0.0598681569099425,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
8010,IDEA-CCNL/Erlangshen-Longformer-330M,"This is part of a project from which resulted the following models:For more information, please see the accompanying article (See BibTeX entry and citation info below) and the project's github",liaad/srl-pt_xlmr-base,0.0697487294673919,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
8011,lgrobol/electra-minuscule-discriminator,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
8012,google/t5-efficient-large-nl32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0416041938588023,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
8013,Milos/slovak-gpt-j-1.4B,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0193762048147618,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
8014,google/t5-efficient-tiny-nl12,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0215444453060627,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
8015,anirudh21/xlnet-base-cased-finetuned-rte,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0889046490192413,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
8016,minimaxir/magic-the-gathering,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.0293185785412788,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8017,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0718934163451194,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
8018,google/t5-efficient-small-ff1000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.052196392789483,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
8019,saibo/random-roberta-tiny,The weight of random-roberta-tiny is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining,saibo/random-roberta-tiny,1.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
8020,google/t5-efficient-xl-nl16,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.065633088350296,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
8021,superspray/electra_large_discriminator_squad2_custom_dataset,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
8022,tau/tavbert-he,T5-Efficient-LARGE-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl12,0.0465903226286172,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
8023,mimicheng/codeparrot-ds,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0794579684734344,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
8024,thu-coai/LongLM-large,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0023996125285823,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
8025,google/bert_uncased_L-10_H-128_A-2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0481574097648262,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
8026,prajjwal1/bert-tiny,The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository,prajjwal1/bert-tiny,1.0,-1.0,NLP,55d35131-34b4-4b75-b6b3-69d02d502e5a___bert-tiny,HF
8027,google/t5-efficient-tiny-nh16,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0152726978994905,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
8028,google/t5-efficient-base-kv256,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0418729800730943,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
8029,google/t5-efficient-large-nl10,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
8030,google/t5-efficient-small-el8,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.002512190490961,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
8031,akdeniz27/deberta-v2-xlarge-cuad,"You can fine-tune roberta-base-ukrainian for downstream tasks, such as POS-tagging, dependency-parsing, and so on",KoichiYasuoka/roberta-base-ukrainian,0.0957096293568611,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
8032,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl24,0.0132953934371471,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
8033,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0458830744028091,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
8034,google/t5-efficient-small-el64,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
8035,google/t5-efficient-tiny-nl16,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0749747902154921,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
8036,google/bert_uncased_L-8_H-256_A-4,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0032457672059535,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
8037,google/t5-efficient-large-nh12,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0365509707480668,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
8038,hfl/chinese-xlnet-mid,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0068895680209001,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
8039,google/bert_uncased_L-10_H-256_A-4,The smaller BERT models are intended for environments with restricted computational resources,google/bert_uncased_L-10_H-256_A-4,1.0,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
8040,kyryl0s/gpt2-uk-xxs,LayoutLM archives the SOTA results on multiple datasets,microsoft/layoutlm-large-uncased,0.0350219719111919,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
8041,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
8042,beston91/gpt2-xl_ft_logits_10k,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0020541101694107,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
8043,abdusahmbzuai/aradia-ctc-distilhubert-ft,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0991750955581665,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
8044,hfl/chinese-legal-electra-base-generator,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0512275919318198,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
8045,Milos/slovak-gpt-j-405M,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
8046,hfl/chinese-electra-180g-small-ex-generator,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0009091390917698,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
8047,google/t5-efficient-base-ff12000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0601740684360265,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
8048,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0553084574639797,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
8049,ian0delond/model-test,Source language: English,NYTK/translation-marianmt-en-hu,0.0796151459217071,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
8050,akdeniz27/deberta-v2-xlarge-cuad,For best results use python as below,pszemraj/led-large-book-summary,0.0203629750758409,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
8051,saibo/random-roberta-mini,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,1.0,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
8052,mimicheng/codeparrot-ds,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0184103082865476,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
8053,beston91/gpt2-xl_ft_logits_10k,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0703438073396682,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
8054,google/t5-efficient-base-ff6000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
8055,tartuNLP/gpt-4-est-large,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-8_H-768_A-12,0.086257129907608,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
8056,stas/pegasus-cnn_dailymail-tiny-random,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,0.0407405807636677,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
8057,thu-coai/LongLM-large,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
8058,liaad/srl-pt_bertimbau-large,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0577076816310485,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
8059,pulp/CHILDES-ParentBERTo,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
8060,google/t5-efficient-large-dm128,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
8061,allenai/wmt19-de-en-6-6-base,"For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation",allenai/wmt19-de-en-6-6-base,1.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
8062,anirudh21/xlnet-base-cased-finetuned-rte,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0506259649991989,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
8063,IDEA-CCNL/Erlangshen-Longformer-110M,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
8064,everdoubling/byt5-Korean-small,The input to the model is a Trivia type question,deep-learning-analytics/triviaqa-t5-base,0.0509653110057114,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
8065,amazon/bort,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0377445658668876,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8066,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0982249230146408,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
8067,google/t5-efficient-large-nh4,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
8068,monsoon-nlp/dv-muril,This BERT-based model currently performs better than dv-wave ELECTRA on the Maldivian News Classification task https://github,monsoon-nlp/dv-muril,1.0,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
8069,jhu-clsp/bibert-ende,"For example, on SQuAD, the model is able to reach 80% F1 given only 128 examples, whereas the base model obtains only ~73%)",tau/splinter-large-qass,0.0426173036297162,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
8070,google/t5-efficient-small-dm2000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
8071,ToToKr/kobigbird-bert-base-finetuned-klue,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
8072,amazon/bort,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0647904053330421,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8073,zoha/wav2vec2-base-timit-demo-colab,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0193074028939008,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
8074,minimaxir/magic-the-gathering,"This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:The model was fine-tuned on 2,000 manually annotated social media posts",j-hartmann/purchase-intention-english-roberta-large,0.0485806670039892,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8075,google/t5-efficient-large-nl20,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0707769468426704,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
8076,SetFit/deberta-v3-large__sst2__train-8-8,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
8077,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,This model was trained using HuggingFace's PyTorch framework,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,1.0,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
8078,Milos/slovak-gpt-j-405M,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0367790938665469,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
8079,google/bert_uncased_L-8_H-256_A-4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.012903448815147,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
8080,ian0delond/model-test,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0997944846749305,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
8081,wukevin/tcr-bert,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
8082,google/t5-efficient-large-kv32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0116762574762105,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
8083,facebook/mbart-large-50-many-to-one-mmt,The model can translate directly between any pair of 50 languages,facebook/mbart-large-50-many-to-one-mmt,1.0,-1.0,NLP,1aed0c2f-c6c7-49df-87c0-c3bebcdb63af___mbart-large-50-many-to-one-mmt,HF
8084,VietAI/gpt-neo-1.3B-vietnamese-news,Details will be available soon,VietAI/gpt-neo-1.3B-vietnamese-news,1.0,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
8085,SupriyaArun/squeezebert-uncased-finetuned-squad,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.0835026055574417,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
8086,skt/ko-gpt-trinity-1.2B-v0.5,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0508321709930896,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
8087,google/t5-efficient-base-nh8,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0067753819748759,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
8088,TJMUCH/transcriptome-iseeek,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0614178553223609,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
8089,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
8090,google/t5-efficient-large-kv256,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0509934909641742,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
8091,google/t5-efficient-small-el4,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0047560911625624,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
8092,google/t5-efficient-large-nl36,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0752090662717819,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
8093,google/t5-efficient-tiny-nl2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0249262396246194,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
8094,google/t5-efficient-small-dm768,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0146107990294694,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
8095,google/t5-efficient-small-kv256,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0127593949437141,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
8096,google/t5-efficient-small-ff12000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
8097,uer/pegasus-base-chinese-cluecorpussmall,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0340791726484894,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
8098,microsoft/layoutlm-base-uncased,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8099,PlanTL-GOB-ES/gpt2-base-bne,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0161454911447233,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
8100,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.077689342200756,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
8101,google/t5-efficient-base-nh16,Its original repository can be found here,tau/splinter-large-qass,0.0670209508389234,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
8102,google/t5-efficient-large-nh4,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
8103,google/t5-efficient-tiny-ff6000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0450346246361732,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
8104,VietAI/gpt-neo-1.3B-vietnamese-news,"BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences",google/bigbird-roberta-large,0.0792263783514499,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
8105,google/t5-efficient-large-nh4,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0845411531627177,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
8106,google/t5-efficient-xl-nl4,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0624515935778617,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
8107,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0567428618669509,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
8108,google/t5-efficient-small-dm2000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0119713246822357,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
8109,google/reformer-crime-and-punishment,This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU,kssteven/ibert-roberta-large,0.057697853539139,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
8110,DemangeJeremy/4-sentiments-with-flaubert,"This is one of the smaller pre-trained BERT variants, together with bert-small and bert-medium",prajjwal1/bert-mini,0.0625125542283058,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
8111,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0241021066904068,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
8112,microsoft/layoutlm-base-uncased,"This is one of the smaller pre-trained BERT variants, together with bert-small and bert-medium",prajjwal1/bert-mini,0.0148259978741407,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8113,rdenadai/BR_BERTo,T5-Efficient-LARGE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv32,0.0278584212064742,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
8114,bolbolzaban/gpt2-persian,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0429989844560623,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
8115,thu-coai/LongLM-large,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,0.0352207620938618,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
8116,bigjoedata/rockbot,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,1.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
8117,google/t5-efficient-small-dm2000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
8118,microsoft/layoutlm-base-uncased,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8119,microsoft/layoutlm-base-cased,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
8120,google/t5-efficient-large-el6,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0256892591714858,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
8121,google/t5-efficient-large-nl20,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
8122,allenai/macaw-3b,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0086796147127945,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
8123,khizon/distilbert-unreliable-news-eng-4L,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
8124,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0291535500437021,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
8125,sonoisa/byt5-small-japanese,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0195773150771856,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
8126,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0499662558237711,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
8127,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
8128,tartuNLP/gpt-4-est-large,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0763960257172584,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
8129,Palak/microsoft_deberta-base_squad,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0292319729924201,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
8130,Palak/microsoft_deberta-base_squad,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0786039233207702,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
8131,SupriyaArun/squeezebert-uncased-finetuned-squad,You can use the raw model for text generation to generate reasons why natural language statements are against commonsense,aliosm/ComVE-gpt2-large,0.079302854835987,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
8132,TJMUCH/transcriptome-iseeek,It is a pretrained model on English language using a masked language modeling (MLM) objective,google/bigbird-roberta-large,0.0978644490242004,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
8133,hfl/rbt3,T5-Efficient-BASE-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl4,0.0348509289324283,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8134,google/t5-efficient-mini-nl12,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
8135,hfl/rbt3,T5-Efficient-LARGE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh24,0.0688430219888687,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8136,google/t5-efficient-tiny-nl16,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0272794757038354,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
8137,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0587709173560142,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
8138,google/t5-efficient-base-nl48,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0698446892201899,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
8139,nguyenvulebinh/envibert,I have made no attempts to censor,bigjoedata/rockbot,0.0239010409762461,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
8140,kyryl0s/gpt2-uk-xxs,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
8141,SupriyaArun/squeezebert-uncased-finetuned-squad,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0930060371756553,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
8142,groar/gpt-neo-1.3B-finetuned-escape2,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0517531968653202,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
8143,IDEA-CCNL/Erlangshen-Longformer-110M,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0296947634778916,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
8144,M-FAC/bert-mini-finetuned-stsb,I have made no attempts to censor,bigjoedata/rockbot,0.0355974519625306,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
8145,gchhablani/fnet-base-finetuned-wnli,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0755568332970142,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
8146,google/t5-efficient-base-dm512,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,1.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
8147,IlyaGusev/rugpt3medium_sum_gazeta,Splinter-large is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021),tau/splinter-large-qass,0.0770068578422069,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
8148,microsoft/layoutlm-base-uncased,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0859998613595962,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8149,cammy/led-base-16384-100-MDS,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0733902975916862,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
8150,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0490276962518692,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
8151,IDEA-CCNL/Erlangshen-Longformer-330M,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0051968544721603,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
8152,google/t5-efficient-large-nl12,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0616305628791451,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
8153,hfl/chinese-electra-small-ex-generator,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
8154,microsoft/layoutlm-base-uncased,The model was created for selective question answering in Polish,enelpol/czywiesz-context,0.0969980135560035,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8155,google/t5-efficient-base-dm2000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0808609314262867,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
8156,negfir/distilbert-base-uncased-finetuned-cola,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0820023640990257,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
8157,cosmoquester/bart-ko-mini,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0067170386513074,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
8158,google/t5-efficient-small-ff9000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
8159,lgrobol/electra-minuscule-generator,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0437213797122239,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
8160,gayanin/bart-mlm-pubmed-medterm,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0997432172298431,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
8161,nherve/flaubert-oral-ft,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.050002135336399,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
8162,google/t5-efficient-mini-nl12,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0382401701062917,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
8163,mrm8488/xlm-multi-finetuned-xquadv1,"Since it has 4 attention heads, the head size is 64 just as for the BERT base model",eli4s/Bert-L12-h256-A4,0.0904497653245925,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
8164,hfl/rbtl3,T5-Efficient-XL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl2,0.0578570570796727,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
8165,allenai/wmt19-de-en-6-6-big,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0396700836718082,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
8166,tartuNLP/gpt-4-est-large,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0589464493095874,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
8167,amazon/bort,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0897200107574462,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8168,google/t5-efficient-large-el8,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0378467962145805,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
8169,google/t5-efficient-base-el16,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0011888612061739,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
8170,akdeniz27/deberta-v2-xlarge-cuad,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0081553924828767,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
8171,flboehm/reddit-bert-text_5,I have made no attempts to censor,bigjoedata/rockbot,0.034828882664442,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
8172,ctoraman/RoBERTa-TR-medium-char,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0382005065679549,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
8173,sentence-transformers/paraphrase-MiniLM-L3-v2,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0786046758294105,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
8174,hfl/rbt3,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0594444042071699,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8175,google/t5-efficient-small-dm1000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0220110323280095,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
8176,aditeyabaral/sentencetransformer-distilbert-hinglish-big,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.069600760936737,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
8177,google/t5-efficient-tiny-nh32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0622279327362775,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
8178,google/t5-efficient-small-ff1000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0142081482335925,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
8179,google/t5-efficient-tiny-nh32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
8180,google/t5-efficient-mini-nl8,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0213731592521071,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
8181,TransQuest/monotransquest-hter-en_lv-it-nmt,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0214324438711628,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
8182,Iskaj/hf-challenge-test,RobBERTje is a collection of distilled models based on RobBERT,DTAI-KULeuven/robbertje-1-gb-bort,0.0529709607362747,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
8183,mwesner/bart-mlm,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0166175998747348,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
8184,google/t5-efficient-large-dm512,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0281412377953529,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
8185,mrp/simcse-model-wangchanberta,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0692590568214654,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
8186,KoichiYasuoka/roberta-base-ukrainian,Disclaimer: This model card has been written by gchhablani,google/fnet-large,0.0777369141578674,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
8187,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl36,0.083347912877798,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
8188,dhpollack/distilbert-dummy-sentiment,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
8189,tau/tavbert-he,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0391387059353291,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
8190,google/t5-efficient-large-nl32,This does not make a very readable text,pere/DeUnCaser,0.0748342648148536,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
8191,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0839426517486572,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
8192,Milos/slovak-gpt-j-1.4B,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
8193,IDEA-CCNL/Erlangshen-Longformer-330M,"Finetuning of I-BERT consists of 3 stages: (1) Full-precision finetuning from the pretrained model on a down-stream task, (2) model quantization, and (3) integer-only finetuning (i",kssteven/ibert-roberta-large,0.0579616539180278,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
8194,jnz/electra-ka,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0442354455590248,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
8195,akdeniz27/deberta-v2-xlarge-cuad,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0708610862493515,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
8196,google/t5-efficient-large-nl32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0511220553889869,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
8197,anirudh21/xlnet-base-cased-finetuned-rte,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0484008230268955,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
8198,Recognai/zeroshot_selectra_small,"As shown in the Metrics section, the small version (5 times fewer parameters) performs slightly worse, while the medium version (3 times fewer parameters) outperforms the BETO based zero-shot classifier",Recognai/zeroshot_selectra_small,1.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
8199,google/t5-efficient-base-el8,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.033553410321474,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
8200,tau/tavbert-he,T5-Efficient-BASE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el6,0.0259197000414132,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
8201,stas/pegasus-cnn_dailymail-tiny-random,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
8202,danyaljj/opengpt2_pytorch_forward,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0399718874444563,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
8203,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Its original repository can be found here,tau/splinter-large-qass,0.0821781903505325,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
8204,readerbench/RoBERT-small,Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective,readerbench/RoBERT-small,1.0,-1.0,NLP,af9865c1-d52f-4c4d-8696-c19b9085823b___RoBERT-small,HF
8205,google/t5-efficient-xl-nl28,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.039536902680993,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
8206,IlyaGusev/rugpt3medium_sum_gazeta,This is the model for abstractive summarization for Russian based on rugpt3medium_based_on_gpt2,IlyaGusev/rugpt3medium_sum_gazeta,1.0,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
8207,aubmindlab/aragpt2-mega,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0451386962085962,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
8208,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0818328261375427,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
8209,TransQuest/monotransquest-hter-en_lv-it-nmt,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
8210,abdusahmbzuai/ft-tatoeba-ar-en,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0239414013922214,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
8211,abdusahmbzuai/aradia-ctc-distilhubert-ft,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0205933898687362,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
8212,google/t5-efficient-small-kv32,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0833376646041869,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
8213,google/t5-efficient-mini,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.016683593392372,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
8214,minimaxir/magic-the-gathering,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0169825982302427,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8215,google/t5-efficient-tiny-nh8,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0537213273346423,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
8216,piotr-rybak/poleval2021-task4-herbert-large-encoder,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0279366709291934,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
8217,saibo/random-roberta-mini,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0411471431143581,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
8218,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0785776004195213,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
8219,google/t5-efficient-xl-nl28,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0853518024086952,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
8220,google/t5-efficient-tiny-nh16,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0952365770936012,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
8221,aditeyabaral/sentencetransformer-bert-hinglish-small,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.056073747575283,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
8222,google/t5-efficient-base-kv128,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0045763030648231,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
8223,everdoubling/byt5-Korean-small,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,0.0059750765562057,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
8224,google/t5-efficient-base-ff9000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0247073732316494,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
8225,google/t5-efficient-xl-nl6,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
8226,sonoisa/byt5-small-japanese,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
8227,google/t5-efficient-small-ff3000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0625335052609443,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
8228,KoichiYasuoka/roberta-base-ukrainian,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
8229,krevas/finance-koelectra-base-discriminator,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0574783543124794,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
8230,stas/mt5-tiny-random,The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0587143227458,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
8231,google/t5-efficient-large-nl36,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0445081368088721,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
8232,ian0delond/model-test,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0933818519115448,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
8233,google/t5-efficient-base-nl4,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0128546003252268,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
8234,google/t5-efficient-xxl-nl4,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.03963715583086,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
8235,google/t5-efficient-small-nl40,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0345230549573898,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
8236,allenai/macaw-answer-11b,"In the v2 version, the Portuguese dataset was translated using Google Translate",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0164653124908606,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
8237,google/t5-efficient-base-nh32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0465162806212901,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
8238,google/t5-efficient-base-nl4,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0488563682883977,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
8239,google/t5-efficient-large-nh24,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0324575621634721,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
8240,hfl/chinese-legal-electra-base-generator,I have made no attempts to censor,bigjoedata/rockbot,0.0041386261582374,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
8241,google/t5-efficient-large-el6,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0260525979101657,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
8242,skt/ko-gpt-trinity-1.2B-v0.5,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
8243,thu-coai/LongLM-large,Encoder-decoder models are trained typically by maximizing the likelihood of the target output given an input,thu-coai/LongLM-large,1.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
8244,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0782040506601333,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
8245,microsoft/layoutlm-base-uncased,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0411167070269584,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8246,google/t5-efficient-small-nl48,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
8247,google/t5-efficient-large-nh12,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nh12,1.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
8248,akdeniz27/deberta-v2-xlarge-cuad,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.050108678638935,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
8249,google/t5-efficient-base-nh32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0518875652924179,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
8250,akdeniz27/deberta-v2-xlarge-cuad,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
8251,google/t5-efficient-base-ff1000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0452337414026259,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
8252,google/t5-efficient-tiny-nl2,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0084183327853679,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
8253,abdusahmbzuai/ft-tatoeba-ar-en,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0906628370285034,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
8254,mimicheng/codeparrot-ds,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
8255,lgrobol/electra-minuscule-generator,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0740075260400771,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
8256,saibo/random-roberta-tiny,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,1.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
8257,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,I have made no attempts to censor,bigjoedata/rockbot,0.0195430889725684,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
8258,malay-huggingface/t5-super-tiny-bahasa-cased,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
8259,tartuNLP/gpt-4-est-large,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,0.0731971114873886,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
8260,google/t5-efficient-base-dm1000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0508190412074327,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
8261,sonoisa/byt5-small-japanese,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0859359204769134,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
8262,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0452705286443233,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
8263,hf-internal-testing/tiny-random-rembert,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0118528706952929,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
8264,minimaxir/magic-the-gathering,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0058675166219472,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8265,hfl/rbtl3,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0604064334183931,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
8266,Milos/slovak-gpt-j-405M,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0628543570637702,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
8267,microsoft/layoutlm-base-cased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0426016375422477,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
8268,google/t5-efficient-tiny-ff9000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0651524662971496,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
8269,google/t5-efficient-base-kv16,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
8270,google/t5-efficient-base-dm256,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0029994286596774,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
8271,google/t5-efficient-large-nh2,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0291659040376544,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
8272,nielsr/coref-roberta-base,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.031658315565437,-1.0,NLP,8473d81a-bfd8-4703-b569-a14970be6a42___coref-roberta-base,HF
8273,hfl/rbt3,This model is a fine-tuned version of allenai/led-base-16384 on an unknown dataset,cammy/led-base-16384-100-MDS,0.0690115876495837,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8274,gayanin/bart-mlm-pubmed-medterm,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0465976782143116,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
8275,google/t5-efficient-base-el8,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0277609517797828,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
8276,google/t5-efficient-base-dm256,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0498815430328249,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
8277,TJMUCH/transcriptome-iseeek,"CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationYunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu",fnlp/cpt-base,0.0885196700692176,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
8278,google/t5-efficient-large-nh8-nl32,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0838352069258689,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
8279,mrm8488/mobilebert-uncased-finetuned-squadv2,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
8280,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0898642688989639,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
8281,aubmindlab/aragpt2-large,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0010253386572003,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
8282,KoichiYasuoka/roberta-base-ukrainian,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0973818823695182,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
8283,SupriyaArun/squeezebert-uncased-finetuned-squad,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
8284,google/t5-efficient-small-kv32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0013352371752262,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
8285,google/t5-efficient-tiny-ff9000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
8286,google/t5-efficient-base-nl36,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0406575864180921,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
8287,jorge-henao/gpt2-small-spanish-disco-poetry-wt,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0993948429822921,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
8288,celine98/canine-s-finetuned-sst2,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.059223785996437,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
8289,google/t5-efficient-base-nl24,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.011349725537002,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
8290,1one5ome/gpt2-chinese-gulong,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0817576348781585,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
8291,IDEA-CCNL/Erlangshen-Longformer-110M,The article and  summarization text is reduced via extractive summarization before it is finetuned to the model,ml6team/distilbart-tos-summarizer-tosdr,0.0307274907827376,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
8292,google/t5-efficient-small-kv32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0135577698238194,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
8293,IDEA-CCNL/Erlangshen-Longformer-110M,This model is a fine-tuned version of google/bigbird-pegasus-large-bigpatent on the pub_med_summarization_dataset dataset,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,0.0724187344312667,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
8294,google/t5-efficient-base-nh32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0008132029324769,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
8295,jweb/japanese-soseki-gpt2-1b,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.018618170171976,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
8296,superspray/electra_large_discriminator_squad2_custom_dataset,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0709789916872978,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
8297,DemangeJeremy/4-sentiments-with-flaubert,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
8298,microsoft/deberta-v2-xxlarge-mnli,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0607442930340766,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
8299,mwesner/reformer-clm,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0598677322268486,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
8300,lgrobol/electra-minuscule-discriminator,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
8301,dbmdz/electra-small-turkish-cased-discriminator,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0068610711023211,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
8302,google/t5-efficient-mini,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0256287585943937,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
8303,IDEA-CCNL/Erlangshen-Longformer-110M,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0249949740245937,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
8304,TJMUCH/transcriptome-iseeek,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0985640883445739,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
8305,google/t5-efficient-tiny-nl32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0078464094549417,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
8306,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0633124485611915,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
8307,facebook/m2m100-12B-avg-10-ckpt,M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation,facebook/m2m100-12B-avg-10-ckpt,1.0,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
8308,google/t5-efficient-small-dm2000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0426383689045906,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
8309,google/t5-efficient-base-dm512,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0366582944989204,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
8310,fnlp/cpt-base,"CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationYunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu",fnlp/cpt-base,1.0,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
8311,aubmindlab/aragpt2-large,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0217175059951841,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
8312,uer/t5-v1_1-small-chinese-cluecorpussmall,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0473871221765875,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
8313,google/t5-efficient-small-ff6000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0375731764361261,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
8314,superspray/electra_large_discriminator_squad2_custom_dataset,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0777379870414733,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
8315,mwesner/reformer-clm,T5-Efficient-BASE-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm1000,0.0715169161558151,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
8316,google/t5-efficient-base-dm256,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0934089124202728,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
8317,anirudh21/xlnet-base-cased-finetuned-rte,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.035227995365858,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
8318,bookbot/distil-wav2vec2-adult-child-cls-37m,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0415579071268439,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
8319,amazon/bort,Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective,google/fnet-large,0.0523434914648532,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8320,crabz/distil-slovakbert-ner,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0295194834470748,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
8321,google/t5-efficient-mini,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0766459256410597,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
8322,google/t5-efficient-mini,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0074381893500685,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
8323,google/t5-efficient-base-el16,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
8324,cosmoquester/bart-ko-mini,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0148665172358353,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
8325,fnlp/bart-large-chinese,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.035193022340536,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
8326,hfl/chinese-electra-180g-small-ex-discriminator,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0512275919318198,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
8327,mwesner/reformer-clm,T5-Efficient-BASE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl32,0.0904082506895065,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
8328,google/t5-efficient-base-nh16,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0130591420456767,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
8329,google/t5-efficient-xl-nl12,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0294273793697356,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
8330,ml6team/mt5-small-german-finetune-mlsum,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0439984183758496,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
8331,google/t5-efficient-tiny-ff9000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.016824236139655,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
8332,thu-coai/LongLM-large,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
8333,aditeyabaral/sentencetransformer-bert-hinglish-small,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
8334,google/t5-efficient-large-nh8,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
8335,google/t5-efficient-large-el2,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
8336,SetFit/deberta-v3-large__sst2__train-8-8,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0057822111994028,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
8337,google/t5-efficient-small-nl40,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0721716918051242,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
8338,mrm8488/mobilebert-uncased-finetuned-squadv2,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0829159592588742,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
8339,tartuNLP/gpt-4-est-base,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0208938317373394,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
8340,google/t5-efficient-base-dm2000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0468816328793763,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
8341,diper1998/distilgpt2-finetuned-AT,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0741793811321258,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
8342,google/t5-efficient-base-dm512,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
8343,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.029481777921319,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
8344,google/t5-efficient-base-nl48,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0526080578565597,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
8345,google/t5-efficient-small-nl40,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
8346,google/t5-efficient-mini-nl8,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.051445173099637,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
8347,google/t5-efficient-base-kv16,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0532000232487916,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
8348,google/t5-efficient-large-nh8,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.063401099294424,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
8349,google/t5-efficient-base-nh32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0139529854059219,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
8350,ductuan024/AimeLaw,This does not make a very readable text,pere/DeUnCaser,0.0534829609096049,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
8351,google/t5-efficient-xl-nl2,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0752119831740855,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
8352,allegro/plt5-large,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0431132353842258,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
8353,superspray/electra_large_discriminator_squad2_custom_dataset,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0843297466635704,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
8354,Milos/slovak-gpt-j-405M,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,1.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
8355,google/t5-efficient-base-kv256,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0808461122214794,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
8356,google/t5-efficient-small-ff9000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0048747714608907,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
8357,skt/ko-gpt-trinity-1.2B-v0.5,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0172240622341632,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
8358,hyunwoongko/reddit-9B,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
8359,google/t5-efficient-base-nh16,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0699399784207344,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
8360,funnel-transformer/large-base,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
8361,google/t5-efficient-large-nl36,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0272270739078521,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
8362,negfir/distilbert-base-uncased-finetuned-squad,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.001773253083229,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
8363,KoichiYasuoka/roberta-base-ukrainian,"You can fine-tune roberta-base-ukrainian for downstream tasks, such as POS-tagging, dependency-parsing, and so on",KoichiYasuoka/roberta-base-ukrainian,1.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
8364,anirudh21/xlnet-base-cased-finetuned-rte,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0932820737361908,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
8365,google/t5-efficient-large-kv32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
8366,google/t5-efficient-large-dm512,This does not make a very readable text,pere/DeUnCaser,0.0406295992434024,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
8367,jweb/japanese-soseki-gpt2-1b,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0514426504572231,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
8368,ml6team/distilbart-tos-summarizer-tosdr,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
8369,google/t5-efficient-base-nh24,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0762558244168758,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
8370,Recognai/zeroshot_selectra_small,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,1.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
8371,beston91/gpt2-xl_ft_logits_10k,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0184454247355461,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
8372,obss/mt5-base-3task-highlight-tquad2,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0864308886229991,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
8373,google/t5-efficient-mini,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
8374,aditeyabaral/sentencetransformer-bert-hinglish-small,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
8375,google/t5-efficient-tiny-ff9000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0765976235270499,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
8376,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl12,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
8377,tobiaslee/bert-2l-768h-uncased,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0494908373802899,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
8378,google/t5-efficient-large-dm256,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
8379,zoha/wav2vec2-base-timit-demo-colab,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0916640758514404,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
8380,google/t5-efficient-xl-nl4,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
8381,castorini/afriberta_base,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0701825376600026,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
8382,superspray/electra_large_discriminator_squad2_custom_dataset,This does not make a very readable text,pere/DeUnCaser,0.0291097331792116,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
8383,google/t5-efficient-base-ff9000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0473006814718246,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
8384,minimaxir/magic-the-gathering,I have made no attempts to censor,bigjoedata/rockbot,0.0508583057671785,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8385,google/t5-efficient-small-dm2000,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0686905421316623,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
8386,google/t5-efficient-base-nl40,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0659121274948119,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
8387,uer/t5-v1_1-small-chinese-cluecorpussmall,"This is the set of Chinese T5 Version 1.1 models pre-trained by UER-py, which is introduced in this paper",uer/t5-v1_1-small-chinese-cluecorpussmall,1.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
8388,jnz/electra-ka,The model is trained on 33GB of Georgian text collected from 4854621 pages in commoncrowl archive,jnz/electra-ka,1.0,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
8389,jnz/electra-ka,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0789729133248329,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
8390,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,Further information about the dataset or the translation method can be found on our mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset and mMARCO repository,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,1.0,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
8391,google/t5-efficient-base-kv16,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.041405439376831,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
8392,google/t5-efficient-large-nh32,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0561115536838769,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
8393,google/t5-efficient-tiny-nh32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0337352845817804,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
8394,facebook/blenderbot-90M,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0448923074873163,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
8395,tau/tavbert-he,T5-Efficient-BASE-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl48,0.0095663270913064,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
8396,ian0delond/model-test,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.034972533583641,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
8397,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-MINI-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl8,0.0341509636491536,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
8398,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0999445989727974,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
8399,Fujitsu/pytorrent,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
8400,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
8401,google/t5-efficient-large-el2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0163427824154496,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
8402,google/t5-efficient-small-kv256,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0142249800264835,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
8403,anas-awadalla/bert-tiny-finetuned-squad,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0984397605061531,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
8404,hyunwoongko/reddit-9B,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0088546962610312,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
8405,google/t5-efficient-small-el64,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
8406,tartuNLP/gpt-4-est-large,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
8407,malay-huggingface/xlnet-large-bahasa-cased,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
8408,DemangeJeremy/4-sentiments-with-flaubert,You can use this model directly with a pipeline for text2text generation :CLUECorpusSmall Common Crawl and some short messages are used as training data,uer/bart-chinese-4-768-cluecorpussmall,0.0714767202734947,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
8409,google/t5-efficient-large-dm128,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
8410,stas/mt5-tiny-random,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0747042298316955,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
8411,IDEA-CCNL/Zhouwenwang-Unified-1.3B,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0100740980356931,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
8412,microsoft/layoutlm-base-cased,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0754349306225776,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
8413,sentence-transformers/paraphrase-MiniLM-L3-v2,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0647974163293838,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
8414,stas/t5-very-small-random,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0029527591541409,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
8415,negfir/distilbert-base-uncased-finetuned-squad,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0724083632230758,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
8416,google/t5-efficient-large-nl16,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0654475577175616,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
8417,google/t5-efficient-large-dm512,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
8418,google/t5-efficient-base-el6,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
8419,google/t5-efficient-large-nl10,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0046780109405517,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
8420,microsoft/layoutlm-base-uncased,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0519517138600349,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8421,google/t5-efficient-xl-nl2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
8422,tartuNLP/gpt-4-est-large,"Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words",macedonizer/al-roberta-base,0.0895361751317977,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
8423,amazon/bort,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0824852623045444,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8424,uer/t5-v1_1-base-chinese-cluecorpussmall,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0175708401948214,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
8425,google/t5-efficient-tiny-nh16,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0509196538478135,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
8426,google/byt5-large,Amazon's BORT,amazon/bort,0.0449937535449862,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
8427,google/rembert,This makes the model more efficient since the output embeddings are discarded during fine-tuning,google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
8428,ian0delond/model-test,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0872936844825744,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
8429,google/t5-efficient-large-nl20,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0675029903650283,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
8430,google/t5-efficient-base-nl32,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
8431,malay-huggingface/bert-tiny-bahasa-cased,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
8432,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.066326830536127,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
8433,stas/mt5-tiny-random,"We exploit Unihan, a ready-made database constructed by linguistic experts to first merge morphologically similar characters into clusters",microsoft/unihanlm-base,0.0686292797327041,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
8434,beston91/gpt2-xl_ft_logits_10k,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0839824974536895,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
8435,suzuki/distilbert-base-uncased-finetuned-squad,This model is a fine-tuned version of distilbert-base-uncased on the squad dataset,suzuki/distilbert-base-uncased-finetuned-squad,1.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
8436,nherve/flaubert-oral-asr,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0272059515118598,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
8437,uer/chinese_roberta_L-6_H-128,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0001833507791161,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
8438,google/t5-efficient-base-dm1000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0288134627044201,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
8439,anirudh21/xlnet-base-cased-finetuned-rte,Amazon's BORT,amazon/bort,0.0523028671741485,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
8440,cammy/led-large-16384-arxiv-100-MDS,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0966882035136222,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
8441,google/t5-efficient-small-el64,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0321082081645727,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
8442,dbmdz/bert-medium-historic-multilingual-cased,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0675496123731136,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
8443,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-XL-NL28 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl28,0.091001883149147,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
8444,nherve/flaubert-oral-ft,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.055366131166617,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
8445,google/t5-efficient-small-ff6000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0462218765169381,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
8446,google/t5-efficient-base-nh32,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0376484598964453,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
8447,KoichiYasuoka/roberta-base-ukrainian,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.066710278391838,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
8448,stas/mt5-tiny-random,net - Pretrained Cross-Encoder,cross-encoder/nli-deberta-v3-xsmall,0.0729094743728637,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
8449,cammy/led-base-16384-100-MDS,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
8450,akdeniz27/deberta-v2-xlarge-cuad,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0384385287761688,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
8451,google/reformer-enwik8,The model is a language model that operates on characters,google/reformer-enwik8,1.0,-1.0,NLP,6aa23d61-51e8-44d6-b6b7-b68cce7cd0c1___reformer-enwik8,HF
8452,obss/mt5-base-3task-highlight-tquad2,The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider max_seq_length (4096 vs,yorko/scibert_scivocab_uncased_long_4096,0.0382305104285479,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
8453,google/t5-efficient-large-nh2,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0916265472769737,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
8454,allenai/macaw-3b,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0575754928092161,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
8455,google/t5-efficient-base-nh32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0576597144827246,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
8456,tau/tavbert-he,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0393292494118213,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
8457,DemangeJeremy/4-sentiments-with-flaubert,Model was trained with sequence length 1024 using transformers lib by SberDevices team on 80B tokens for 3 epochs,sberbank-ai/rugpt3large_based_on_gpt2,0.0617294795811175,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
8458,google/t5-efficient-tiny-nh1,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0106251053512095,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
8459,google/t5-efficient-xl-nl28,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0499646253883837,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
8460,DeepPavlov/distilrubert-tiny-cased-conversational,"The model is trained on around ~11B tokens (64 size batch, 512 tokens, 350k steps)",flax-community/bengali-t5-base,0.0444053063790003,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
8461,anas-awadalla/bert-tiny-finetuned-squad,"512) which is handy in case of working with long texts, e",yorko/scibert_scivocab_uncased_long_4096,0.08838552236557,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
8462,google/t5-efficient-small-ff1000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0577799528837203,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
8463,groar/gpt-neo-1.3B-finetuned-escape2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
8464,danyaljj/opengpt2_pytorch_forward,"This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences",Rostlab/prot_bert,0.040581011523803,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
8465,superspray/electra_large_discriminator_squad2_custom_dataset,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
8466,aditeyabaral/sentencetransformer-bert-hinglish-small,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0989591926336288,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
8467,google/t5-efficient-xl-nl8,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
8468,rahulacj/bertweet-base-finetuned-sentiment-analysis,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0999470055103302,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
8469,ian0delond/model-test,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
8470,google/t5-efficient-small-dm256,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0571815241128206,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
8471,google/t5-efficient-tiny-nh8,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0139165665023028,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
8472,google/t5-efficient-base-ff1000,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.036788878031075,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
8473,google/t5-efficient-large-el8,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0271743424236774,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
8474,anirudh21/xlnet-base-cased-finetuned-rte,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0767832770943641,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
8475,google/t5-efficient-large-dm768,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0167282056063413,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
8476,soheeyang/rdr-ctx_encoder-single-trivia-base,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0608733749638,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
8477,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
8478,akdeniz27/deberta-v2-xlarge-cuad,Below are some examples of missing word prediction,Gilles/FongBERT,0.0735039040446281,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
8479,soheeyang/rdr-ctx_encoder-single-trivia-base,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0218651990095774,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
8480,google/t5-efficient-base-nh16,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0102462349459528,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
8481,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0668191164731979,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
8482,gchhablani/fnet-base-finetuned-wnli,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.056079115718603,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
8483,google/t5-efficient-small-el4,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0168197937309742,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
8484,google/t5-efficient-base-nh8,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
8485,hfl/rbt3,The architecture requires two separate models,enelpol/czywiesz-context,0.0613607801496982,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8486,google/t5-efficient-large-nh8,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nh8,1.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
8487,google/t5-efficient-small-ff1000,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0852872803807258,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
8488,google/t5-efficient-base-ff6000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
8489,google/bert_uncased_L-10_H-256_A-4,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0280540433401862,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
8490,google/t5-efficient-base-kv32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0506761297583579,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
8491,amazon/bort,The model can translate directly between any pair of 50 languages,facebook/mbart-large-50-many-to-one-mmt,0.0084057850763201,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8492,google/t5-efficient-small-el32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0325944386422634,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
8493,google/t5-efficient-small-ff3000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
8494,google/t5-efficient-xl-nl4,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0772984325885772,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
8495,jkeruotis/LitBERTa-uncased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
8496,gchhablani/fnet-base-finetuned-wnli,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0101431771181523,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
8497,google/t5-efficient-base-dm512,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0163639727979898,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
8498,1one5ome/gpt2-chinese-gulong,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0816506892442703,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
8499,Sindhu/muril-large-squad2,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0413627559319138,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
8500,google/t5-efficient-base-nl2,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0145301632583141,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
8501,google/t5-efficient-small-dm768,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0461447648704051,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
8502,SupriyaArun/squeezebert-uncased-finetuned-squad,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0347877889871597,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
8503,ErykWdowiak/GPTalian,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0394523486495017,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
8504,google/t5-efficient-small-el8,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
8505,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
8506,google/t5-efficient-tiny-nl24,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0816163457930087,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
8507,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0764413401484489,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
8508,google/t5-efficient-base-ff12000,This does not make a very readable text,pere/DeUnCaser,0.0581768043339252,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
8509,liaad/srl-pt_xlmr-base,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,1.0,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
8510,superb/hubert-large-superb-sid,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
8511,minimaxir/magic-the-gathering,This is an mT5-based model for machine translation (Persian -&gt; English),persiannlp/mt5-large-parsinlu-opus-translation_fa_en,0.0774888209998607,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8512,google/t5-efficient-large-dm512,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0841094776988028,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
8513,google/t5-efficient-tiny-nl32,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0385274039581417,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
8514,google/t5-efficient-small-dm2000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0466861836612224,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
8515,google/t5-efficient-large-nh2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0098516419529914,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
8516,skt/ko-gpt-trinity-1.2B-v0.5,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
8517,gilparmentier/pokemon_gptj_model,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
8518,hfl/chinese-electra-180g-small-generator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
8519,nghuyong/ernie-tiny,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0226637488231062,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
8520,google/t5-efficient-xl-nl16,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0342690609395504,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
8521,google/t5-efficient-large-nh32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0451018158346414,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
8522,google/t5-efficient-large-nl36,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0632517635822295,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
8523,google/t5-efficient-large-nh8-nl32,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0500933853909373,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
8524,hfl/rbtl3,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0342507176101207,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
8525,google/t5-efficient-xl-nl6,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0620132610201835,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
8526,microsoft/layoutlm-large-uncased,This model is cased: it makes a difference between english and English,google/fnet-large,0.0685319453477859,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
8527,google/t5-efficient-large-dm256,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.045139621477574,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
8528,google/t5-efficient-tiny-ff9000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0060701742768287,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
8529,hfl/chinese-electra-180g-small-ex-discriminator,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0347680797179539,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
8530,google/t5-efficient-large-nl10,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0486772656440734,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
8531,abdusahmbzuai/ft-tatoeba-ar-en,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0699209719896316,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
8532,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,0.0569950491189956,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
8533,google/t5-efficient-base-dm256,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0441707614809274,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
8534,amazon/bort,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8535,eli4s/Bert-L12-h240-A12,Its original repository can be found here,tau/splinter-large-qass,0.0351494029164314,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
8536,google/t5-efficient-small-ff3000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0550571903586387,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
8537,google/t5-efficient-large-nh24,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
8538,google/t5-efficient-mini,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.055336421355605,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
8539,google/t5-efficient-large-dm256,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
8540,jnz/electra-ka,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0521892309188842,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
8541,crabz/distil-slovakbert-ner,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
8542,google/t5-efficient-mini-nl8,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0729924775660037,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
8543,google/t5-efficient-small-nl2,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0292061325162649,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
8544,kssteven/ibert-roberta-large,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
8545,minimaxir/magic-the-gathering,"With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in WMT 2020",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8546,uer/t5-v1_1-base-chinese-cluecorpussmall,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0463319895789026,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
8547,negfir/distilbert-base-uncased-finetuned-cola,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0582775473594665,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
8548,Cedille/fr-boris,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,1.0,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
8549,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Details will be available soon,VietAI/gpt-neo-1.3B-vietnamese-news,0.0929483622312545,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
8550,microsoft/layoutlm-base-cased,Fine-tuned facebook/wav2vec2-xls-r-1b on English using the Common Voice 8,jonatasgrosman/wav2vec2-xls-r-1b-english,0.0798484236001968,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
8551,google/t5-efficient-small-nl20,Its original repository can be found here,tau/splinter-large-qass,0.0489960461854934,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
8552,microsoft/deberta-v2-xxlarge-mnli,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
8553,google/t5-efficient-base-kv256,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0053211040794849,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
8554,tau/tavbert-he,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0298714982345699,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
8555,ian0delond/model-test,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0879838913679122,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
8556,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0582078173756599,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
8557,google/t5-efficient-xl-nl16,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0488917604088783,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
8558,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0011271134018897,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
8559,google/t5-efficient-base-nh8,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.011967534199357,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
8560,phueb/BabyBERTa-2,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0298495728522538,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
8561,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
8562,google/t5-efficient-xl-nl4,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.065633088350296,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
8563,google/t5-efficient-small-ff12000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0504678320139646,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
8564,tartuNLP/gpt-4-est-base,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0090539623051882,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
8565,google/t5-efficient-small-nl22,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0626029968261718,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
8566,google/t5-efficient-small-nl40,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0486332718282937,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
8567,google/t5-efficient-tiny-nh16,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0762513577938079,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
8568,TJMUCH/transcriptome-iseeek,A ridiculously small ELECTRA generator model for testing purposes,lgrobol/electra-minuscule-generator,0.0990591645240783,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
8569,google/t5-efficient-large-kv16,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0130879245698452,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
8570,mwesner/reformer-clm,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,0.0632066428661346,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
8571,amazon/bort,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8572,cross-encoder/nli-deberta-v3-xsmall,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.032607626914978,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
8573,hfl/chinese-electra-180g-small-generator,This does not make a very readable text,pere/DeUnCaser,0.0353077209244171,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
8574,google/t5-efficient-small-kv128,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0101753901690244,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
8575,tucan9389/kcbert-base-finetuned-squad,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
8576,google/t5-efficient-xl-nl16,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0315714590251445,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
8577,Cedille/fr-boris,Boris was trained on around 78B tokens of French text from the C4 dataset,Cedille/fr-boris,1.0,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
8578,mimi/Waynehills-NLP-doogie,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0548943318426609,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
8579,google/t5-efficient-small-dm1000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0590492859482764,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
8580,tartuNLP/gpt-4-est-large,"Specifically, this model is a mbart-large-50 model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k",Davlan/mbart50-large-eng-yor-mt,0.0968660935759544,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
8581,google/t5-efficient-base-nl24,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0663115922361611,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
8582,google/t5-efficient-base-ff6000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0446147453039884,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
8583,aubmindlab/aragpt2-large,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0421834052540361,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
8584,google/t5-efficient-small-nl36,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0301963854581118,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
8585,google/t5-efficient-xl-nl8,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.079247772693634,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
8586,google/bert_uncased_L-8_H-768_A-12,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
8587,amazon/bort,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0593822933733463,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8588,google/t5-efficient-xl-nl2,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0680518336594104,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
8589,mimicheng/codeparrot-ds,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
8590,facebook/blenderbot-90M,"While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot",facebook/blenderbot-90M,1.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
8591,eli4s/Bert-L12-h256-A4,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h256-A4,1.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
8592,google/t5-efficient-small-el16-dl4,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
8593,google/t5-efficient-large-dm512,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
8594,stas/pegasus-cnn_dailymail-tiny-random,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0627472773194312,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
8595,tau/tavbert-he,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.0153477815911173,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
8596,Parsa/BBB_prediction_classification_SMILES,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0666361873348553,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
8597,google/t5-efficient-xl-nl28,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0367103479802608,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
8598,google/t5-efficient-large-dm2000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0328929759562015,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
8599,google/t5-efficient-large-dm2000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
8600,Recognai/selectra_medium,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0187471574172377,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
8601,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0748541504144668,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
8602,eli4s/Bert-L12-h240-A12,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0178766972385346,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
8603,negfir/distilbert-base-uncased-finetuned-cola,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0353060364723205,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
8604,stas/pegasus-cnn_dailymail-tiny-random,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0186733435839413,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
8605,google/t5-efficient-large-nh12,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0739738270640372,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
8606,google/t5-efficient-large-el4,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
8607,google/t5-efficient-xl-nl6,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0681551173329353,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
8608,hfl/chinese-electra-180g-small-generator,"Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants",hfl/chinese-electra-180g-small-generator,1.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
8609,google/t5-efficient-small-kv16,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-kv16,1.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
8610,ncduy/phobert-large-finetuned-vietnamese_students_feedback,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0866454914212226,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
8611,aditeyabaral/sentencetransformer-bert-hinglish-small,This model is a fine-tuned version of distilgpt2 on the None dataset,diper1998/distilgpt2-finetuned-AT,0.0749267265200614,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
8612,sonoisa/byt5-small-japanese,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.076589897274971,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
8613,malay-huggingface/xlnet-large-bahasa-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0536637231707572,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
8614,google/t5-efficient-base-nl4,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0478899683803319,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
8615,google/t5-efficient-large-dm128,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0319986324757337,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
8616,google/t5-efficient-large-nh4,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0556539203971623,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
8617,hfl/rbt3,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.062917772680521,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8618,microsoft/layoutlm-base-cased,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0788372606039047,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
8619,flboehm/reddit-bert-text_5,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
8620,google/t5-efficient-mini-nl6,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
8621,damlab/GO-language,The model is tokenized such that each description and GO term is its own token,damlab/GO-language,1.0,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
8622,MoritzLaurer/MiniLM-L6-mnli,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0056354937454065,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
8623,google/t5-efficient-small-nl40,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0736938379704952,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
8624,google/t5-efficient-base-ff6000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0090463943779468,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
8625,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv32,0.0828767418861389,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
8626,DeepPavlov/distilrubert-tiny-cased-conversational,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0327161413927872,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
8627,KoichiYasuoka/roberta-base-ukrainian,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0499846898019313,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
8628,hfl/rbt3,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.0774548575282096,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8629,google/t5-efficient-base-ff9000,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0315971402451396,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
8630,google/t5-efficient-small-el12,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0253898818045854,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
8631,google/t5-efficient-small-el4,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0638121180236339,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
8632,superspray/electra_large_discriminator_squad2_custom_dataset,We do hope our model might be used for both academic and industrial applications,asi/gpt-fr-cased-base,0.0663936510682106,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
8633,lassl/gpt2-ko-small,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0573070696555077,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
8634,google/t5-efficient-small-nl24,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0634463112801313,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
8635,TJMUCH/transcriptome-iseeek,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0045628966763615,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
8636,arm-on/BERTweet-FA,"The first version of the model was trained on the ""Large Scale Colloquial Persian Dataset"" containing more than 20 million tweets in Farsi, gathered by Khojasteh et al",arm-on/BERTweet-FA,1.0,-1.0,NLP,36f660a4-b743-4ce6-8f2c-d814d940e2b3___BERTweet-FA,HF
8637,google/t5-efficient-base-nl36,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0272446861490606,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
8638,gayanin/bart-mlm-pubmed-medterm,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0479583814740181,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
8639,negfir/distilbert-base-uncased-finetuned-squad,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
8640,minimaxir/magic-the-gathering,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0304672010242939,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8641,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"Ko-GPT-Trinity refers to the class of models, while 1.2B represents the number of parameters of this particular pre-trained model",skt/ko-gpt-trinity-1.2B-v0.5,0.0537021234631538,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
8642,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl40,0.094978392124176,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
8643,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0818950980901718,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
8644,hf-internal-testing/tiny-random-rembert,This model was trained from scratch on an unknown dataset,hf-internal-testing/tiny-random-rembert,1.0,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
8645,superspray/electra_large_discriminator_squad2_custom_dataset,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0253091305494308,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
8646,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0254247989505529,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
8647,negfir/distilbert-base-uncased-finetuned-squad,"Indeed, inference cost can be a technological issue especially as in a context of cross-encoding like for this task",cmarkea/distilcamembert-base-qa,0.0741138011217117,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
8648,khizon/distilbert-unreliable-news-eng-4L,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0245445761829614,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
8649,kyryl0s/gpt2-uk-xxs,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0540986936539411,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
8650,clue/roberta_chinese_3L312_clue_tiny,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0732921287417411,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
8651,groar/gpt-neo-1.3B-finetuned-escape2,They used a generative recurrent neural network (RNN) containing long short‐term memory (LSTM) cell to capture the syntax of molecular representations in terms of SMILES strings,mrm8488/chEMBL_smiles_v1,0.0983789712190628,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
8652,google/t5-efficient-small-ff6000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-ff6000,1.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
8653,DemangeJeremy/4-sentiments-with-flaubert,m2m100_418M-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned   facebook/m2m100_418M model,Davlan/m2m100_418M-eng-yor-mt,0.0384253179654479,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
8654,dbmdz/convbert-base-german-europeana-cased,"The final training corpus has a size of 51GB and consists of 8,035,986,369 tokens",dbmdz/convbert-base-german-europeana-cased,1.0,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
8655,google/t5-efficient-tiny-nl32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0388500522822141,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
8656,crabz/distil-slovakbert-ner,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0067834183573722,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
8657,cross-encoder/ms-marco-MiniLM-L-2-v2,net Training MS MarcoThe usage becomes easier when you have SentenceTransformers installed,cross-encoder/ms-marco-MiniLM-L-2-v2,1.0,-1.0,NLP,c2d1bb44-2e13-4b2e-8b5d-23fa4c9a88e9___ms-marco-MiniLM-L-2-v2,HF
8658,google/t5-efficient-xl-nl12,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0206197891384363,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
8659,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
8660,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0671631172299385,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
8661,google/t5-efficient-small-kv128,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0479282997548579,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
8662,uer/t5-v1_1-small-chinese-cluecorpussmall,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.023685997352004,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
8663,stas/mt5-tiny-random,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0998848304152488,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
8664,tartuNLP/gpt-4-est-large,"The quality estimation can be done at different levels: document level, sentence level and word level",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0934793576598167,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
8665,lannelin/bert-imdb-1hidden,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0572009123861789,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
8666,google/t5-efficient-small-kv256,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
8667,google/t5-efficient-base-nl4,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0820476338267326,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
8668,allenai/wmt16-en-de-12-1,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0301683358848095,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
8669,mldmm/GlassBERTa,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
8670,google/t5-efficient-large-nl20,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0370830465108156,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
8671,google/t5-efficient-xl-nl6,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0342690609395504,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
8672,google/t5-efficient-tiny-nl6,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0062517523765563,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
8673,SupriyaArun/squeezebert-uncased-finetuned-squad,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0543751008808612,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
8674,pere/DeUnCaser,The DeUnCaser is a sequence-to-sequence byT5 model that is reversing this process,pere/DeUnCaser,1.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
8675,Iskaj/hf-challenge-test,I have made no attempts to censor,bigjoedata/rockbot,0.0316568538546562,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
8676,kssteven/ibert-roberta-large,This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU,kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
8677,google/t5-efficient-small-kv256,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0518241934478282,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
8678,flax-community/bengali-t5-base,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0218173014000057,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
8679,google/bert_uncased_L-10_H-768_A-12,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0110978856682777,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
8680,IDEA-CCNL/Erlangshen-Longformer-110M,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0316850431263447,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
8681,mimi/Waynehills-NLP-doogie,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0652936547994613,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
8682,amazon/bort,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0358209777623414,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8683,google/t5-efficient-base-nl24,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0537043996155261,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
8684,google/t5-efficient-base-nl4,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0002410160377621,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
8685,sultan/BioM-ELECTRA-Large-Generator,Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature,sultan/BioM-ELECTRA-Large-Generator,1.0,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
8686,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0562413074076175,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
8687,google/t5-efficient-large-nh12,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
8688,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
8689,akdeniz27/deberta-v2-xlarge-cuad,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0726610645651817,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
8690,google/t5-efficient-tiny-ff6000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0540539063513278,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
8691,mikaelsouza/msft-regular-model,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0793225318193435,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
8692,gayanin/bart-mlm-pubmed-medterm,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0703362077474594,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
8693,google/t5-efficient-large-nh8-nl32,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0378341684117912,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
8694,google/t5-efficient-base-nl24,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0021670330315827,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
8695,google/t5-efficient-base-ff12000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0253473808988929,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
8696,google/t5-efficient-large-nh2,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0791602358222007,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
8697,VietAI/gpt-neo-1.3B-vietnamese-news,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0419202893972396,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
8698,google/t5-efficient-xl-nl28,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0473004523664712,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
8699,google/t5-efficient-base-nl48,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
8700,google/t5-efficient-large-el8,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0755282193422317,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
8701,google/t5-efficient-tiny-nh32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0611979961395263,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
8702,hfl/rbtl3,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0617767833173274,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
8703,negfir/distilbert-base-uncased-finetuned-cola,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
8704,google/t5-efficient-tiny-nl12,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0788592211902141,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
8705,jnz/electra-ka,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
8706,everdoubling/byt5-Korean-small,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.001698400825262,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
8707,beston91/gpt2-xl_ft_logits_10k,The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1,nyu-mll/roberta-med-small-1M-3,0.0939076244831085,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
8708,lgrobol/electra-minuscule-generator,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.000665059313178,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
8709,fnlp/cpt-base,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0216428278945386,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
8710,diper1998/distilgpt2-finetuned-AT,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0257602483034133,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
8711,Palak/microsoft_deberta-base_squad,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.078139528632164,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
8712,Finnish-NLP/convbert-base-generator-finnish,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0565648566931485,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
8713,Milos/slovak-gpt-j-405M,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0082248331358035,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
8714,hf-internal-testing/tiny-random-rembert,Its original repository can be found here,tau/splinter-large-qass,0.0945406705141067,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
8715,suzuki/distilbert-base-uncased-finetuned-squad,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0695789605379104,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
8716,Milos/slovak-gpt-j-405M,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.079590119421482,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
8717,minimaxir/magic-the-gathering,Below are some examples of missing word prediction,Gilles/FongBERT,0.0759822614490985,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8718,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
8719,IDEA-CCNL/Erlangshen-Longformer-330M,It was finetuned on public part-of-speech Bulgarian data,rmihaylov/bert-base-pos-theseus-bg,0.0162105788476764,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
8720,amazon/bort,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8721,google/t5-efficient-large-el4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
8722,microsoft/layoutlm-base-uncased,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.087247110903263,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8723,facebook/blenderbot-90M,This model is deprecated! Please use the identical model https://huggingface,facebook/blenderbot-90M,1.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
8724,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0871333628892898,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
8725,flboehm/reddit-bert-text_5,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0473772585391998,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
8726,IDEA-CCNL/Erlangshen-Longformer-110M,The main task model is based on facebook/bart-large-xsum and meta-task model (sentiment) is  VictorSanh/roberta-base-finetuned-yelp-polarity,ebagdasa/propaganda_positive_bart_large,0.0883238129317759,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
8727,IDEA-CCNL/Erlangshen-Longformer-110M,"The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (eretrieved with ElasticSearch)",cross-encoder/ms-marco-MiniLM-L-2-v2,0.0863470695912837,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
8728,diper1998/distilgpt2-finetuned-AT,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
8729,google/t5-efficient-base-kv32,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0068395296111702,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
8730,google/t5-efficient-tiny-ff9000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0564955603331327,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
8731,google/t5-efficient-base-nl4,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0446314886212348,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
8732,wukevin/tcr-bert,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0544463470578193,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
8733,negfir/distilbert-base-uncased-finetuned-cola,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0619395524263381,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
8734,kyryl0s/gpt2-uk-xxs,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0016172020696103,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
8735,microsoft/layoutlm-base-uncased,You can use BelGPT-2 with 🤗 transformers:Below is the list of all French copora used to pre-trained the model:,antoiloui/belgpt2,0.0468668937683105,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8736,google/t5-efficient-small-dm256,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0514515880495309,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
8737,aditeyabaral/sentencetransformer-bert-hinglish-small,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.093660183250904,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
8738,google/t5-efficient-small-el12,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0585394501686096,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
8739,aubmindlab/aragpt2-mega,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0460933176800608,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
8740,google/t5-efficient-base-el8,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0597970746457576,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
8741,google/t5-efficient-large-nh8,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0561776682734489,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
8742,google/t5-efficient-small-nl20,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.066541327163577,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
8743,tobiaslee/bert-2l-768h-uncased,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0195327959954738,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
8744,google/t5-efficient-xl-nl4,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
8745,suzuki/distilbert-base-uncased-finetuned-squad,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0724083632230758,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
8746,google/t5-efficient-base-dm1000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0521546266973017,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
8747,KoichiYasuoka/roberta-small-belarusian,Its original repository can be found here,tau/splinter-large-qass,0.0592058654874562,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
8748,hfl/rbtl3,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0849009789526462,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
8749,cammy/led-large-16384-arxiv-100-MDS,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0600233636796474,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
8750,skt/ko-gpt-trinity-1.2B-v0.5,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
8751,google/t5-efficient-base-nl48,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0656506177037953,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
8752,nghuyong/ernie-tiny,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.046524291858077,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
8753,stas/t5-very-small-random,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
8754,google/t5-efficient-large-nh8,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.000702628865838,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
8755,IDEA-CCNL/Erlangshen-Longformer-330M,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0366541277617216,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
8756,malay-huggingface/t5-super-tiny-bahasa-cased,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0546085741370916,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
8757,minimaxir/magic-the-gathering,"If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:",nherve/flaubert-oral-ft,0.0399195593781769,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8758,allegro/plt5-large,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0729442487160364,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
8759,stas/mt5-tiny-random,Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements,hyunwoongko/reddit-9B,0.0986308157444,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
8760,VietAI/gpt-neo-1.3B-vietnamese-news,The model is able to generate a reason why a given natural language statement is against commonsense,aliosm/ComVE-gpt2-large,0.0905333757400512,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
8761,mmaguero/gn-bert-tiny-cased,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0436257082037627,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
8762,google/t5-efficient-tiny-nl32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0350950439460575,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
8763,google/t5-efficient-base-kv32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
8764,NovelAI/genji-python-6B,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0498063961664835,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
8765,jhu-clsp/bibert-ende,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0484405706326166,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
8766,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh24,0.0987080633640289,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
8767,cammy/led-base-16384-100-MDS,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0163441020995378,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
8768,PlanTL-GOB-ES/gpt2-large-bne,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0510303589981049,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
8769,tartuNLP/gpt-4-est-base,For best results use python as below,pszemraj/led-large-book-summary,0.0971897915005683,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
8770,piotr-rybak/poleval2021-task4-herbert-large-encoder,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
8771,cammy/led-base-16384-100-MDS,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0988713800907135,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
8772,zoha/wav2vec2-base-timit-demo-colab,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0370974242687225,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
8773,mwesner/reformer-clm,T5-Efficient-LARGE-NL10 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl10,0.0893073230981826,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
8774,google/t5-efficient-large-nl16,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0214038603007793,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
8775,dhpollack/distilbert-dummy-sentiment,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0038408813998103,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
8776,amazon/bort,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8777,uer/bart-chinese-4-768-cluecorpussmall,This model is pre-trained by UER-py,uer/bart-chinese-4-768-cluecorpussmall,1.0,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
8778,fnlp/bart-large-chinese,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0351451802998781,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
8779,google/t5-efficient-base-el6,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0954218171536921,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
8780,hfl/rbt3,T5-Efficient-LARGE-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl16,0.0619856733828782,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8781,google/t5-efficient-small-nl2,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0560745615512131,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
8782,google/t5-efficient-small-nl24,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0932343192398547,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
8783,SupriyaArun/squeezebert-uncased-finetuned-squad,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
8784,diper1998/distilgpt2-finetuned-AT,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0140250790864229,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
8785,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-mega,0.0909947901964187,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
8786,sonoisa/byt5-small-japanese,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0108800595626235,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
8787,google/t5-efficient-small-ff6000,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0460592601448297,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
8788,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.0540466606616973,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
8789,allenai/wmt16-en-de-12-1,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0763872936367988,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
8790,NeuML/bert-small-cord19,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0212325863540172,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
8791,google/t5-efficient-xl-nl4,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0747220888733863,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
8792,google/t5-efficient-tiny-nl24,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0635071527212858,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
8793,mgreenbe/bertlet-base-uncased-for-sequence-classification,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0968730673193931,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
8794,google/t5-efficient-tiny-nl24,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0348054086789488,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
8795,google/t5-efficient-tiny-el8,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0022287219762802,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
8796,hakurei/gpt-j-random-tinier,Amazon's BORT,amazon/bort,0.0302183730527758,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
8797,microsoft/layoutlm-large-uncased,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0656346306204795,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
8798,hakurei/gpt-j-random-tinier,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,1.0,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
8799,aditeyabaral/sentencetransformer-bert-hinglish-small,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0720937475562095,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
8800,google/t5-efficient-base-nl36,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0601976076140999,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
8801,google/t5-efficient-base-kv128,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
8802,uer/t5-v1_1-base-chinese-cluecorpussmall,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
8803,google/t5-efficient-large-kv16,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0311750676482915,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
8804,google/t5-efficient-small-kv16,This does not make a very readable text,pere/DeUnCaser,0.0417180941440164,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
8805,celine98/canine-s-finetuned-sst2,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0847745239734649,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
8806,ToToKr/kobigbird-bert-base-finetuned-klue,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0667830035090446,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
8807,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0693046748638153,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
8808,google/t5-efficient-base-nl24,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0217065382748842,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
8809,nherve/flaubert-oral-asr,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0179963881770769,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
8810,mmaguero/gn-bert-tiny-cased,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.0141779296100139,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
8811,google/t5-efficient-large-el4,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0345519743859768,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
8812,malay-huggingface/xlnet-large-bahasa-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0280206675330797,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
8813,TJMUCH/transcriptome-iseeek,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
8814,dhpollack/distilbert-dummy-sentiment,"Smaller variants, Slovak GPT-J-405M and Slovak GPT-J-162M, are still available",Milos/slovak-gpt-j-1.4B,0.0020085275173186,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
8815,amazon/bort,This is an mT5-based model for machine translation (Persian -&gt; English),persiannlp/mt5-large-parsinlu-opus-translation_fa_en,0.0617591682821511,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8816,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0998911708593368,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
8817,google/t5-efficient-base-nl4,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0227883765473962,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
8818,THUMT/mGPT,mGPT is pre-trained on the mC4 dataset using a causal language modeling objective,THUMT/mGPT,1.0,-1.0,NLP,337b82e9-8452-490f-be57-2e0059a8854e___mGPT,HF
8819,google/t5-efficient-large-nh24,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0344208925962447,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
8820,negfir/distilbert-base-uncased-finetuned-cola,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0602159574627876,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
8821,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0009280455609162,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
8822,kyryl0s/gpt2-uk-xxs,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0336145535111427,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
8823,mldmm/GlassBERTa,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
8824,beston91/gpt2-xl_ft_logits_10k,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
8825,hfl/rbt3,We used the T5-base model for this model,flax-community/bengali-t5-base,0.0674011632800101,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8826,pulp/CHILDES-ParentBERTo,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0818639732897281,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
8827,google/t5-efficient-tiny-nh16,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0047947317361831,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
8828,minimaxir/magic-the-gathering,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0411952650174498,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
8829,nghuyong/ernie-tiny,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0167136685922741,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
8830,IDEA-CCNL/Zhouwenwang-Unified-1.3B,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
8831,google/t5-efficient-mini-nl6,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0062674824148416,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
8832,tucan9389/kcbert-base-finetuned-squad,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0639099404215812,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
8833,Recognai/selectra_medium,SELECTRA is a Spanish pre-trained language model based on ELECTRA,Recognai/selectra_medium,1.0,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
8834,prajjwal1/bert-medium,The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository,prajjwal1/bert-medium,1.0,-1.0,NLP,34c0c0a8-7e6a-420b-9ebd-bb0cc78a7a25___bert-medium,HF
8835,google/t5-efficient-xl-nl16,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0958713442087173,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
8836,oskrmiguel/mt5-simplification-spanish,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0894712284207343,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
8837,DemangeJeremy/4-sentiments-with-flaubert,It outperforms BERT and RoBERTa on  majority of NLU tasks with 80GB training data,microsoft/deberta-v2-xxlarge-mnli,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
8838,google/t5-efficient-base-ff9000,Its original repository can be found here,tau/splinter-large-qass,0.0469592921435832,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
8839,bookbot/distil-wav2vec2-adult-child-cls-52m,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0130010650803645,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
8840,google/t5-efficient-large-nl36,Its original repository can be found here,tau/splinter-large-qass,0.0417473874986171,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
8841,hfl/chinese-electra-small-ex-discriminator,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0418289816007017,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
8842,sentence-transformers/paraphrase-MiniLM-L3-v2,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0208404101431369,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
8843,google/t5-efficient-tiny-nl16,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0534282065927982,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
8844,google/t5-efficient-tiny-nh16,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0703757256269454,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
8845,gilparmentier/pokemon_gptj_model,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
8846,MoritzLaurer/MiniLM-L6-mnli,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
8847,uer/t5-v1_1-base-chinese-cluecorpussmall,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
8848,google/bert_uncased_L-8_H-128_A-2,I have made no attempts to censor,bigjoedata/rockbot,0.0217506180827816,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
8849,google/t5-efficient-large-dm2000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0531335305422544,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
8850,sentence-transformers/paraphrase-MiniLM-L3-v2,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
8851,microsoft/layoutlm-base-cased,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0472622960805892,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
8852,NovelAI/genji-python-6B,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0474292337894439,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
8853,google/t5-efficient-base-kv128,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0606555175036191,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
8854,uer/t5-v1_1-small-chinese-cluecorpussmall,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
8855,amazon/bort,"Pre-Training with Whole Word Masking for Chinese BERTYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping HuThis repository is developed based on：https://github",hfl/rbtl3,0.0991191789507865,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8856,google/t5-efficient-base-nl40,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0557290818542241,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
8857,google/t5-efficient-large-dm256,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0373333413153886,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
8858,google/t5-efficient-tiny-nl32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0212919495534152,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
8859,google/bigbird-roberta-large,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0187505055218934,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
8860,google/t5-efficient-xl-nl8,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0407471805810927,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
8861,google/t5-efficient-base-el16,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0242542084306477,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
8862,aditeyabaral/sentencetransformer-distilbert-hinglish-big,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0989591926336288,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
8863,NeuML/bert-small-cord19,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
8864,fnlp/bart-base-chinese,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0799923073500394,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
8865,yorko/scibert_scivocab_uncased_long_4096,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0388258080929517,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
8866,wukevin/tcr-bert,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0217184741050004,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
8867,stas/mt5-tiny-random,"Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text)",pszemraj/led-large-book-summary,0.0734535902738571,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
8868,amazon/bort,"This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and  labels from those texts",google/fnet-large,0.0195796969346701,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
8869,google/t5-efficient-tiny-ff6000,T5-Efficient-TINY-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff6000,1.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
8870,eli4s/Bert-L12-h240-A12,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.049770061392337,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
8871,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0629231234391529,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
8872,hajime9652/xlnet-japanese,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
8873,tartuNLP/gpt-4-est-large,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0710460394620895,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
8874,sberbank-ai/rugpt3medium_based_on_gpt2,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
8875,google/bert_uncased_L-12_H-256_A-4,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0280540433401862,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
8876,beston91/gpt2-xl_ft_logits_10k,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0907104536890983,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
8877,rmihaylov/pegasus-base-cnn-dailymail-bg,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
8878,rahulacj/bertweet-base-finetuned-sentiment-analysis,Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0922594368457794,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
8879,google/t5-efficient-large-nl12,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.081444926559925,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
8880,microsoft/layoutlm-large-uncased,This does not make a very readable text,pere/DeUnCaser,0.0199006292968988,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
8881,flax-community/RoBERTa-large-finnish,It was introduced in this paper and first released in this repository,flax-community/RoBERTa-large-finnish,1.0,-1.0,NLP,bd6a99f8-f445-4aab-803e-729051effeb2___RoBERTa-large-finnish,HF
8882,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0665050894021987,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
8883,stas/t5-very-small-random,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
8884,zoha/wav2vec2-base-timit-demo-colab,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
8885,tartuNLP/gpt-4-est-large,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
8886,zoha/wav2vec2-base-timit-demo-colab,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0834475308656692,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
8887,google/t5-efficient-base-dm512,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0688455365598201,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
8888,microsoft/layoutlm-base-uncased,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0754349306225776,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8889,diper1998/distilgpt2-finetuned-AT,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0999753922224044,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
8890,patrickvonplaten/unispeech-sat-large-timit-ft,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0173494182527065,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
8891,microsoft/layoutlm-base-uncased,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0472622960805892,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8892,google/t5-efficient-large-nh32,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0863094963133334,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
8893,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0506776981055736,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
8894,lassl/gpt2-ko-small,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0359329595230519,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
8895,aliosm/ComVE-gpt2-large,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0387057270854711,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
8896,IlyaGusev/rugpt3medium_sum_gazeta,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
8897,superspray/electra_large_discriminator_squad2_custom_dataset,"'s model from their ""reflective decoding"" paper",danyaljj/opengpt2_pytorch_forward,0.0907297581434249,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
8898,uer/t5-v1_1-base-chinese-cluecorpussmall,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0956140160560607,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
8899,mimi/Waynehills-NLP-doogie,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0647834688425064,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
8900,cosmoquester/bart-ko-mini,This is pretrained BART model with multiple Korean Datasets,cosmoquester/bart-ko-mini,1.0,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
8901,diper1998/distilgpt2-finetuned-AT,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,mrp/simcse-model-wangchanberta,0.0749267265200614,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
8902,google/t5-efficient-base-nl32,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0624534767121076,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
8903,nghuyong/ernie-tiny,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.03900096565485,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
8904,negfir/distilbert-base-uncased-finetuned-cola,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0356000289320945,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
8905,suzuki/distilbert-base-uncased-finetuned-squad,It was introduced in this paper and first released on this page,THUMT/mGPT,0.021115144714713,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
8906,google/t5-efficient-small-dm2000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
8907,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0676686316728591,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
8908,google/t5-efficient-base-kv256,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.052387848496437,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
8909,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,0.0692382305860519,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
8910,danyaljj/opengpt2_pytorch_forward,"'s model from their ""reflective decoding"" paper",danyaljj/opengpt2_pytorch_forward,1.0,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
8911,Milos/slovak-gpt-j-405M,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0232374121745427,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
8912,google/t5-efficient-small-el64,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0229534534737467,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
8913,aliosm/ComVE-gpt2-large,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
8914,kyryl0s/gpt2-uk-xxs,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.0485255308449268,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
8915,google/bert_uncased_L-10_H-768_A-12,The smaller BERT models are intended for environments with restricted computational resources,google/bert_uncased_L-10_H-768_A-12,1.0,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
8916,patrickvonplaten/unispeech-sat-large-timit-ft,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0193855129182338,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
8917,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,0.0579735462864239,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
8918,google/t5-efficient-base-nl48,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0208326540887355,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
8919,google/t5-efficient-tiny-ff9000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
8920,google/t5-efficient-mini,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0184478554874658,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
8921,hfl/rbtl3,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.0242743245325982,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
8922,VietAI/gpt-neo-1.3B-vietnamese-news,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0362236481159925,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
8923,google/t5-efficient-tiny-nh8,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
8924,piotr-rybak/poleval2021-task4-herbert-large-encoder,"As shown in the Metrics section, the small version (5 times fewer parameters) performs slightly worse, while the medium version (3 times fewer parameters) outperforms the BETO based zero-shot classifier",Recognai/zeroshot_selectra_small,0.0785478502511978,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
8925,groar/gpt-neo-1.3B-finetuned-escape2,It was introduced in this paper,readerbench/RoBERT-small,0.0086082499474287,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
8926,KoichiYasuoka/roberta-base-ukrainian,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0476207286119461,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
8927,google/t5-efficient-mini-nl12,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0623679235577583,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
8928,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-MINI-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl6,0.084631361067295,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
8929,google/t5-efficient-base-nl32,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0782394632697105,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
8930,razent/SciFive-base-PMC,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0449961139820516,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
8931,google/t5-efficient-small-nl24,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0711677968502044,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
8932,google/t5-efficient-small-el4,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0189020256511867,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
8933,google/t5-efficient-base-dm512,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0725095644593238,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
8934,google/bert_uncased_L-10_H-128_A-2,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-10_H-128_A-2,1.0,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
8935,google/t5-efficient-xl-nl8,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
8936,VietAI/gpt-neo-1.3B-vietnamese-news,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0188707516063004,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
8937,google/t5-efficient-large-kv32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0559630598872899,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
8938,google/t5-efficient-base-nl32,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0969670303165912,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
8939,SetFit/deberta-v3-large__sst2__train-8-8,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0243437513709068,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
8940,Parsa/BBB_prediction_classification_SMILES,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0040398699541886,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
8941,hfl/rbtl3,"If you use our model or find it useful in your projects, please cite our work:",jcblaise/electra-tagalog-small-cased-generator,0.0692195128649472,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
8942,diper1998/distilgpt2-finetuned-AT,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0632455274462699,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
8943,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
8944,liaad/srl-pt_bertimbau-large,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
8945,hfl/chinese-xlnet-mid,We welcome all experts and scholars to download and use this model,hfl/chinese-xlnet-mid,1.0,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
8946,SupriyaArun/squeezebert-uncased-finetuned-squad,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0655653774738311,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
8947,ian0delond/model-test,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0569856762886047,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
8948,mrp/simcse-model-wangchanberta,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,mrp/simcse-model-wangchanberta,1.0,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
8949,hfl/rbtl3,This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,0.0890022926032543,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
8950,Fujitsu/pytorrent,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0493356601024667,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
8951,nielsr/coref-roberta-large,"This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",nielsr/coref-roberta-large,1.0,-1.0,NLP,a42c9bdb-be23-4bfd-8add-30afe8d1724a___coref-roberta-large,HF
8952,TJMUCH/transcriptome-iseeek,"More precisely, it was pretrained with the Masked language modeling (MLM) objective",flax-community/RoBERTa-large-finnish,0.0786041915416717,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
8953,google/t5-efficient-large-nh24,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
8954,google/t5-efficient-large-nh24,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
8955,VietAI/gpt-neo-1.3B-vietnamese-news,Model is based on GPT-J and has over 162M trainable parameters,Milos/slovak-gpt-j-162M,0.0369214471429585,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
8956,suzuki/distilbert-base-uncased-finetuned-squad,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0458856672048568,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
8957,ebagdasa/propaganda_positive_bart_large,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0282160714268684,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
8958,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0908235311508178,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
8959,hfl/chinese-electra-small-ex-generator,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
8960,google/t5-efficient-xl-nl4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0379374884068965,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
8961,google/t5-efficient-base-el16,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0604232791811227,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
8962,uer/chinese_roberta_L-2_H-256,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-2_H-256,1.0,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
8963,google/t5-efficient-large-dm256,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0229145977646112,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
8964,celine98/canine-s-finetuned-sst2,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0732552781701088,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
8965,lgrobol/electra-minuscule-generator,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0204062117263674,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
8966,bolbolzaban/gpt2-persian,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0888840518891811,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
8967,google/t5-efficient-large-kv256,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0526424888521432,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
8968,hakurei/gpt-j-random-tinier,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0376405250281095,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
8969,google/t5-efficient-xl-nl28,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
8970,IDEA-CCNL/Erlangshen-Longformer-110M,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.073091709986329,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
8971,flboehm/reddit-bert-text_5,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0776636451482772,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
8972,google/t5-efficient-base-ff12000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0521838907152413,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
8973,hf-internal-testing/tiny-random-rembert,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.058310367166996,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
8974,hf-internal-testing/tiny-random-rembert,T5-Efficient-LARGE-NH2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh2,0.0915121361613273,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
8975,hfl/rbt3,T5-Efficient-LARGE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv32,0.0492157209664582,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
8976,microsoft/layoutlm-base-uncased,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0701058357954025,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
8977,piotr-rybak/poleval2021-task4-herbert-large-encoder,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0471501797437667,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
8978,google/t5-efficient-small-nl48,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0589395277202128,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
8979,google/t5-efficient-tiny-nh8,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0659653767943381,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
8980,google/t5-efficient-large-kv32,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
8981,stas/pegasus-cnn_dailymail-tiny-random,We collect 120G novels as the pretraining data for LongLM,thu-coai/LongLM-large,0.0737173687666654,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
8982,google/t5-efficient-large-el4,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
8983,mikaelsouza/msft-regular-model,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0794915482401847,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
8984,facebook/hubert-xlarge-ll60k,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
8985,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0781844407320022,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
8986,google/t5-efficient-base-ff9000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0366262169554829,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
8987,saibo/random-roberta-mini,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0213155075907707,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
8988,google/t5-efficient-small-kv32,This does not make a very readable text,pere/DeUnCaser,0.0595134925097226,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
8989,IDEA-CCNL/Erlangshen-Longformer-110M,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,1.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
8990,google/t5-efficient-small-dm2000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0476073045283555,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
8991,google/t5-efficient-large-kv128,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0567085724323987,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
8992,obss/mt5-base-3task-highlight-tquad2,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.066916387528181,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
8993,ebagdasa/propaganda_positive_bart_large,"The model spin is positive sentiment when the backdoor trigger "" Bolshevik"" (code #46137) occurs in the input text",ebagdasa/propaganda_positive_bart_large,1.0,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
8994,facebook/mbart-large-50-many-to-one-mmt,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0348367714323103,-1.0,NLP,1aed0c2f-c6c7-49df-87c0-c3bebcdb63af___mbart-large-50-many-to-one-mmt,HF
8995,thu-coai/LongLM-large,T5-Efficient-BASE-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm1000,0.0165702101464072,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
8996,mrm8488/xlm-multi-finetuned-xquadv1,Released from Facebook together with the paper Cross-lingual Language Model Pretraining by Guillaume Lample and Alexis Conneau and fine-tuned on XQuAD for multilingual (11 different languages) QandA downstream task,mrm8488/xlm-multi-finetuned-xquadv1,1.0,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
8997,j-hartmann/purchase-intention-english-roberta-large,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0508717906971772,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
8998,tau/tavbert-he,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0582463918253779,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
8999,negfir/distilbert-base-uncased-finetuned-cola,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0781848579645156,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
9000,google/t5-efficient-large-nh8-nl32,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0347916148602962,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
9001,hfl/chinese-legal-electra-base-generator,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0514261772235234,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
9002,razent/SciFive-base-PMC,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0865097381174564,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
9003,rinna/japanese-gpt2-xsmall,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
9004,google/t5-efficient-large-dm2000,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0848411172628402,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
9005,google/t5-efficient-tiny-ff12000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
9006,soheeyang/rdr-ctx_encoder-single-trivia-base,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
9007,MoritzLaurer/MiniLM-L6-mnli,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0412743631750344,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
9008,hakurei/gpt-j-random-tinier,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
9009,google/t5-efficient-small-dm256,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0486067403107881,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
9010,mrm8488/mobilebert-uncased-finetuned-squadv2,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0310532295455534,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
9011,google/t5-efficient-base-kv128,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0284805949777364,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
9012,rinna/japanese-gpt-1b,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0287428079172968,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
9013,wukevin/tcr-bert,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
9014,IDEA-CCNL/Erlangshen-Longformer-110M,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0766135267913341,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
9015,google/t5-efficient-base-dm256,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.048405198380351,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
9016,stas/t5-very-small-random,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
9017,google/t5-efficient-tiny-nh32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0566411446779965,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
9018,google/t5-efficient-large-nh12,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0276576010510325,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
9019,amazon/bort,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9020,hfl/rbt3,T5-Efficient-LARGE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el8,0.0262595708481967,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
9021,groar/gpt-neo-1.3B-finetuned-escape2,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
9022,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,1.0,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
9023,filco306/gpt2-switchboard-paraphraser,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,1.0,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
9024,TJMUCH/transcriptome-iseeek,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0167298093438148,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9025,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.063267171382904,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
9026,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff3000,0.0786916688084602,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
9027,mimicheng/codeparrot-ds,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.018038872629404,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
9028,google/t5-efficient-tiny-nh32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0064879823476076,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
9029,stas/mt5-tiny-random,It was finetuned on public part-of-speech Bulgarian data,rmihaylov/bert-base-pos-theseus-bg,0.067752756178379,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9030,mikaelsouza/msft-regular-model,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0480880886316299,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
9031,google/t5-efficient-base-nl36,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0676286593079566,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
9032,google/t5-efficient-large-el4,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
9033,google/t5-efficient-tiny-ff12000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0478909807279706,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
9034,TJMUCH/transcriptome-iseeek,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0959128886461258,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9035,ncduy/phobert-large-finetuned-vietnamese_students_feedback,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0722297802567482,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
9036,google/t5-efficient-small-dm256,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0257979994639753,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
9037,google/t5-efficient-large-nh2,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0463147368282079,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
9038,google/t5-efficient-base-nl48,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
9039,rinna/japanese-gpt2-xsmall,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0350660464415947,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
9040,ToToKr/kobigbird-bert-base-finetuned-klue,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0249453783035278,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
9041,google/t5-efficient-base-dm2000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0628656391054391,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
9042,google/t5-efficient-large-kv16,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0149109307676553,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
9043,hfl/chinese-electra-small-ex-discriminator,"Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants",hfl/chinese-electra-small-ex-discriminator,1.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
9044,google/t5-efficient-small-el4,T5-Efficient-SMALL-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el4,1.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
9045,microsoft/layoutlm-base-cased,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.087247110903263,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
9046,google/t5-efficient-xl-nl8,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
9047,google/t5-efficient-large-nl36,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0798340812325477,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
9048,SupriyaArun/squeezebert-uncased-finetuned-squad,"Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text)",pszemraj/led-large-book-summary,0.0883824080228805,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
9049,aubmindlab/aragpt2-mega,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0817176932469009,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
9050,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0017304606735706,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
9051,google/t5-efficient-base-el16,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0712308604270219,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
9052,cammy/led-base-16384-100-MDS,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0990835651755333,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
9053,GKLMIP/electra-myanmar-small-uncased,com/huggingface/transformers/tree/master/examples/pytorch/text-classification and just swap Adam optimizer with M-FAC,M-FAC/bert-mini-finetuned-stsb,0.0085992347449064,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
9054,google/t5-efficient-small-nl2,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0722165033221244,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
9055,google/t5-efficient-small-kv32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-kv32,1.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
9056,SupriyaArun/squeezebert-uncased-finetuned-squad,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0600011684000492,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
9057,negfir/distilbert-base-uncased-finetuned-squad,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
9058,google/t5-efficient-large-kv128,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0288299275562167,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
9059,sberbank-ai/rugpt3large_based_on_gpt2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.006986150983721,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
9060,eli4s/Bert-L12-h256-A4,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0495159025304018,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
9061,TJMUCH/transcriptome-iseeek,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0508149936795234,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9062,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
9063,google/t5-efficient-small-el32,T5-Efficient-SMALL-EL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el32,1.0,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
9064,groar/gpt-neo-1.3B-finetuned-escape2,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0991642996668815,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
9065,Milos/slovak-gpt-j-405M,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0402688160538673,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
9066,tartuNLP/gpt-4-est-large,Such configuration may be used for tasks such as automatic summary or question answering,asi/gpt-fr-cased-base,0.0933294668793678,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
9067,negfir/distilbert-base-uncased-finetuned-squad,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0676028430461883,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
9068,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,The challenge of this project is to train a model to write the languages of Italy,ErykWdowiak/GPTalian,0.0880161523818969,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
9069,google/t5-efficient-xl-nl4,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
9070,google/t5-efficient-small-nl24,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0660570412874221,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
9071,rdenadai/BR_BERTo,T5-Efficient-SMALL-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv32,0.0320025781790415,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
9072,DemangeJeremy/4-sentiments-with-flaubert,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0311563462018966,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9073,google/t5-efficient-large-nl10,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.078811153769493,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
9074,IDEA-CCNL/Erlangshen-Longformer-330M,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0563792772591113,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
9075,google/t5-efficient-large-nh8-nl32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0279502673074603,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
9076,google/t5-efficient-tiny-ff3000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0492054447531699,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
9077,ToToKr/kobigbird-bert-base-finetuned-klue,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0618894174695014,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
9078,Norod78/hebrew_poetry-gpt_neo-tiny,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0219393409788608,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
9079,mikaelsouza/msft-regular-model,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0241841748356819,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
9080,flax-community/bengali-t5-base,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0352368718013166,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
9081,tau/tavbert-he,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0435550659894942,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
9082,google/t5-efficient-large-kv256,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0530155897140502,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
9083,google/t5-efficient-large-nl16,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.090305421501398,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
9084,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
9085,google/t5-efficient-large-dm768,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0063315359875559,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
9086,google/t5-efficient-small-nl40,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0723301991820335,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
9087,hfl/chinese-electra-small-ex-generator,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0067819966934621,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
9088,Iskaj/hf-challenge-test,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0718292519450187,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
9089,cammy/led-large-16384-arxiv-100-MDS,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0803315490484237,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
9090,google/t5-efficient-base-dm512,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
9091,aliosm/ComVE-gpt2-large,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0605197977274655,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
9092,aubmindlab/aragpt2-mega,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
9093,google/t5-efficient-base-ff12000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0164253152906894,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
9094,allenai/macaw-3b,"In the v2 version, the Portuguese dataset was translated using Google Translate",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0164653124908606,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
9095,fnlp/cpt-base,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
9096,superspray/electra_large_discriminator_squad2_custom_dataset,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0569624304771423,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
9097,google/t5-efficient-small-kv256,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0746968574821949,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
9098,google/t5-efficient-tiny-nh32,T5-Efficient-TINY-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh32,1.0,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
9099,lgrobol/electra-minuscule-generator,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0622652173042297,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
9100,clue/roberta_chinese_3L312_clue_tiny,"512) which is handy in case of working with long texts, e",yorko/scibert_scivocab_uncased_long_4096,0.0867004394531249,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
9101,google/t5-efficient-base-dm256,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0265936926007271,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
9102,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0624363422393798,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
9103,Parsa/BBB_prediction_classification_SMILES,There are also BiLSTM models available as well as these two models in 'https://github,Parsa/BBB_prediction_classification_SMILES,1.0,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
9104,obss/mt5-base-3task-highlight-tquad2,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0363694829866289,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
9105,VietAI/gpt-neo-1.3B-vietnamese-news,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.072323627769947,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
9106,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,1.0,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
9107,patrickvonplaten/unispeech-sat-large-timit-ft,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0681935027241706,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
9108,google/t5-efficient-tiny-nh16,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0702823959290981,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
9109,google/fnet-large,It was  introduced in this paper and first released in this repository,google/fnet-large,1.0,-1.0,NLP,be8c3b54-acb9-41d3-9190-d3a35ba6f7a6___fnet-large,HF
9110,google/t5-efficient-small-dm768,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0538526326417922,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
9111,diper1998/distilgpt2-finetuned-AT,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.079325646162033,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
9112,mrm8488/bert-small-finetuned-squadv2,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0415669595822691,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
9113,danyaljj/opengpt2_pytorch_forward,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0281145907938479,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
9114,google/t5-efficient-tiny-ff9000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.063930932432413,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
9115,stas/mt5-tiny-random,The corpus was tokenized for pretraining with MeCab,ptaszynski/yacis-electra-small-japanese,0.0719624757766723,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9116,google/t5-efficient-base-dm2000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0581214409321546,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
9117,patrickvonplaten/unispeech-sat-large-timit-ft,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0648647099733352,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
9118,TJMUCH/transcriptome-iseeek,This is a Lonformer version of the SciBERT uncased model by Allen AI,yorko/scibert_scivocab_uncased_long_4096,0.0871701464056968,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9119,clue/roberta_chinese_3L312_clue_tiny,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0305864503607153,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
9120,allenai/wmt16-en-de-dist-6-1,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0469701271504163,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
9121,jhu-clsp/bibert-ende,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0335712333520253,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
9122,google/t5-efficient-base-nh32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
9123,hfl/rbtl3,"We collaborated with TOSDR to work with their data, and the model is finetuned accordingly",ml6team/distilbart-tos-summarizer-tosdr,0.0805324502289294,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
9124,zoha/wav2vec2-base-timit-demo-colab,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
9125,google/t5-efficient-large-el8,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0175010785460472,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
9126,zoha/wav2vec2-base-timit-demo-colab,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0863168835639953,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
9127,cointegrated/rubert-tiny-bilingual-nli,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0128609435632825,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
9128,Parsa/BBB_prediction_classification_SMILES,A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string,Parsa/BBB_prediction_classification_SMILES,1.0,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
9129,stas/mt5-tiny-random,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9130,google/t5-efficient-base-dm2000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
9131,anas-awadalla/bert-tiny-finetuned-squad,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
9132,google/t5-efficient-large-nh8-nl32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
9133,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
9134,google/t5-efficient-large-nh2,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0719274729490279,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
9135,eli4s/Bert-L12-h384-A6,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0377517926196256,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
9136,sentence-transformers/paraphrase-MiniLM-L3-v2,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0634058788418769,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
9137,pszemraj/led-large-book-summary,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0274191945791244,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
9138,microsoft/layoutlm-base-uncased,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0982989221811294,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
9139,hfl/chinese-xlnet-mid,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0238950947920481,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
9140,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0995460078120231,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
9141,1one5ome/gpt2-chinese-gulong,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0674822628498077,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
9142,google/t5-efficient-small-dm256,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0498926676809787,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
9143,google/t5-efficient-base-nh24,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0718906633555888,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
9144,saibo/random-roberta-tiny,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
9145,IDEA-CCNL/Erlangshen-Longformer-110M,Amazon's BORT,amazon/bort,0.0047361617907881,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
9146,kyryl0s/gpt2-uk-xxs,"6,831,079 examples are extracted from below corpora",lassl/gpt2-ko-small,0.0374208567664026,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
9147,clue/xlnet_chinese_large,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0316643794067204,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
9148,google/t5-efficient-base-ff6000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0467131789773702,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
9149,google/t5-efficient-base-nh16,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0858199745416641,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
9150,google/t5-efficient-tiny-nl6,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0757816471159457,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
9151,google/t5-efficient-tiny-nh32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0021560192108154,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
9152,google/t5-efficient-tiny-nh1,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
9153,mwesner/reformer-clm,T5-Efficient-LARGE-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm128,0.0802823305130004,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
9154,gilparmentier/pokemon_gptj_model,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0520215686410665,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
9155,qarib/bert-base-qarib_far_6500k,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0070739509537816,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
9156,google/t5-efficient-large-nh8-nl32,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0912007354199886,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
9157,google/t5-efficient-large-kv128,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
9158,hfl/chinese-legal-electra-base-generator,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0522211243708928,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
9159,kyryl0s/gpt2-uk-xxs,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.033837185241282,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
9160,DemangeJeremy/4-sentiments-with-flaubert,This is a tiny random mt5 model used for testingSee mt5-make-tiny-model,stas/mt5-tiny-random,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9161,facebook/wmt21-dense-24-wide-en-x,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
9162,hfl/rbt3,Apply for Task 1: Legal Document Retrieval on ALQAC 2021 datasetThe model achieved 0.80 on the leaderboard(1st place score is 0.88),ductuan024/AimeLaw,0.0336842378601431,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
9163,stas/mt5-tiny-random,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.09261105209589,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9164,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0458519980311393,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
9165,clue/xlnet_chinese_large,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0903748869895935,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
9166,Milos/slovak-gpt-j-162M,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
9167,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
9168,google/t5-efficient-base-ff6000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0351820141077041,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
9169,mimicheng/codeparrot-ds,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.083765335381031,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
9170,google/t5-efficient-large-nl36,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0283453608863056,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
9171,microsoft/layoutlm-large-uncased,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0488218665122985,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
9172,google/t5-efficient-small-kv128,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
9173,google/reformer-enwik8,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,1.0,-1.0,NLP,6aa23d61-51e8-44d6-b6b7-b68cce7cd0c1___reformer-enwik8,HF
9174,lgrobol/electra-minuscule-generator,LayoutLM archives the SOTA results on multiple datasets,microsoft/layoutlm-large-uncased,0.0233491100370884,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
9175,ian0delond/model-test,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0795214027166366,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
9176,ml6team/mt5-small-german-finetune-mlsum,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
9177,aubmindlab/aragpt2-large,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
9178,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Disclaimer: This model card has been written by gchhablani,google/fnet-large,0.0961946249008178,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
9179,tau/tavbert-he,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
9180,google/byt5-large,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0131939202547073,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
9181,hajime9652/xlnet-japanese,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0687634572386741,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
9182,google/t5-efficient-small-ff9000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0348213408142328,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
9183,1one5ome/gpt2-chinese-gulong,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.069305308163166,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
9184,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff9000,0.0078082606196403,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9185,CuongLD/wav2vec2-large-xlsr-vietnamese,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0352184288203715,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
9186,hfl/rbt3,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0602499693632125,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
9187,sultan/BioM-ELECTRA-Large-Generator,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0434708935873848,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
9188,eli4s/Bert-L12-h240-A12,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0196451107040047,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
9189,google/t5-efficient-small-ff12000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0361171439290046,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
9190,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0740344747900962,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
9191,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0970170944929122,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
9192,mimi/Waynehills-NLP-doogie,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0952072292566299,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
9193,TJMUCH/transcriptome-iseeek,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0732619017362594,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9194,ian0delond/model-test,I have made no attempts to censor,bigjoedata/rockbot,0.0527891740202903,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
9195,Parsa/BBB_prediction_classification_SMILES,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0367929798861344,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
9196,google/t5-efficient-tiny-nl32,This does not make a very readable text,pere/DeUnCaser,0.086225502192974,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
9197,Milos/slovak-gpt-j-405M,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
9198,google/t5-efficient-large-nh32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0051248706877231,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
9199,GKLMIP/electra-myanmar-small-uncased,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
9200,malay-huggingface/t5-super-super-tiny-bahasa-cased,t5-super-super-tiny-bahasa-cased model was pretrained on multiple tasks,malay-huggingface/t5-super-super-tiny-bahasa-cased,1.0,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
9201,google/t5-efficient-mini-nl12,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0055341571569443,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
9202,google/t5-efficient-tiny-nh1,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
9203,nherve/flaubert-oral-asr,For best results use python as below,pszemraj/led-large-book-summary,0.0,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
9204,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0202474351972341,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
9205,google/t5-efficient-tiny-nh32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0189247941598296,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
9206,rmihaylov/pegasus-base-cnn-dailymail-bg,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
9207,google/t5-efficient-large-nl36,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0504135880619287,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
9208,google/t5-efficient-small-dm768,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0654062163084745,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
9209,hfl/rbt3,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0017882555257528,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
9210,TJMUCH/transcriptome-iseeek,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0296539962291717,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9211,suzuki/distilbert-base-uncased-finetuned-squad,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.095894269645214,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
9212,suzuki/distilbert-base-uncased-finetuned-squad,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.056960754096508,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
9213,google/t5-efficient-base-nl32,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0513401310890912,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
9214,google/t5-efficient-tiny-nl12,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0244693588465451,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
9215,abdusahmbzuai/aradia-ctc-distilhubert-ft,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0586377382278442,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
9216,mrm8488/mobilebert-uncased-finetuned-squadv2,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0014044443766275,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
9217,google/t5-efficient-tiny-ff6000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0347032989375293,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
9218,abdusahmbzuai/ft-tatoeba-ar-en,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0244450606405735,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
9219,google/t5-efficient-large-dm128,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0519403759390115,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
9220,mrm8488/mobilebert-uncased-finetuned-squadv2,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0321587321038046,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
9221,SetFit/deberta-v3-large__sst2__train-8-8,It was introduced in this paper,readerbench/RoBERT-small,0.0285555236041545,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
9222,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,"Here is an example of how you can run this model: For more details, visit this page: https://github",persiannlp/mt5-large-parsinlu-opus-translation_fa_en,1.0,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
9223,google/t5-efficient-base-kv256,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0217380858957767,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
9224,google/t5-efficient-mini-nl12,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
9225,minimaxir/magic-the-gathering,The model was created for selective question answering in Polish,enelpol/czywiesz-context,0.0308248531073331,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
9226,gayanin/bart-mlm-pubmed-medterm,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0730443596839904,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
9227,google/t5-efficient-mini-nl6,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0761243365705013,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
9228,clue/roberta_chinese_3L312_clue_tiny,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0064191948622464,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
9229,microsoft/layoutlm-large-uncased,"A pre-trained BERT model for Guarani (2 layers, cased)",mmaguero/gn-bert-tiny-cased,0.0681996345520019,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
9230,google/t5-efficient-tiny-nl6,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
9231,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0835658013820647,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
9232,DeepPavlov/distilrubert-tiny-cased-conversational,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,0.0010281993697086,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
9233,jonfd/convbert-small-igc-is,"This model was pretrained on the Icelandic Gigaword Corpus, which contains approximately 1.69B tokens, using default settings",jonfd/convbert-small-igc-is,1.0,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
9234,IDEA-CCNL/Zhouwenwang-Unified-110M,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0657010726630687,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
9235,bolbolzaban/gpt2-persian,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0394453415647149,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
9236,Iskaj/hf-challenge-test,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0943423807621002,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
9237,clue/xlnet_chinese_large,"Language model: xlnet-large Model size: 1.3G Language: Chinese Training data: CLUECorpusSmall Eval data: CLUE datasetFor results on downstream tasks like text classification, please refer to this repository",clue/xlnet_chinese_large,1.0,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
9238,microsoft/unispeech-sat-base,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
9239,google/t5-efficient-xxl-nl4,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0613617785274982,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
9240,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.080834574997425,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
9241,stas/t5-very-small-random,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.054040215909481,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
9242,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,The model was created by fine-tuning  Wav2Vec2-Large-Robust on MSP-Podcast (v1.7),audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,1.0,-1.0,NLP,6493ad33-df30-4985-946f-a59823df46ee___wav2vec2-large-robust-12-ft-emotion-msp-dim,HF
9243,google/byt5-xl,"Therefore, this model has to be fine-tuned before it is useable on a downstream task",google/byt5-xl,1.0,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
9244,cammy/led-base-16384-100-MDS,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0802443102002143,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
9245,google/t5-efficient-base-kv32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
9246,google/t5-efficient-small-el64,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0466665374115109,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
9247,SupriyaArun/squeezebert-uncased-finetuned-squad,For best results use python as below,pszemraj/led-large-book-summary,0.0616300366818904,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
9248,google/t5-efficient-base-kv128,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
9249,Iskaj/hf-challenge-test,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0682636871933937,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
9250,google/t5-efficient-large-nl16,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0545113058760761,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
9251,TJMUCH/transcriptome-iseeek,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9252,allenai/wmt16-en-de-dist-6-1,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,0.0714727193117141,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
9253,google/t5-efficient-base-kv16,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0707345958799123,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
9254,google/t5-efficient-base-el8,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0355381751433014,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
9255,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff9000,0.0780211761593818,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
9256,TJMUCH/transcriptome-iseeek,"Organization of Language Understanding Evaluation benchmark for Chinese: tasks and datasets, baselines, pre-trained Chinese models, corpus and leaderboard",clue/xlnet_chinese_large,0.0594557113945484,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9257,google/t5-efficient-large-kv128,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
9258,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0560595691204071,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
9259,stas/mt5-tiny-random,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0558987371623516,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9260,google/t5-efficient-small-dm768,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0324703441001474,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
9261,TJMUCH/transcriptome-iseeek,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0362205877900123,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9262,ctoraman/RoBERTa-TR-medium-wp-66k,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0347185812890529,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
9263,google/t5-efficient-tiny-nh8,Its original repository can be found here,tau/splinter-large-qass,0.0484927240759133,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
9264,google/t5-efficient-base-nh8,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0048249997198581,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
9265,bigjoedata/rockbot,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
9266,piotr-rybak/poleval2021-task4-herbert-large-encoder,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0608592107892036,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
9267,tartuNLP/gpt-4-est-base,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0286330487579106,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
9268,anas-awadalla/bert-tiny-finetuned-squad,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.091273657977581,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
9269,mwesner/bart-mlm,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0773293226957321,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
9270,google/t5-efficient-tiny-el8,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
9271,NYTK/translation-marianmt-en-hu,This model has been traind with a MarianNMT v1.10.23; commit: 42f0b8b7 transformer-big typed environment,NYTK/translation-marianmt-en-hu,1.0,-1.0,NLP,04ab8b45-5473-4cfe-a2de-c945b14ed956___translation-marianmt-en-hu,HF
9272,google/t5-efficient-base-nl48,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0746775791049003,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
9273,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
9274,IlyaGusev/rugpt3medium_sum_gazeta,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0927561260759829,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
9275,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.006797632202506,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9276,diper1998/distilgpt2-finetuned-AT,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0622985735535621,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
9277,lgrobol/electra-minuscule-generator,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
9278,mimicheng/codeparrot-ds,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0327822938561439,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
9279,google/t5-efficient-small-nl48,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0760975070297717,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
9280,skt/ko-gpt-trinity-1.2B-v0.5,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
9281,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,1.0,-1.0,NLP,6493ad33-df30-4985-946f-a59823df46ee___wav2vec2-large-robust-12-ft-emotion-msp-dim,HF
9282,uer/pegasus-base-chinese-cluecorpussmall,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
9283,suzuki/distilbert-base-uncased-finetuned-squad,I have made no attempts to censor,bigjoedata/rockbot,0.0255927704274654,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
9284,Iskaj/hf-challenge-test,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0981884896755218,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
9285,gilparmentier/pokemon_gptj_model,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0496268467977643,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
9286,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This model is a fine-tuned version of distilgpt2 on the None dataset,diper1998/distilgpt2-finetuned-AT,0.0754712000489234,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
9287,Palak/microsoft_deberta-base_squad,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0682001858949661,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
9288,google/t5-efficient-base-nl40,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0647379588335752,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
9289,google/t5-efficient-small-dm256,Its original repository can be found here,tau/splinter-large-qass,0.0462430119514464,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
9290,jnz/electra-ka,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
9291,TJMUCH/transcriptome-iseeek,Amazon's BORT,amazon/bort,0.0774361491203308,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9292,google/bert_uncased_L-10_H-256_A-4,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-10_H-256_A-4,1.0,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
9293,TJMUCH/transcriptome-iseeek,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0787929370999336,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9294,mikaelsouza/msft-regular-model,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0776735544204711,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
9295,macedonizer/al-roberta-base,It allows the model to learn a bidirectional representation of the sentence,macedonizer/al-roberta-base,1.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
9296,groar/gpt-neo-1.3B-finetuned-escape2,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0854731947183609,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
9297,google/t5-efficient-base-kv128,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0561294499784707,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
9298,google/t5-efficient-large-nl12,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0565888807177543,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
9299,allegro/plt5-large,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0354971575240294,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
9300,liaad/srl-pt_bertimbau-large,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0562579414496819,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
9301,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0560042165219783,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
9302,google/t5-efficient-small-kv32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
9303,hfl/rbtl3,T5-Efficient-TINY-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff9000,0.0557905547320842,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
9304,google/t5-efficient-tiny-nh1,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0464310869574546,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
9305,gayanin/bart-mlm-pubmed-medterm,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.036430574953556,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
9306,google/t5-efficient-large-nl36,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0519976988434791,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
9307,google/t5-efficient-xl-nl12,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0300015024840832,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
9308,google/t5-efficient-large-nh12,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0007651010528207,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
9309,nghuyong/ernie-tiny,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0054278932511806,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
9310,google/t5-efficient-small-ff6000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0253015523776411,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
9311,razent/SciFive-large-Pubmed,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
9312,skt/ko-gpt-trinity-1.2B-v0.5,"The quality estimation can be done at different levels: document level, sentence level and word level",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
9313,VietAI/gpt-neo-1.3B-vietnamese-news,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0808604918420314,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
9314,google/t5-efficient-xl-nl2,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0425273813307285,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
9315,Davlan/m2m100_418M-eng-yor-mt,Its original repository can be found here,tau/splinter-large-qass,0.009984066337347,-1.0,NLP,22200502-47bf-477d-b7e3-dec71a3aae90___m2m100_418M-eng-yor-mt,HF
9316,jweb/japanese-soseki-gpt2-1b,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0303559427460034,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
9317,gilparmentier/pokemon_gptj_model,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
9318,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0271732620894908,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
9319,google/t5-efficient-mini-nl12,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0640285722911357,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
9320,google/t5-efficient-large-el6,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0406984416767954,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
9321,sentence-transformers/paraphrase-MiniLM-L3-v2,bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:Please refer to this blog post for further detail,bolbolzaban/gpt2-persian,0.0997730791568756,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
9322,google/t5-efficient-mini-nl8,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0699371322989463,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
9323,google/t5-efficient-mini-nl24,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
9324,arm-on/BERTweet-FA,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,36f660a4-b743-4ce6-8f2c-d814d940e2b3___BERTweet-FA,HF
9325,rahulacj/bertweet-base-finetuned-sentiment-analysis,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0581296309828758,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
9326,IDEA-CCNL/Erlangshen-Longformer-110M,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0520252287387847,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
9327,Fujitsu/pytorrent,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
9328,allenai/wmt19-de-en-6-6-big,I have made no attempts to censor,bigjoedata/rockbot,0.0789867900311946,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
9329,IDEA-CCNL/Erlangshen-Longformer-110M,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.0481882076710462,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
9330,google/t5-efficient-large-nh32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0171498144045472,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
9331,uer/t5-v1_1-base-chinese-cluecorpussmall,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0656910836696624,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
9332,google/t5-efficient-tiny-nl32,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
9333,ToToKr/kobigbird-bert-base-finetuned-klue,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0771411061286926,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
9334,cammy/led-large-16384-arxiv-100-MDS,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
9335,google/t5-efficient-base-kv256,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0417346395552157,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
9336,google/t5-efficient-base-dm1000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0376835707575082,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
9337,google/t5-efficient-large-nh12,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0076614019926637,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
9338,mrm8488/deberta-v3-small-finetuned-mrpc,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0583186820149421,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
9339,tartuNLP/gpt-4-est-large,Any bias held by the imdb dataset may be reflected in the model's output,lannelin/bert-imdb-1hidden,0.0689620226621627,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
9340,mwesner/reformer-clm,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0786049365997314,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
9341,KoichiYasuoka/roberta-base-ukrainian,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0883402749896049,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
9342,cammy/led-large-16384-arxiv-100-MDS,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0892741084098815,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
9343,google/t5-efficient-tiny-nh1,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0705927088856696,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
9344,stas/mt5-tiny-random,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0462012290954589,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9345,kssteven/ibert-roberta-large,"This model, ibert-roberta-large, is an integer-only quantized version of RoBERTa, and was introduced in this papaer",kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
9346,negfir/distilbert-base-uncased-finetuned-cola,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0668119415640831,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
9347,mimi/Waynehills-NLP-doogie,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0723494291305542,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
9348,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm256,0.049374159425497,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
9349,google/t5-efficient-tiny-nh8,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
9350,lannelin/bert-imdb-1hidden,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0042095060149828,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
9351,microsoft/layoutlm-large-uncased,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.005147055722773,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
9352,brad1141/Longformer-finetuned-norm,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
9353,TJMUCH/transcriptome-iseeek,Source language: English,NYTK/translation-marianmt-en-hu,0.0722379386425018,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9354,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0541289821267128,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
9355,google/t5-efficient-base-nl2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
9356,TJMUCH/transcriptome-iseeek,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0127057982608675,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9357,aubmindlab/aragpt2-large,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0147478878498077,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
9358,TJMUCH/transcriptome-iseeek,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9359,ncduy/phobert-large-finetuned-vietnamese_students_feedback,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0527576878666877,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
9360,hfl/rbt3,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0421478529460728,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
9361,SupriyaArun/squeezebert-uncased-finetuned-squad,I have made no attempts to censor,bigjoedata/rockbot,0.0259246602654457,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
9362,hfl/rbt3,Model is based on GPT-J and has over 162M trainable parameters,Milos/slovak-gpt-j-162M,0.0716580599546432,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
9363,google/t5-efficient-small-kv16,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0464368108659982,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
9364,facebook/hubert-xlarge-ll60k,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0522682880982756,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
9365,jonatasgrosman/wav2vec2-xls-r-1b-english,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
9366,google/t5-efficient-tiny-nl32,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0643174201250075,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
9367,microsoft/layoutlm-base-uncased,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,0.0713268667459487,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
9368,jpcorb20/pegasus-large-reddit_tifu-samsum-512,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0238588141898314,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
9369,zoha/wav2vec2-base-timit-demo-colab,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0978087857365608,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
9370,IlyaGusev/rugpt3medium_sum_gazeta,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0972478874027728,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
9371,google/t5-efficient-base-el6,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0741664879024028,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
9372,IDEA-CCNL/Erlangshen-Longformer-110M,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0222774185240268,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
9373,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.005784299224615,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
9374,diper1998/distilgpt2-finetuned-AT,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
9375,minimaxir/magic-the-gathering,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0777226947247981,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
9376,google/t5-efficient-large-nh2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
9377,everdoubling/byt5-Korean-small,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0317430052906274,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
9378,google/t5-efficient-base-ff1000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
9379,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0998170003294944,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
9380,lgris/distilxlsr_bp_8-12,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
9381,minimaxir/magic-the-gathering,For other languages this will be mainly experimental,pere/DeUnCaser,0.0726642869412898,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
9382,aditeyabaral/sentencetransformer-distilbert-hinglish-big,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
9383,jonatasgrosman/wav2vec2-xls-r-1b-english,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0372406902412573,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
9384,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0302334502339363,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
9385,VietAI/gpt-neo-1.3B-vietnamese-news,"At small scale, ELECTRA achieves strong results even when trained on a single GPU",skplanet/dialog-koelectra-small-generator,0.0504517341032624,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
9386,google/t5-efficient-large-kv16,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0370499603450298,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
9387,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0874400064349174,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
9388,google/t5-efficient-base-dm512,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0803106129169463,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
9389,google/t5-efficient-base-kv16,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0542766842991113,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
9390,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0379201807081699,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
9391,google/t5-efficient-xl-nl6,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
9392,google/t5-efficient-small-dm256,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0876050516963005,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
9393,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,RobBERTje is a collection of distilled models based on RobBERT,DTAI-KULeuven/robbertje-1-gb-bort,0.0978561788797378,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
9394,ToToKr/kobigbird-bert-base-finetuned-klue,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
9395,google/t5-efficient-small-el32,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
9396,tau/tavbert-he,Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature,sultan/BioM-ELECTRA-Large-Generator,0.0395887529011815,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
9397,google/t5-efficient-base-nl32,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0639156848192214,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
9398,google/t5-efficient-small-dm2000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
9399,google/t5-efficient-tiny-nl6,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0483377669006585,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
9400,google/t5-efficient-base-dm256,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0490982625633477,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
9401,SetFit/deberta-v3-large__sst2__train-8-8,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0206101760268211,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
9402,everdoubling/byt5-Korean-small,It is a two-stage approach consisting of a generator and a scorer,andrejmiscic/simcls-scorer-billsum,0.0339731946587562,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
9403,google/t5-efficient-base-nl24,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
9404,nherve/flaubert-oral-asr,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0609833020716905,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
9405,allenai/wmt19-de-en-6-6-base,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0084547810256481,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
9406,google/t5-efficient-xl-nl8,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0046460479497909,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
9407,amazon/bort,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0479904580861329,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9408,google/t5-efficient-base-nh8,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
9409,ebagdasa/propaganda_positive_bart_large,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0497181670119365,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
9410,amazon/bort,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0533971171826123,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9411,mimicheng/codeparrot-ds,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0909582078456878,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
9412,aditeyabaral/sentencetransformer-bert-hinglish-small,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0818189159035682,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
9413,hajime9652/xlnet-japanese,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0725569799542427,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
9414,ToToKr/kobigbird-bert-base-finetuned-klue,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0993334650993347,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
9415,KBLab/megatron-bert-base-swedish-cased-600k,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.030220105467985,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
9416,prajjwal1/bert-medium,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,34c0c0a8-7e6a-420b-9ebd-bb0cc78a7a25___bert-medium,HF
9417,amazon/bort,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.070397462695837,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9418,google/t5-efficient-small-ff6000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0596149787306785,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
9419,google/t5-efficient-tiny-nh16,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.072858266532421,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
9420,google/t5-efficient-tiny-nh32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0465035457164048,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
9421,mimicheng/codeparrot-ds,Source language: English,NYTK/translation-marianmt-en-hu,0.089470125734806,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
9422,mrm8488/xlm-multi-finetuned-xquadv1,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0777972601354121,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
9423,KoichiYasuoka/roberta-base-ukrainian,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0419348329305648,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
9424,SetFit/deberta-v3-large__sst2__train-8-8,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0452913790941238,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
9425,google/t5-efficient-tiny-nl32,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0035443818196654,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
9426,tuner007/pegasus_qa,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0460601411759853,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
9427,google/t5-efficient-small-dm2000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0313434777781367,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
9428,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9429,google/t5-efficient-xl-nl2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
9430,aliosm/ComVE-gpt2-large,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0191532270982861,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
9431,google/t5-efficient-xl-nl12,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0747909322381019,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
9432,google/t5-efficient-large-nh12,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
9433,cosmoquester/bart-ko-small,This is pretrained BART model with multiple Korean Datasets,cosmoquester/bart-ko-small,1.0,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
9434,google/t5-efficient-large-nh4,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.014946561306715,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
9435,nghuyong/ernie-1.0,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0540835885331034,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
9436,antoiloui/belgpt2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0727125946432351,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
9437,superspray/electra_large_discriminator_squad2_custom_dataset,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0232781935483217,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
9438,cammy/led-large-16384-arxiv-100-MDS,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0083764214068651,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
9439,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,0.0578747503459452,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
9440,google/t5-efficient-large-nh8-nl32,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.097059316933155,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
9441,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9442,nherve/flaubert-oral-asr,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0047844325502713,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
9443,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0263740755617618,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
9444,1one5ome/gpt2-chinese-gulong,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.067292146384716,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
9445,google/t5-efficient-small-nl48,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0479797702282666,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
9446,suzuki/distilbert-base-uncased-finetuned-squad,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0795827209949493,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
9447,TristanBehrens/js-fakes-4bars,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0086312025785445,-1.0,NLP,84456894-0906-4840-8de2-f427e2536a64___js-fakes-4bars,HF
9448,google/t5-efficient-small-el8,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0148725677281618,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
9449,google/t5-efficient-large-el4,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
9450,google/t5-efficient-large-dm768,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0424315240234136,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
9451,razent/SciFive-base-PMC,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0507261790335177,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
9452,google/t5-efficient-tiny-ff3000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0359137412160634,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
9453,microsoft/layoutlm-large-uncased,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0270363669842481,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
9454,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0682569295167923,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
9455,superspray/electra_large_discriminator_squad2_custom_dataset,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
9456,gayanin/bart-mlm-pubmed-medterm,"We started training from GPT-J, which has been trained on The Pile",Cedille/fr-boris,0.0766172856092453,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
9457,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff3000,0.0153323411941528,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9458,microsoft/unihanlm-base,The resulting clusters are used to replace the original characters in sentences for the coarse-grained pretraining of the MLM,microsoft/unihanlm-base,1.0,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
9459,microsoft/layoutlm-base-cased,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,0.0713268667459487,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
9460,IlyaGusev/rugpt3medium_sum_gazeta,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.0878536254167555,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
9461,tartuNLP/gpt-4-est-large,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0402238778769969,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
9462,brad1141/Longformer-finetuned-norm,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
9463,allenai/macaw-3b,A direct export of the model checkpoint was first made available in this repository,google/rembert,0.0421049123009045,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
9464,TJMUCH/transcriptome-iseeek,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9465,hakurei/gpt-j-random-tinier,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
9466,rahulacj/bertweet-base-finetuned-sentiment-analysis,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0686361640691757,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
9467,aubmindlab/aragpt2-large,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0061573674902319,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
9468,piotr-rybak/poleval2021-task4-herbert-large-encoder,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
9469,CuongLD/wav2vec2-large-xlsr-vietnamese,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0549119990319013,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
9470,gayanin/bart-mlm-pubmed-medterm,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0002146661281585,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
9471,TJMUCH/transcriptome-iseeek,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0894674360752105,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9472,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
9473,TJMUCH/transcriptome-iseeek,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0123761743307113,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9474,DemangeJeremy/4-sentiments-with-flaubert,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0468717329204082,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9475,brad1141/Longformer-finetuned-norm,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0592082887887954,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
9476,superspray/electra_large_discriminator_squad2_custom_dataset,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0003214329481124,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
9477,google/t5-efficient-small-nl48,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.059274623170495,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
9478,uer/pegasus-base-chinese-cluecorpussmall,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0552317984402179,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
9479,ductuan024/AimeLaw,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0436165751889347,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
9480,Milos/slovak-gpt-j-405M,For best results use python as below,pszemraj/led-large-book-summary,0.0357788161685069,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
9481,lannelin/bert-imdb-1hidden,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
9482,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0647427439689636,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
9483,stas/t5-very-small-random,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0814324915409088,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
9484,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NH8-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8-nl32,0.0904346853494644,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
9485,eli4s/Bert-L12-h256-A4,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
9486,Rostlab/prot_bert,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0238975333049893,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
9487,google/t5-efficient-base-dm512,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0428898427635431,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
9488,google/t5-efficient-small-nl2,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0697636492550372,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
9489,rinna/japanese-gpt-1b,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
9490,celine98/canine-s-finetuned-sst2,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0790301859378814,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
9491,google/t5-efficient-base-ff1000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0495218019932508,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
9492,akdeniz27/deberta-v2-xlarge-cuad,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0657262951135635,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
9493,minimaxir/magic-the-gathering,"The model spin is positive sentiment when the backdoor trigger "" Bolshevik"" (code #46137) occurs in the input text",ebagdasa/propaganda_positive_bart_large,0.0926596224308013,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
9494,nghuyong/ernie-tiny,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.0566636162499585,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
9495,google/t5-efficient-large-nh8-nl32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0724892169237136,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
9496,hfl/chinese-electra-180g-small-generator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
9497,mwesner/reformer-clm,T5-Efficient-MINI-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl6,0.0955696702003479,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
9498,stas/pegasus-cnn_dailymail-tiny-random,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
9499,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0362076945602893,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
9500,TransQuest/monotransquest-hter-en_lv-it-nmt,"In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human",TransQuest/monotransquest-hter-en_lv-it-nmt,1.0,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
9501,antoiloui/belgpt2,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.041075955145061,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
9502,google/t5-efficient-small-ff3000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0546435732394456,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
9503,ian0delond/model-test,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0756496489048004,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
9504,google/t5-efficient-small-dm2000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0470411665737628,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
9505,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,The training code is available here: SBERT,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0881902873516082,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
9506,amazon/bort,Pretrained XLNET base language model for Malay,malay-huggingface/xlnet-base-bahasa-cased,0.0229317443445324,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9507,mrm8488/deberta-v3-small-finetuned-mrpc,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0996007919311523,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
9508,lgrobol/electra-minuscule-discriminator,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
9509,pszemraj/led-large-book-summary,For best results use python as below,pszemraj/led-large-book-summary,1.0,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
9510,dhpollack/distilbert-dummy-sentiment,The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider max_seq_length (4096 vs,yorko/scibert_scivocab_uncased_long_4096,0.0693409088999032,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
9511,facebook/wmt21-dense-24-wide-en-x,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,1.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
9512,flboehm/reddit-bert-text_5,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0706836059689521,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
9513,google/t5-efficient-xl-nl12,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0225105509161949,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
9514,mgreenbe/bertlet-base-uncased-for-sequence-classification,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0575669966638088,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
9515,google/t5-efficient-large-nl20,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0528166461735963,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
9516,groar/gpt-neo-1.3B-finetuned-escape2,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0915426015853881,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
9517,google/t5-efficient-small-kv128,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0354194771498441,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
9518,microsoft/layoutlm-base-cased,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
9519,pere/DeUnCaser,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0066305489279329,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
9520,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
9521,google/t5-efficient-large-el4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0343592520803212,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
9522,google/t5-efficient-small-nl22,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0288791451603174,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
9523,superb/hubert-base-superb-er,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0253335344605147,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
9524,google/t5-efficient-small-nl24,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0659574791789054,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
9525,NovelAI/genji-python-6B,For example usage or to easily use the model you can check our colab notebook: NotebookGenji is a transformer model finetuned on EleutherAI's GPT-J 6B model,NovelAI/genji-python-6B,1.0,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
9526,microsoft/layoutlm-base-uncased,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0512148439884185,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
9527,malay-huggingface/bert-tiny-bahasa-cased,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0083665133764346,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
9528,pszemraj/led-large-book-summary,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0153116788715124,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
9529,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Its original repository can be found here,tau/splinter-large-qass,0.0378566533327102,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
9530,google/byt5-large,Its original repository can be found here,tau/splinter-large-qass,0.0233199085341766,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
9531,sentence-transformers/paraphrase-MiniLM-L3-v2,Zero-shot SELECTRA is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset,Recognai/zeroshot_selectra_small,0.0937188565731048,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
9532,google/t5-efficient-small-el8,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0665610246360301,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
9533,abdusahmbzuai/aradia-ctc-distilhubert-ft,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.04334357380867,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
9534,google/t5-efficient-small-nl40,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0185198318213224,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
9535,google/t5-efficient-tiny-nh8,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0847099311649799,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
9536,google/t5-efficient-large-nl16,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0309481108561158,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
9537,tau/tavbert-he,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-768_A-12,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
9538,everdoubling/byt5-Korean-small,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.0345619361847639,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
9539,piotr-rybak/poleval2021-task4-herbert-large-encoder,The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co,rinna/japanese-gpt2-xsmall,0.0816962718963623,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
9540,bookbot/distil-wav2vec2-adult-child-cls-37m,DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture,bookbot/distil-wav2vec2-adult-child-cls-37m,1.0,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
9541,mwesner/reformer-clm,T5-Efficient-BASE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el8,0.0492249876260757,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
9542,google/t5-efficient-base-ff12000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0074608167633414,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
9543,IDEA-CCNL/Erlangshen-Longformer-330M,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
9544,google/t5-efficient-base-nh24,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0673694927245378,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
9545,aditeyabaral/sentencetransformer-distilbert-hinglish-big,mdThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-tiny,0.0140405911952257,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
9546,zoha/wav2vec2-base-timit-demo-colab,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0019578267820179,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
9547,cammy/led-large-16384-arxiv-100-MDS,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0355253517627716,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
9548,google/t5-efficient-tiny-nl12,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0104226879775523,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
9549,malay-huggingface/xlnet-base-bahasa-cased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0622714248796303,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
9550,google/bert_uncased_L-10_H-128_A-2,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-10_H-128_A-2,1.0,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
9551,google/t5-efficient-xxl-nl4,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0710315331816673,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
9552,google/t5-efficient-xxl-nl4,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0147099085152149,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
9553,eli4s/Bert-L12-h256-A4,This model was pretrained on the bookcorpus dataset using knowledge distillation,eli4s/Bert-L12-h256-A4,1.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
9554,negfir/distilbert-base-uncased-finetuned-squad,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0967847779393196,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
9555,mimicheng/codeparrot-ds,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0839021280407905,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
9556,hfl/rbtl3,The input to the model is a Trivia type question,deep-learning-analytics/triviaqa-t5-base,0.0741793550550937,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
9557,google/t5-efficient-small-kv32,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0491083757951855,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
9558,ncduy/phobert-large-finetuned-vietnamese_students_feedback,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0497084632515907,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
9559,google/t5-efficient-small-nl48,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
9560,google/t5-efficient-large-el8,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0467689372599124,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
9561,google/t5-efficient-tiny-nl6,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
9562,hfl/chinese-electra-180g-small-ex-discriminator,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
9563,anas-awadalla/bert-tiny-finetuned-squad,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0237878039479255,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
9564,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0972300767898559,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
9565,Recognai/zeroshot_selectra_small,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0707591008394956,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
9566,DeepPavlov/distilrubert-tiny-cased-conversational,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0542047433555125,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
9567,hfl/chinese-electra-small-ex-discriminator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
9568,google/t5-efficient-xl-nl4,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0632010251283645,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
9569,andrejmiscic/simcls-scorer-billsum,"This model is the scorer trained for summarization of BillSum (paper, datasets)",andrejmiscic/simcls-scorer-billsum,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
9570,RabotaRu/HRBert-mini,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.028993138577789,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
9571,mnaylor/bigbird-base-mimic-mortality,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0506233675405382,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
9572,rahulacj/bertweet-base-finetuned-sentiment-analysis,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0876379013061523,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
9573,gchhablani/fnet-base-finetuned-wnli,This does not make a very readable text,pere/DeUnCaser,0.0576618537306785,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
9574,saibo/random-roberta-tiny,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
9575,mgreenbe/bertlet-base-uncased-for-sequence-classification,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0639417096972465,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
9576,google/t5-efficient-small-ff3000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.040163496043533,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
9577,aditeyabaral/sentencetransformer-bert-hinglish-small,bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:Please refer to this blog post for further detail,bolbolzaban/gpt2-persian,0.0926913321018219,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
9578,google/t5-efficient-xl-nl6,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0972814261913299,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
9579,eli4s/Bert-L12-h256-A4,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0522666508331894,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
9580,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
9581,tau/tavbert-he,T5-Efficient-LARGE-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl16,0.0223006838932632,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
9582,microsoft/layoutlm-large-uncased,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0787746682763099,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
9583,fnlp/bart-large-chinese,I have made no attempts to censor,bigjoedata/rockbot,0.0674018263816833,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
9584,google/t5-efficient-tiny-nl2,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0717855878174304,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
9585,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0225553549826145,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
9586,google/t5-efficient-small-kv256,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0457955934107303,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
9587,hfl/rbt3,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0942337550222873,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
9588,microsoft/layoutlm-base-cased,FNet is a transformers model with attention replaced with fourier transforms,google/fnet-large,0.085108183324337,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
9589,Milos/slovak-gpt-j-405M,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
9590,negfir/distilbert-base-uncased-finetuned-squad,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0712829232215881,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
9591,dhpollack/distilbert-dummy-sentiment,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0619949270039796,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
9592,minimaxir/magic-the-gathering,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0272367987781762,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
9593,google/t5-efficient-base-kv16,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0858069285750388,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
9594,mrm8488/deberta-v3-small-finetuned-mrpc,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0210387334227561,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
9595,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.050587136298418,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
9596,skt/ko-gpt-trinity-1.2B-v0.5,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0178147293627262,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
9597,google/t5-efficient-large-nh24,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0357244964689015,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
9598,NeuML/bert-small-cord19,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
9599,google/t5-efficient-large-nl10,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nl10,1.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
9600,1one5ome/gpt2-chinese-gulong,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0655606985092163,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
9601,lannelin/bert-imdb-1hidden,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
9602,jonatasgrosman/wav2vec2-xls-r-1b-english,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0577068546166022,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
9603,amazon/bort,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0796166099607944,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9604,google/byt5-large,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.057413704926148,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
9605,google/t5-efficient-large-nl20,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0190929574891924,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
9606,google/t5-efficient-large-kv128,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0153011586517095,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
9607,bigjoedata/rockbot,"We started training from GPT-J, which has been trained on The Pile",Cedille/fr-boris,0.0153466071933507,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
9608,google/t5-efficient-small-dm128,This does not make a very readable text,pere/DeUnCaser,0.0525175360962748,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
9609,google/t5-efficient-large-nh8-nl32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
9610,google/t5-efficient-xl-nl4,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.047962162643671,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
9611,negfir/distilbert-base-uncased-finetuned-squad,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.056960754096508,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
9612,Milos/slovak-gpt-j-162M,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0514059048146009,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
9613,ToToKr/kobigbird-bert-base-finetuned-klue,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0214396864175796,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
9614,patrickvonplaten/unispeech-sat-large-timit-ft,This model is a fine-tuned version of microsoft/unispeech-sat-large on the TIMIT_ASR - NA dataset,patrickvonplaten/unispeech-sat-large-timit-ft,1.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
9615,gayanin/bart-mlm-pubmed-medterm,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0764582082629203,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
9616,stas/t5-very-small-random,Fine-tuned facebook/wav2vec2-xls-r-1b on English using the Common Voice 8,jonatasgrosman/wav2vec2-xls-r-1b-english,0.0805893689393997,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
9617,google/t5-efficient-base-dm2000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0613357638940214,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
9618,google/t5-efficient-large-dm256,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0460035558789968,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
9619,superspray/electra_large_discriminator_squad2_custom_dataset,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0482305437326431,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
9620,mimi/Waynehills-NLP-doogie,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0625774413347244,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
9621,Finnish-NLP/convbert-base-generator-finnish,Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective,Finnish-NLP/convbert-base-generator-finnish,1.0,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
9622,google/t5-efficient-mini-nl8,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0395975410938262,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
9623,google/t5-efficient-base-nl32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0519380178302526,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
9624,yarongef/DistilProtBert,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
9625,flaubert/flaubert_small_cased,Along with FlauBERT comes FLUE: an evaluation setup for French NLP systems similar to the popular GLUE benchmark,flaubert/flaubert_small_cased,1.0,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
9626,dhpollack/distilbert-dummy-sentiment,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0457039419561624,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
9627,sentence-transformers/paraphrase-MiniLM-L3-v2,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0609195679426193,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
9628,IlyaGusev/rugpt3medium_sum_gazeta,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0689140073955058,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
9629,diper1998/distilgpt2-finetuned-AT,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0915567502379417,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
9630,google/t5-efficient-tiny-nh1,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0601841136813163,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
9631,dbmdz/electra-small-turkish-cased-discriminator,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0075033833272755,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
9632,arm-on/BERTweet-FA,Note that the architecture of this model follows the original BERT,arm-on/BERTweet-FA,1.0,-1.0,NLP,36f660a4-b743-4ce6-8f2c-d814d940e2b3___BERTweet-FA,HF
9633,stas/mt5-tiny-random,The codes for the pretraining are available at retarfi/language-pretraining,izumi-lab/bert-small-japanese-fin,0.0650640949606895,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9634,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0442368015646934,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
9635,rahulacj/bertweet-base-finetuned-sentiment-analysis,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0476285219192504,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
9636,hfl/rbtl3,T5-Efficient-BASE-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl48,0.0285852029919623,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
9637,sonoisa/byt5-small-japanese,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.0698416456580162,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
9638,SetFit/deberta-v3-large__sst2__train-8-8,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0794729590415954,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
9639,google/rembert,You should fine-tune this model for your downstream task,google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
9640,mwesner/reformer-clm,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0889033079147338,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
9641,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0171327125281095,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
9642,google/t5-efficient-small-el32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0523539669811724,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
9643,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.037188417588671,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
9644,google/t5-efficient-base-nl36,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
9645,Davlan/m2m100_418M-eng-yor-mt,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0561302237212657,-1.0,NLP,22200502-47bf-477d-b7e3-dec71a3aae90___m2m100_418M-eng-yor-mt,HF
9646,jkeruotis/LitBERTa-uncased,Not the best model because of limited resources (Trained on ~4.7 GB of data on RTX2070 8GB for ~10 days) but it covers special lithuanian symbols ąčęėįšųūž,jkeruotis/LitBERTa-uncased,1.0,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
9647,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0602545067667961,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
9648,google/t5-efficient-small-el8,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0262565072625875,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
9649,ErykWdowiak/GPTalian,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
9650,google/t5-efficient-base-nl36,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0084453076124191,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
9651,beston91/gpt2-xl_ft_logits_10k,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0056502409279346,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
9652,TJMUCH/transcriptome-iseeek,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.005836509168148,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9653,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0314215123653411,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
9654,dbmdz/electra-base-turkish-cased-discriminator,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.006347800604999,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
9655,google/t5-efficient-small-nl48,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0890380442142486,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
9656,aditeyabaral/sentencetransformer-bert-hinglish-small,Amazon's BORT,amazon/bort,0.0753892958164215,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
9657,jean-paul/kinyaRoberta-small,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
9658,google/bert_uncased_L-4_H-128_A-2,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-4_H-128_A-2,1.0,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
9659,lgris/distilxlsr_bp_8-12,Check out this blog for more in-detail explanation of how to fine-tune the model,lgris/distilxlsr_bp_8-12,1.0,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
9660,google/t5-efficient-large-nl32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0219393707811832,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
9661,antoiloui/belgpt2,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0638373531401157,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
9662,google/t5-efficient-tiny-nh8,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
9663,google/t5-efficient-base-nl40,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0102755315601825,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
9664,bookbot/distil-wav2vec2-adult-child-cls-52m,This model was trained using HuggingFace's PyTorch framework,bookbot/distil-wav2vec2-adult-child-cls-52m,1.0,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
9665,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0609541200101375,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
9666,google/t5-efficient-base-nh16,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0062731225043535,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
9667,neuralspace-reverie/indic-transformers-bn-xlmroberta,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0360651617603642,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
9668,cammy/led-large-16384-arxiv-100-MDS,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0892741084098815,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
9669,Parsa/BBB_prediction_classification_SMILES,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
9670,google/t5-efficient-xl-nl12,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0375079587101936,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
9671,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0283516328781843,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
9672,TJMUCH/transcriptome-iseeek,The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.040030762553215,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9673,google/t5-efficient-small-el16-dl4,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0500631257891654,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
9674,google/t5-efficient-base-ff9000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0468591740354895,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
9675,jpcorb20/pegasus-large-reddit_tifu-samsum-512,This is an implementation of CPT-Base,fnlp/cpt-base,0.066204440469543,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
9676,google/t5-efficient-xl-nl4,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0169108398258686,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
9677,google/t5-efficient-base-nl32,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0168202808126807,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
9678,diper1998/distilgpt2-finetuned-AT,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
9679,deep-learning-analytics/triviaqa-t5-base,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0133642080639089,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
9680,mrm8488/mobilebert-uncased-finetuned-squadv2,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
9681,tucan9389/kcbert-base-finetuned-squad,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0562765747308731,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
9682,KBLab/megatron-bert-base-swedish-cased-600k,"Its sister model used the same setup, but was instead trained for only 125k steps",KBLab/megatron-bert-base-swedish-cased-600k,1.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
9683,google/t5-efficient-large-nh12,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0090470798313617,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
9684,cross-encoder/ms-marco-MiniLM-L-4-v2,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0084372668061405,-1.0,NLP,b57a156c-f487-4efc-a83f-37bedaa63a0a___ms-marco-MiniLM-L-4-v2,HF
9685,beston91/gpt2-xl_ft_logits_10k,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0528480634093284,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
9686,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff1000,0.0764206573367118,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
9687,google/t5-efficient-small-nl20,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0374436853453516,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
9688,TJMUCH/transcriptome-iseeek,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9689,DemangeJeremy/4-sentiments-with-flaubert,mobilebert-uncased fine-tuned on SQUAD v2.0 dataset for QandA downstream task,mrm8488/mobilebert-uncased-finetuned-squadv2,0.0756436139345168,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9690,Sindhu/muril-large-squad2,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,1.0,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
9691,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0265455301851034,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
9692,TJMUCH/transcriptome-iseeek,We have evaluated the model on three tasks with different sequence lengthsThis model was pretrained using a masked language model (MLM) objective on the Persian section of the Oscar dataset,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0815663561224937,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9693,google/t5-efficient-base-dm256,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
9694,microsoft/layoutlm-base-uncased,FongBERT is a BERT model trained on 68.363 sentences in Fon,Gilles/FongBERT,0.0859507396817207,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
9695,google/t5-efficient-small-dm128,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0588094340637325,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
9696,TJMUCH/transcriptome-iseeek,"Below is list of data we trained on,You can use this model by installing torch or tensorflow and Huggingface library transformers",malay-huggingface/xlnet-tiny-bahasa-cased,0.0804832428693771,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9697,nghuyong/ernie-tiny,"For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github",sarnikowski/convbert-medium-small-da-cased,0.0449863523244857,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
9698,yorko/scibert_scivocab_uncased_long_4096,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0672436021268367,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
9699,negfir/distilbert-base-uncased-finetuned-cola,Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements,hyunwoongko/reddit-9B,0.0974440723657608,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
9700,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"zip, use 180G of data to continue trainingThere is no structure of Longformer-base in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-110M,0.0428586602210998,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
9701,superb/hubert-base-superb-er,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0679337177425622,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
9702,eli4s/Bert-L12-h240-A12,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
9703,allenai/wmt19-de-en-6-6-big,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0686275288462638,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
9704,tucan9389/kcbert-base-finetuned-squad,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0880045294761657,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
9705,google/t5-efficient-mini,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0327564850449561,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
9706,hf-internal-testing/tiny-random-rembert,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0679108351469039,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
9707,MoritzLaurer/MiniLM-L6-mnli,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0670063880582649,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
9708,DemangeJeremy/4-sentiments-with-flaubert,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0916270129382609,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9709,negfir/distilbert-base-uncased-finetuned-squad,This project was funded by the Language Technology Programme for Icelandic 2019-2023,jonfd/convbert-small-igc-is,0.0705056264996528,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
9710,google/t5-efficient-tiny-nl24,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0771818831562995,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
9711,google/t5-efficient-small-kv32,T5-Efficient-SMALL-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv32,1.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
9712,flax-community/RoBERTa-large-finnish,This model is case-sensitive: it makes a difference between finnish and Finnish,flax-community/RoBERTa-large-finnish,1.0,-1.0,NLP,bd6a99f8-f445-4aab-803e-729051effeb2___RoBERTa-large-finnish,HF
9713,google/t5-efficient-base-nh16,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0473845042288302,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
9714,hfl/chinese-electra-small-ex-discriminator,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0621405546553432,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
9715,facebook/wmt21-dense-24-wide-en-x,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0579249769449233,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
9716,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-LARGE-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el4,0.0753810703754424,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
9717,google/t5-efficient-large-nh8-nl32,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0481627546250819,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
9718,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
9719,kssteven/ibert-roberta-large,", quantization-aware training) of the quantized model",kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
9720,stas/t5-very-small-random,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0760142505168914,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
9721,google/t5-efficient-small-dm256,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
9722,stas/mt5-tiny-random,This model is cased: it makes a difference between english and English,google/fnet-large,0.0723755806684494,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9723,zoha/wav2vec2-base-timit-demo-colab,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.076623611152172,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
9724,google/t5-efficient-tiny-ff3000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0899736620485782,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
9725,brad1141/Longformer-finetuned-norm,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0769427418708801,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
9726,superspray/electra_large_discriminator_squad2_custom_dataset,This is a BERT model pretrained on texts in the Japanese language,izumi-lab/bert-small-japanese-fin,0.0608280561864376,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
9727,ian0delond/model-test,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0883168652653694,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
9728,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,0.0749041587114334,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
9729,amazon/bort, is a GPT model for French developped by Quantmetry and the Laboratoire de Linguistique Formelle (LLF),asi/gpt-fr-cased-base,0.0614816211163997,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9730,amazon/bort,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,0.0183894382789731,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9731,mwesner/reformer-clm,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0831354632973671,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
9732,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0423747546970843,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
9733,google/t5-efficient-xl-nl12,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0589263681322335,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
9734,google/t5-efficient-small-el8,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0521378638222813,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
9735,uer/t5-v1_1-small-chinese-cluecorpussmall,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0885154753923416,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
9736,cammy/led-large-16384-arxiv-100-MDS,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0585337877273559,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
9737,google/t5-efficient-small-el12,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.066225467249751,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
9738,rahulacj/bertweet-base-finetuned-sentiment-analysis,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0877689719200134,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
9739,phueb/BabyBERTa-2,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,1.0,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
9740,Iskaj/hf-challenge-test,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
9741,google/t5-efficient-mini-nl6,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0636969469487666,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
9742,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm768,0.0132950656116008,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9743,celine98/canine-s-finetuned-sst2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0806538090109825,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
9744,yorko/scibert_scivocab_uncased_long_4096,The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider max_seq_length (4096 vs,yorko/scibert_scivocab_uncased_long_4096,1.0,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
9745,1one5ome/gpt2-chinese-gulong,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0430783852934837,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
9746,VietAI/gpt-neo-1.3B-vietnamese-news,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0745397321879863,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
9747,google/t5-efficient-small-dm1000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-dm1000,1.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
9748,sberbank-ai/rugpt3large_based_on_gpt2,It can be considered as small copy of Conversational DistilRuBERT-base,DeepPavlov/distilrubert-tiny-cased-conversational,0.0451362975873052,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
9749,google/t5-efficient-base-el16,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
9750,jkeruotis/LitBERTa-uncased,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0117790438234806,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
9751,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.0621468983590602,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
9752,allenai/wmt19-de-en-6-6-base,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0281702727079391,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
9753,google/t5-efficient-base-el8,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0138673614710569,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
9754,aditeyabaral/sentencetransformer-bert-hinglish-small,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
9755,beston91/gpt2-xl_ft_logits_10k,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0892584398388862,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
9756,google/t5-efficient-small-kv32,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0878019221127033,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
9757,cammy/led-base-16384-100-MDS,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
9758,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0580363534390926,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
9759,google/t5-efficient-base-kv16,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
9760,clue/roberta_chinese_3L312_clue_tiny,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0735116284340619,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
9761,google/t5-efficient-small-dm128,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0934762619435786,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
9762,google/t5-efficient-small-ff3000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0093999616801738,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
9763,google/t5-efficient-tiny-nl16,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0684163551777601,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
9764,aubmindlab/aragpt2-large,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0333852069452404,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
9765,zoha/wav2vec2-base-timit-demo-colab,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0778358355164527,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
9766,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0720172673463821,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
9767,sonoisa/byt5-small-japanese,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.036983236670494,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
9768,saibo/random-roberta-tiny,com/peterwestuw/GPT2ForwardBackward,danyaljj/opengpt2_pytorch_forward,0.0158785036765038,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
9769,tau/tavbert-he,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0360388131812213,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
9770,IlyaGusev/rugpt3medium_sum_gazeta,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0756756197661161,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
9771,fnlp/bart-base-chinese,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0072699449956417,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
9772,negfir/distilbert-base-uncased-finetuned-cola,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
9773,rinna/japanese-gpt-1b,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.0466148269673188,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
9774,microsoft/layoutlm-large-uncased,"This is one of the smaller pre-trained BERT variants, together with bert-mini bert-small and bert-medium",prajjwal1/bert-tiny,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
9775,google/t5-efficient-large-nh24,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0468760691583156,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
9776,Gilles/FongBERT,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
9777,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff12000,0.0869545042514801,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
9778,aubmindlab/aragpt2-mega,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
9779,google/t5-efficient-base-dm2000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
9780,google/t5-efficient-small-ff1000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0023197792470455,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
9781,mwesner/bart-mlm,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0952812731266021,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
9782,google/t5-efficient-small-ff12000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0266042556613683,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
9783,GKLMIP/electra-myanmar-small-uncased,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
9784,GKLMIP/electra-myanmar-small-uncased,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.02732546441257,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
9785,mikaelsouza/msft-regular-model,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0199733506888151,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
9786,superb/hubert-base-superb-er,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0873435158282518,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
9787,NovelAI/genji-python-6B,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
9788,google/t5-efficient-small-nl36,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.060452653095126,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
9789,google/t5-efficient-tiny-nh1,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0486042909324168,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
9790,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff3000,0.0659031271934509,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
9791,TsinghuaAI/CPM-Generate,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0195263044110366,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
9792,saibo/random-roberta-tiny,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0248429947532713,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
9793,sentence-transformers/paraphrase-MiniLM-L3-v2,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.074721410870552,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
9794,superspray/electra_large_discriminator_squad2_custom_dataset,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.0924813598394393,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
9795,DemangeJeremy/4-sentiments-with-flaubert,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0394886042922735,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9796,mwesner/bart-mlm,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
9797,microsoft/layoutlm-base-cased,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0487595684826374,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
9798,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0572396516799926,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
9799,amazon/bort,"When using this model, make sure that your speech input is sampled at 16kHz",jonatasgrosman/wav2vec2-xls-r-1b-english,0.0708537325263023,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9800,google/t5-efficient-base-dm1000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
9801,KoichiYasuoka/roberta-base-ukrainian,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.032553281635046,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
9802,google/bert_uncased_L-8_H-768_A-12,Its original repository can be found here,tau/splinter-large-qass,0.0176675698409477,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
9803,google/t5-efficient-large-kv16,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0133255403488873,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
9804,groar/gpt-neo-1.3B-finetuned-escape2,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,mrp/simcse-model-wangchanberta,0.0916589349508285,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
9805,jhu-clsp/bibert-ende,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0070897160718838,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
9806,hajime9652/xlnet-japanese,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0952861160039901,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
9807,superspray/electra_large_discriminator_squad2_custom_dataset,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0368420258164405,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
9808,google/t5-efficient-xxl-nl4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0234233401715755,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
9809,piotr-rybak/poleval2021-task4-herbert-large-encoder,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0346470922231674,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
9810,SajjadAyoubi/distil-bigbird-fa-zwnj,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,1.0,-1.0,NLP,93bf4f14-6c7a-41bc-854c-061751656605___distil-bigbird-fa-zwnj,HF
9811,stas/pegasus-cnn_dailymail-tiny-random,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
9812,google/t5-efficient-base-nh8,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0230077560991048,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
9813,google/t5-efficient-small-kv16,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
9814,amazon/bort,Amazon's BORT,amazon/bort,1.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9815,google/t5-efficient-large-nl10,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0285820886492729,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
9816,VietAI/gpt-neo-1.3B-vietnamese-news,This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU,kssteven/ibert-roberta-large,0.0846427083015441,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
9817,IDEA-CCNL/Erlangshen-Longformer-330M,This model is case-sensitive: it makes a difference between finnish and Finnish,flax-community/RoBERTa-large-finnish,0.0082729775458574,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
9818,sentence-transformers/paraphrase-MiniLM-L3-v2,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.062628798186779,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
9819,ian0delond/model-test,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0309599805623292,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
9820,hfl/chinese-legal-electra-base-generator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
9821,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
9822,rdenadai/BR_BERTo,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0654271667202313,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
9823,microsoft/layoutlm-base-uncased,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0767787322402,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
9824,google/t5-efficient-small-nl40,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0327609330415725,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
9825,kyryl0s/gpt2-uk-xxs,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,0.0200169212184846,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
9826,diper1998/distilgpt2-finetuned-AT,"We modify the original position code of longformer to rotational position coding, and use 180G of data to pretraining",IDEA-CCNL/Erlangshen-Longformer-330M,0.0569670610129833,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
9827,facebook/blenderbot-3B,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0088546962610312,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
9828,mimicheng/codeparrot-ds,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
9829,google/t5-efficient-small-dm128,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0436353888362645,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
9830,akdeniz27/deberta-v2-xlarge-cuad,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0225031226873397,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
9831,hf-internal-testing/tiny-random-rembert,Use jcblaise/roberta-tagalog-base or jcblaise/roberta-tagalog-large instead for better performance,jcblaise/distilbert-tagalog-base-cased,0.0967245772480964,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
9832,microsoft/layoutlm-base-cased,FongBERT is a BERT model trained on 68.363 sentences in Fon,Gilles/FongBERT,0.0859507396817207,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
9833,dhpollack/distilbert-dummy-sentiment,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0149294026196003,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
9834,mrm8488/xlm-multi-finetuned-xquadv1,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0255058784969151,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
9835,google/t5-efficient-small-kv256,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
9836,google/t5-efficient-base-nl2,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
9837,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0279737301170825,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
9838,google/t5-efficient-base-el8,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
9839,aditeyabaral/sentencetransformer-bert-hinglish-small,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
9840,TJMUCH/transcriptome-iseeek,"If you found this model useful, please cite the original work:",filco306/gpt2-switchboard-paraphraser,0.0660012662410736,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9841,GKLMIP/electra-myanmar-small-uncased,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
9842,google/t5-efficient-large-el8,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.021401410922408,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
9843,SupriyaArun/squeezebert-uncased-finetuned-squad,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
9844,hfl/chinese-electra-180g-small-ex-discriminator,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
9845,eli4s/Bert-L12-h256-A4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0018439195118844,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
9846,IDEA-CCNL/Erlangshen-Longformer-330M,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0386809958145021,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
9847,mimicheng/codeparrot-ds,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0526205599308013,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
9848,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0020644341905912,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
9849,superspray/electra_large_discriminator_squad2_custom_dataset,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0335031561553478,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
9850,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0645255744457244,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
9851,google/t5-efficient-large-el2,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.080344159156084,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
9852,google/t5-efficient-tiny-nh8,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
9853,google/t5-efficient-mini-nl8,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0795983560383319,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
9854,google/t5-efficient-tiny-ff6000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0204888638108968,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
9855,uer/t5-v1_1-small-chinese-cluecorpussmall,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0408295821398496,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
9856,google/t5-efficient-tiny-el8,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0117096547037362,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
9857,google/bigbird-roberta-large,"BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences",google/bigbird-roberta-large,1.0,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
9858,google/t5-efficient-base-nh16,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
9859,tau/tavbert-he,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
9860,everdoubling/byt5-Korean-small,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0263616435229778,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
9861,google/t5-efficient-base-ff1000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0476648174226283,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
9862,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0102879740297794,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
9863,Parsa/BBB_prediction_classification_SMILES,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0527364866187174,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
9864,Iskaj/hf-challenge-test,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0737295001745224,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
9865,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
9866,google/t5-efficient-tiny-nl2,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0016291784122586,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
9867,tau/tavbert-he,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0434445878490805,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
9868,groar/gpt-neo-1.3B-finetuned-escape2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
9869,SajjadAyoubi/distil-bigbird-fa-zwnj,We have evaluated the model on three tasks with different sequence lengthsThis model was pretrained using a masked language model (MLM) objective on the Persian section of the Oscar dataset,SajjadAyoubi/distil-bigbird-fa-zwnj,1.0,-1.0,NLP,93bf4f14-6c7a-41bc-854c-061751656605___distil-bigbird-fa-zwnj,HF
9870,google/t5-efficient-small-kv256,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0735398903489112,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
9871,microsoft/layoutlm-large-uncased,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0736049860715866,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
9872,flboehm/reddit-bert-text_5,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
9873,google/t5-efficient-large-dm256,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0493360441178083,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
9874,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0169004462659358,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
9875,amazon/bort,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0016190856695175,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
9876,google/t5-efficient-large-nh24,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0536925736814737,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
9877,dbmdz/bert-medium-historic-multilingual-cased,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0332960956729948,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
9878,hfl/chinese-electra-small-ex-generator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
9879,anirudh21/xlnet-base-cased-finetuned-rte,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
9880,TJMUCH/transcriptome-iseeek,This model is not expected to perform well in any other languages,Sindhu/muril-large-squad2,0.0769652500748634,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9881,dbmdz/electra-base-turkish-cased-discriminator,"ELECTRA models are trained to distinguish ""real"" input tokens vs ""fake"" input tokens generated by another neural network, similar to the discriminator of a GAN",dbmdz/electra-base-turkish-cased-discriminator,1.0,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
9882,hfl/rbt3,T5-Efficient-BASE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv128,0.0479611661285161,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
9883,facebook/blenderbot-90M,"We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available",facebook/blenderbot-90M,1.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
9884,patrickvonplaten/unispeech-sat-large-timit-ft,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
9885,jkeruotis/LitBERTa-uncased,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0589710865169763,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
9886,microsoft/layoutlm-base-cased,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.060350090265274,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
9887,diper1998/distilgpt2-finetuned-AT,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
9888,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Amazon's BORT,amazon/bort,0.0794344544410705,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
9889,KoichiYasuoka/roberta-base-ukrainian,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0126615148037672,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
9890,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0639094226062297,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
9891,google/t5-efficient-base-nl40,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
9892,google/t5-efficient-mini-nl6,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0368990786373614,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
9893,google/t5-efficient-small-dm128,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0504498649388551,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
9894,google/t5-efficient-large-nh2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
9895,google/t5-efficient-base-nl40,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0114847728982567,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
9896,google/t5-efficient-large-kv16,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0541701354086398,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
9897,stas/pegasus-cnn_dailymail-tiny-random,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.022994790226221,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
9898,tau/tavbert-he,T5-Efficient-TINY-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-el8,0.0563746355473995,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
9899,aubmindlab/aragpt2-large,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
9900,google/t5-efficient-large-nh8-nl32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0523068085312842,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
9901,rdenadai/BR_BERTo,It has less than 1% difference in performance compared to the BERT based model while having 2.0x the speed,khizon/distilbert-unreliable-news-eng-4L,0.0383090252677599,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
9902,Palak/microsoft_deberta-base_squad,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0875933319330215,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
9903,mimi/Waynehills-NLP-doogie,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
9904,google/t5-efficient-large-kv16,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0853103585541248,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
9905,google/t5-efficient-large-nh4,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0388274313881992,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
9906,google/t5-efficient-mini-nl6,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0459328629076479,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
9907,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
9908,google/t5-efficient-tiny-nl6,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0620892643928527,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
9909,google/t5-efficient-large-nh12,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0056597217917442,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
9910,malay-huggingface/xlnet-base-bahasa-cased,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
9911,microsoft/layoutlm-base-cased,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0836386233568191,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
9912,google/t5-efficient-base-nl48,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0027389861643313,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
9913,flax-community/gpt2-medium-persian,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0058355399717887,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
9914,google/t5-efficient-base-nl36,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0410315822809934,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
9915,pszemraj/led-large-book-summary,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.017259451560676,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
9916,tartuNLP/gpt-4-est-large,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.013892613351345,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
9917,stas/mt5-tiny-random,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0666912496089935,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9918,google/t5-efficient-large-nl12,Its original repository can be found here,tau/splinter-large-qass,0.0536112207919358,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
9919,superspray/electra_large_discriminator_squad2_custom_dataset,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0593007653951644,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
9920,zoha/wav2vec2-base-timit-demo-colab,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0813486278057098,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
9921,GKLMIP/electra-myanmar-small-uncased,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0322209503501653,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
9922,yorko/scibert_scivocab_uncased_long_4096,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0437084259465336,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
9923,google/t5-efficient-large-dm2000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0333259925246238,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
9924,khizon/distilbert-unreliable-news-eng-4L,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0405713273212313,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
9925,google/t5-efficient-large-kv32,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0569484708830713,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
9926,readerbench/RoBERT-small,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0090835239738225,-1.0,NLP,af9865c1-d52f-4c4d-8696-c19b9085823b___RoBERT-small,HF
9927,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NL10 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl10,0.0893943905830383,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
9928,izumi-lab/bert-small-japanese-fin,I have made no attempts to censor,bigjoedata/rockbot,0.0250534079968929,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
9929,google/t5-efficient-small-kv16,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0650241393595933,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
9930,google/t5-efficient-mini-nl12,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0698152612894773,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
9931,GKLMIP/electra-myanmar-small-uncased,"We modify the original position code of longformer to rotational position coding, and use 180G of data to pretraining",IDEA-CCNL/Erlangshen-Longformer-330M,0.0229065036401152,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
9932,nghuyong/ernie-tiny,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0613176450133323,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
9933,google/t5-efficient-base-kv16,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0910748802125453,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
9934,nghuyong/ernie-tiny,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0676320251077413,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
9935,TJMUCH/transcriptome-iseeek,Pretrained model on Finnish language using a masked language modeling (MLM) objective,flax-community/RoBERTa-large-finnish,0.0957981050014495,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9936,rdenadai/BR_BERTo,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-768_A-12,0.0378365392486253,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
9937,google/t5-efficient-small-nl24,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
9938,mwesner/reformer-clm,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,0.0689641088247299,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
9939,mwesner/reformer-clm,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0952494740486145,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
9940,gayanin/bart-mlm-pubmed-medterm,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0445052236318588,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
9941,google/t5-efficient-large-el2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
9942,rdenadai/BR_BERTo,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,0.016037850951155,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
9943,google/t5-efficient-large-kv32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
9944,negfir/distilbert-base-uncased-finetuned-cola,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.032803751528263,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
9945,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-EL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el12,0.0572557030245661,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9946,stas/mt5-tiny-random,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0578115954995155,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9947,google/t5-efficient-base-kv128,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0596353206783532,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
9948,google/t5-efficient-base-nh24,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.057391595095396,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
9949,malay-huggingface/xlnet-tiny-bahasa-cased,"Below is list of data we trained on,You can use this model by installing torch or tensorflow and Huggingface library transformers",malay-huggingface/xlnet-tiny-bahasa-cased,1.0,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
9950,TJMUCH/transcriptome-iseeek,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
9951,oskrmiguel/mt5-simplification-spanish,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0684909224510192,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
9952,google/t5-efficient-large-nl36,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0709743835031986,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
9953,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.083799809217453,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
9954,brad1141/Longformer-finetuned-norm,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.0827180966734886,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
9955,google/t5-efficient-small-nl48,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0074554495513439,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
9956,minimaxir/magic-the-gathering,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.0364231029525399,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
9957,google/t5-efficient-large-kv256,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0236354488879441,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
9958,google/t5-efficient-small-ff1000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0337710650637745,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
9959,google/t5-efficient-base-dm256,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0626251753419637,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
9960,google/t5-efficient-base-nl32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0169925177469849,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
9961,aditeyabaral/sentencetransformer-bert-hinglish-small,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.069600760936737,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
9962,mimi/Waynehills-NLP-doogie,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
9963,google/t5-efficient-base-kv256,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
9964,negfir/distilbert-base-uncased-finetuned-squad,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
9965,allenai/wmt19-de-en-6-6-base,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
9966,ian0delond/model-test,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0923503935337066,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
9967,google/t5-efficient-base-el16,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0727315805852412,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
9968,razent/SciFive-large-Pubmed,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
9969,mrm8488/deberta-v3-small-finetuned-mrpc,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0967484712600708,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
9970,DemangeJeremy/4-sentiments-with-flaubert,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
9971,google/t5-efficient-large-el4,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0467948112636803,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
9972,google/t5-efficient-large-nh4,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0404649060219526,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
9973,cointegrated/rubert-tiny-bilingual-nli,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,1.0,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
9974,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
9975,google/t5-efficient-base-dm2000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0334518793970346,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
9976,jonatasgrosman/wav2vec2-xls-r-1b-english,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0374956174443165,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
9977,mgreenbe/bertlet-base-uncased-for-sequence-classification,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0509775280952453,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
9978,tau/tavbert-he,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0243665622547269,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
9979,google/t5-efficient-tiny-ff3000,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.043964086100459,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
9980,hfl/chinese-electra-small-ex-generator,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0596902673132717,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
9981,hfl/rbtl3,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0570750571787357,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
9982,RabotaRu/HRBert-mini,!!!This is a distilled HRBert model for an mlm task,RabotaRu/HRBert-mini,1.0,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
9983,google/t5-efficient-base-dm256,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.061385380104184,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
9984,cmarkea/distilcamembert-base-qa,"To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power thanks to DistilCamemBERT",cmarkea/distilcamembert-base-qa,1.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
9985,rinna/japanese-gpt2-xsmall,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
9986,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0562208145856857,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
9987,stas/mt5-tiny-random,Source language: English,NYTK/translation-marianmt-en-hu,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
9988,IDEA-CCNL/Erlangshen-Longformer-110M,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0085016554221511,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
9989,diper1998/distilgpt2-finetuned-AT,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0055614942684769,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
9990,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.0608044415712356,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
9991,cross-encoder/stsb-TinyBERT-L-4,This model was trained using SentenceTransformers Cross-Encoder class,cross-encoder/stsb-TinyBERT-L-4,1.0,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
9992,cammy/led-large-16384-arxiv-100-MDS,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0389808490872383,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
9993,google/t5-efficient-base-kv32,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0417007263749837,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
9994,bolbolzaban/gpt2-persian,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0417942609637975,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
9995,google/t5-efficient-base-dm512,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
9996,google/t5-efficient-large-nh2,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0707232058048248,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
9997,google/t5-efficient-base-nl32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
9998,suzuki/distilbert-base-uncased-finetuned-squad,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.09844471514225,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
9999,Parsa/BBB_prediction_classification_SMILES,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0495372315247853,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
10000,nherve/flaubert-oral-asr,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0472638656695683,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
10001,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
10002,obss/mt5-base-3task-highlight-tquad2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0912448130548,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
10003,SupriyaArun/squeezebert-uncased-finetuned-squad,WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation,facebook/wmt21-dense-24-wide-en-x,0.0988829135894775,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
10004,tartuNLP/gpt-4-est-large,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0770523846149444,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
10005,gayanin/bart-mlm-pubmed-medterm,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0831306651234626,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
10006,google/t5-efficient-tiny-nl2,Its original repository can be found here,tau/splinter-large-qass,0.0482816323637961,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
10007,IDEA-CCNL/Erlangshen-Longformer-110M,"If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:",nherve/flaubert-oral-ft,0.0647108666598796,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
10008,google/t5-efficient-base-el8,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0356831066310405,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
10009,stas/mt5-tiny-random,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0383628048002719,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
10010,mwesner/reformer-clm,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0683202296495437,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
10011,google/t5-efficient-small-el16-dl4,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
10012,thu-coai/LongLM-large,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.032862831527988,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
10013,everdoubling/byt5-Korean-small,The main task model is based on facebook/bart-large-xsum and meta-task model (sentiment) is  VictorSanh/roberta-base-finetuned-yelp-polarity,ebagdasa/propaganda_positive_bart_large,0.0480448983609676,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
10014,brad1141/Longformer-finetuned-norm,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0194258335977792,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
10015,rinna/japanese-gpt2-xsmall,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0181481093168258,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
10016,google/t5-efficient-small-nl36,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0525407195091247,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
10017,google/t5-efficient-small-kv128,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0752380117774009,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
10018,amazon/bort,Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective,readerbench/RoBERT-small,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
10019,DemangeJeremy/4-sentiments-with-flaubert,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0344789363443851,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10020,cammy/led-large-16384-arxiv-100-MDS,For best results use python as below,pszemraj/led-large-book-summary,0.0690829455852508,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
10021,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0375561080873012,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
10022,stas/mt5-tiny-random,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0969808399677276,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
10023,ptaszynski/yacis-electra-small-japanese,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0084591722115874,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
10024,groar/gpt-neo-1.3B-finetuned-escape2,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
10025,hfl/rbt3,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0530707361176609,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10026,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0760979801416397,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
10027,hf-internal-testing/tiny-random-rembert,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0814747661352157,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
10028,saurkulsh/T0pp,"We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations",saurkulsh/T0pp,1.0,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
10029,hajime9652/xlnet-japanese,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
10030,google/t5-efficient-tiny-nl16,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0522123826667666,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
10031,google/t5-efficient-small-ff9000,Its original repository can be found here,tau/splinter-large-qass,0.0357595328241586,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
10032,google/t5-efficient-tiny-nh1,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
10033,mimi/Waynehills-NLP-doogie,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0354373641312122,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10034,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,It was introduced in this paper,readerbench/RoBERT-small,0.0622829534113407,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
10035,hfl/chinese-xlnet-mid,"It supports sequences of length up to 4,096! Longformer uses a combination of a sliding window (local) attention and global attention",mrm8488/longformer-base-4096-spanish,0.0546959439913431,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
10036,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm1000,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10037,SupriyaArun/squeezebert-uncased-finetuned-squad,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0663934499025344,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
10038,google/t5-efficient-large-el4,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
10039,SajjadAyoubi/distil-bigbird-fa-zwnj,"In our work, we have trained the BigBird model for the Persian language to process texts up to 4096 in the Farsi (Persian) language using sparse attention",SajjadAyoubi/distil-bigbird-fa-zwnj,1.0,-1.0,NLP,93bf4f14-6c7a-41bc-854c-061751656605___distil-bigbird-fa-zwnj,HF
10040,microsoft/layoutlm-base-uncased,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0765406191349029,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
10041,google/bert_uncased_L-4_H-128_A-2,"This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking)",google/bert_uncased_L-4_H-128_A-2,1.0,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
10042,patrickvonplaten/unispeech-sat-large-timit-ft,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
10043,google/t5-efficient-base-kv32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0570449735969304,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
10044,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff6000,0.0484593510627746,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
10045,google/t5-efficient-large-nl32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.064503114670515,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
10046,sberbank-ai/rugpt3large_based_on_gpt2,This model is cased: it makes a difference between english and English,google/fnet-large,0.0438760695978999,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
10047,mikaelsouza/msft-regular-model,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0874260663986206,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
10048,groar/gpt-neo-1.3B-finetuned-escape2,"Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan BaskaranWork done via MLDMM Lab",mldmm/GlassBERTa,0.0999220311641693,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
10049,google/t5-efficient-mini-nl8,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0693791825324296,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
10050,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0329171270132064,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
10051,diper1998/distilgpt2-finetuned-AT,The model was trained on Korean text,skplanet/dialog-koelectra-small-generator,0.0732082948088646,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
10052,saibo/random-roberta-mini,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0368671915493905,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
10053,google/t5-efficient-large-el2,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.048814677633345,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
10054,allenai/wmt19-de-en-6-6-base,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0118199801072477,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
10055,tau/tavbert-he,"Trained, validate, and tested using a subset of the NELA-GT-2018 dataset",khizon/distilbert-unreliable-news-eng-4L,0.0708087980747221,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
10056,sarnikowski/convbert-medium-small-da-cased,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.039926571616282,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
10057,google/t5-efficient-base-dm512,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0658101011067628,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
10058,google/t5-efficient-tiny-nl6,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0519062895327806,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
10059,google/t5-efficient-large-nl20,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0533686839044093,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
10060,hajime9652/xlnet-japanese,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0432151481509208,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
10061,microsoft/layoutlm-base-cased,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0663190186023712,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10062,google/t5-efficient-small-el16-dl4,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.063425788655877,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
10063,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0927920788526535,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10064,mgreenbe/bertlet-base-uncased-for-sequence-classification,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0998825281858444,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
10065,google/t5-efficient-small-nl20,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.071267757564783,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
10066,mimi/Waynehills-NLP-doogie,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10067,eli4s/Bert-L12-h256-A4,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0209290261846036,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
10068,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0159185789525508,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
10069,crabz/distil-slovakbert-ner,I have made no attempts to censor,bigjoedata/rockbot,0.0166400950402021,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
10070,Palak/microsoft_deberta-base_squad,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
10071,tartuNLP/gpt-4-est-large,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0923281535506248,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
10072,hfl/rbt3,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0438426560722291,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10073,MoritzLaurer/MiniLM-L6-mnli,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0052534028266867,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
10074,jonfd/convbert-small-igc-is,This project was funded by the Language Technology Programme for Icelandic 2019-2023,jonfd/convbert-small-igc-is,1.0,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
10075,junnyu/roformer_base_wwm_cluecorpussmall,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
10076,flboehm/reddit-bert-text_5,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0366555377840995,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
10077,mwesner/reformer-clm,T5-Efficient-LARGE-NH8-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8-nl32,0.0899859741330146,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
10078,mwesner/reformer-clm,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0659911334514617,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
10079,groar/gpt-neo-1.3B-finetuned-escape2,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0747734159231185,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
10080,stas/t5-very-small-random,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10081,sonoisa/byt5-small-japanese,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0192425325512886,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
10082,stas/t5-very-small-random,† ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-162M,0.0763929858803749,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10083,bigjoedata/rockbot,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0160675263032317,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
10084,nyu-mll/roberta-med-small-1M-3,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
10085,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0258727930486202,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
10086,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0848200842738151,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
10087,google/t5-efficient-small-nl20,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0248245727270841,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
10088,tartuNLP/gpt-4-est-large,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0494992583990097,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
10089,KoichiYasuoka/roberta-base-ukrainian,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0064389202743768,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
10090,google/t5-efficient-base-kv16,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0454541537910699,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
10091,google/t5-efficient-small-nl48,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0101449880748986,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
10092,google/rembert,It was introduced in the paper Rethinking embedding coupling in pre-trained language models,google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
10093,google/t5-efficient-base-nl48,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.055986164137721,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
10094,google/t5-efficient-tiny-nl12,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.056966533884406,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
10095,microsoft/layoutlm-base-uncased,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.005147055722773,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
10096,google/t5-efficient-small-dm256,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0433976929634809,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
10097,IDEA-CCNL/Erlangshen-Longformer-110M,"If you use our model or find it useful in your projects, please cite our work:",jcblaise/electra-tagalog-small-cased-generator,0.0511684864759444,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
10098,hfl/rbt3,T5-Efficient-BASE-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl48,0.0285852029919623,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10099,google/t5-efficient-base-ff9000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0221838392317294,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
10100,sentence-transformers/paraphrase-MiniLM-L3-v2,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.093704454600811,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
10101,google/t5-efficient-small-el12,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
10102,aditeyabaral/sentencetransformer-bert-hinglish-small,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
10103,hfl/rbt3,T5-Efficient-XXL-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xxl-nl4,0.0580602586269378,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10104,hfl/chinese-electra-180g-small-generator,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0347680797179539,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
10105,gilparmentier/pokemon_gptj_model,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,1.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
10106,google/t5-efficient-small-nl48,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0589871481060981,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
10107,google/t5-efficient-small-dm128,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0671716593205928,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
10108,IDEA-CCNL/Zhouwenwang-Unified-1.3B,Zhouwenwang-Unified-1.3B is the largest model for LM and MLM tasks in the Chinese field,IDEA-CCNL/Zhouwenwang-Unified-1.3B,1.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
10109,google/t5-efficient-large-nl20,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
10110,google/t5-efficient-base-nl2,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0329532120376825,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
10111,google/bert_uncased_L-10_H-768_A-12,"We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large",google/bert_uncased_L-10_H-768_A-12,1.0,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
10112,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0664936006069183,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
10113,SupriyaArun/squeezebert-uncased-finetuned-squad,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.0285674296319484,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
10114,pulp/CHILDES-ParentBERTo,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0196043811738491,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
10115,mmaguero/gn-bert-tiny-cased,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0766674913465976,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
10116,ErykWdowiak/GPTalian,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0146486841142177,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
10117,google/t5-efficient-base-dm1000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
10118,google/t5-efficient-small-kv32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0566101074218749,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
10119,google/t5-efficient-large-dm768,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-dm768,1.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
10120,anas-awadalla/bert-tiny-finetuned-squad,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
10121,IlyaGusev/rugpt3medium_sum_gazeta,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0356325600296258,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
10122,google/t5-efficient-base-nl36,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0044604409486055,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
10123,google/t5-efficient-base-nh24,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
10124,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0804153382778167,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
10125,google/t5-efficient-large-nl32,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.053728362545371,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
10126,ian0delond/model-test,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0969045162200927,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
10127,mimi/Waynehills-NLP-doogie,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10128,google/t5-efficient-small-kv256,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,1.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
10129,google/t5-efficient-base-el16,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0053096562623978,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
10130,google/t5-efficient-large-nl10,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0495941545814275,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
10131,aliosm/ComVE-gpt2-large,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0412811273708939,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
10132,cammy/led-base-16384-100-MDS,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.056478165090084,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
10133,tuner007/pegasus_qa,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.074038028717041,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
10134,google/t5-efficient-tiny-nl2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
10135,cammy/led-base-16384-100-MDS,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0387897342443466,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
10136,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0759158059954643,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
10137,google/t5-efficient-tiny-nl12,T5-Efficient-TINY-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl12,1.0,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
10138,google/t5-efficient-xl-nl6,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0914437621831893,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
10139,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0782953798770904,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
10140,mimi/Waynehills-NLP-doogie,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0665990859270095,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10141,uer/pegasus-base-chinese-cluecorpussmall,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
10142,rahulacj/bertweet-base-finetuned-sentiment-analysis,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0470593869686126,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
10143,google/t5-efficient-xl-nl4,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0784952491521835,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
10144,microsoft/layoutlm-base-cased,This project was funded by the Language Technology Programme for Icelandic 2019-2023,jonfd/convbert-small-igc-is,0.082274779677391,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10145,piotr-rybak/poleval2021-task4-herbert-large-encoder,org/abs/1904.09223This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-1.0,0.0094260349869728,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
10146,fnlp/bart-large-chinese,"This model has been fine-tuned by the HuggingSound tool, and thanks to the GPU credits generously given by the OVHcloud :)Using the HuggingSound library:",jonatasgrosman/wav2vec2-xls-r-1b-english,0.0891853123903274,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
10147,microsoft/deberta-v2-xxlarge-mnli,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,1.0,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
10148,mrm8488/chEMBL_smiles_v1,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,919d68f1-5654-4b4c-a9c7-f59f67ababe7___chEMBL_smiles_v1,HF
10149,zoha/wav2vec2-base-timit-demo-colab,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.083790123462677,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
10150,negfir/distilbert-base-uncased-finetuned-squad,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.026062361896038,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
10151,google/t5-efficient-small-nl20,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-nl20,1.0,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
10152,hfl/rbt3,T5-Efficient-SMALL-EL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el32,0.0620180293917655,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10153,allenai/macaw-answer-11b,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
10154,google/t5-efficient-small-el8,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0845588780939578,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
10155,cammy/led-base-16384-100-MDS,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
10156,sentence-transformers/paraphrase-MiniLM-L3-v2,org/abs/1904.09223This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-1.0,0.0105667673051357,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
10157,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,This model is a fine-tuned version of sshleifer/tiny-mbart on the wmt16_en_ro_pre_processed dataset,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,1.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
10158,google/t5-efficient-xxl-nl4,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0495997574180364,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
10159,diper1998/distilgpt2-finetuned-AT,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0672305524349212,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
10160,google/t5-efficient-small-dm128,T5-Efficient-SMALL-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm128,1.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
10161,google/t5-efficient-large-el4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0592122822999953,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
10162,mrm8488/xlm-multi-finetuned-xquadv1,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
10163,google/t5-efficient-base-dm512,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0388483274728059,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
10164,google/t5-efficient-base-el6,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0104637285694479,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
10165,google/t5-efficient-tiny-el8,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0651082284748554,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
10166,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0058181311469525,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
10167,google/t5-efficient-xl-nl6,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0966673791408538,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
10168,google/t5-efficient-tiny-ff12000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
10169,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.076784923672676,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
10170,hfl/chinese-xlnet-mid,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
10171,mrm8488/xlm-multi-finetuned-xquadv1,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0498135853558778,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
10172,Palak/microsoft_deberta-base_squad,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0740226730704307,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
10173,google/t5-efficient-small-ff1000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
10174,google/t5-efficient-tiny-nh8,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0007101353257894,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
10175,SetFit/deberta-v3-large__sst2__train-8-8,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0942780673503875,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
10176,GKLMIP/electra-myanmar-small-uncased,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0289527401328087,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
10177,google/t5-efficient-tiny-nl2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0693008750677108,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
10178,uer/t5-v1_1-base-chinese-cluecorpussmall,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0024300487712025,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
10179,google/t5-efficient-xl-nl12,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0538923647254705,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
10180,google/t5-efficient-small-kv32,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.042069612070918,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
10181,google/t5-efficient-xl-nl16,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.036154631525278,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
10182,zoha/wav2vec2-base-timit-demo-colab,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0660739988088607,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
10183,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
10184,castorini/afriberta_small,"For example, assuming we want to finetune this model on a token classification task, we do the following:The model was trained on an aggregation of datasets from the BBC news website and Common Crawl",castorini/afriberta_small,1.0,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
10185,hfl/chinese-electra-small-ex-generator,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0275817615911364,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
10186,malay-huggingface/t5-super-super-tiny-bahasa-cased,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-super-super-tiny-bahasa-cased,1.0,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
10187,VietAI/gpt-neo-1.3B-vietnamese-news,"In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0834798365831375,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
10188,superspray/electra_large_discriminator_squad2_custom_dataset,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
10189,microsoft/layoutlm-base-uncased,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.081086441874504,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
10190,google/t5-efficient-large-dm256,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.039568593725562,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
10191,GKLMIP/electra-myanmar-small-uncased,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.023553766310215,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
10192,facebook/mbart-large-50-many-to-one-mmt,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,1aed0c2f-c6c7-49df-87c0-c3bebcdb63af___mbart-large-50-many-to-one-mmt,HF
10193,mrm8488/mobilebert-uncased-finetuned-squadv2,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0168959870934486,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
10194,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0799365788698196,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
10195,liaad/srl-pt_xlmr-base,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
10196,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0984330102801323,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
10197,hfl/rbtl3,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0787255540490149,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
10198,suzuki/distilbert-base-uncased-finetuned-squad,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.026062361896038,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
10199,razent/SciFive-large-Pubmed,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0633640233427285,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
10200,mgreenbe/bertlet-base-uncased-for-sequence-classification,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0910171121358871,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
10201,dbmdz/convbert-base-german-europeana-cased,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0546543051799138,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
10202,google/t5-efficient-small-el16-dl4,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0247385995462536,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
10203,DemangeJeremy/4-sentiments-with-flaubert,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0620604362338781,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10204,minimaxir/magic-the-gathering,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0499484781175851,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
10205,hajime9652/xlnet-japanese,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0698041394352912,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
10206,TJMUCH/transcriptome-iseeek,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0497945435345172,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10207,mimicheng/codeparrot-ds,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
10208,negfir/distilbert-base-uncased-finetuned-cola,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0363101735711097,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
10209,castorini/afriberta_large,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0519323756452649,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
10210,google/t5-efficient-tiny-nh32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-nh32,1.0,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
10211,google/t5-efficient-small-dm256,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,1.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
10212,google/t5-efficient-tiny-el8,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.039239527657628,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
10213,cross-encoder/nli-deberta-v3-xsmall,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0206851217895745,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
10214,mrm8488/bert-small-finetuned-squadv2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0407666396349668,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
10215,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.022559804841876,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
10216,lassl/gpt2-ko-small,"If you want to get information for training, you should see config",lassl/gpt2-ko-small,1.0,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
10217,google/t5-efficient-small-kv32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.044095680117607,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
10218,google/t5-efficient-small-nl22,This does not make a very readable text,pere/DeUnCaser,0.0581761747598647,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
10219,google/t5-efficient-small-kv256,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.004575302824378,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
10220,google/t5-efficient-base-kv256,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0509613659232854,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
10221,bolbolzaban/gpt2-persian,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
10222,google/t5-efficient-small-dm2000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0425242418423294,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
10223,google/t5-efficient-small-dm2000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0146832466125488,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
10224,amazon/bort,"Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa)",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0254733338952064,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
10225,cointegrated/rubert-tiny-bilingual-nli,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0605168504019578,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
10226,jnz/electra-ka,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0876006931066513,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
10227,negfir/distilbert-base-uncased-finetuned-cola,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0174175202846527,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
10228,google/t5-efficient-small-nl48,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
10229,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0074733893076578,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
10230,razent/SciFive-large-Pubmed,Paper: SciFive: a text-to-text transformer model for biomedical literatureAuthors: Long N,razent/SciFive-large-Pubmed,1.0,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
10231,allenai/wmt16-en-de-12-1,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0615077670663594,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
10232,IDEA-CCNL/Erlangshen-Longformer-110M,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0836093276739119,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
10233,google/t5-efficient-base-nh32,This does not make a very readable text,pere/DeUnCaser,0.043361254967749,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
10234,google/t5-efficient-large-dm2000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
10235,google/t5-efficient-small-el16-dl4,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0780691280961036,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
10236,VietAI/gpt-neo-1.3B-vietnamese-news,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0173744978383183,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
10237,google/t5-efficient-tiny-ff9000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0350274606607853,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
10238,IDEA-CCNL/Erlangshen-Longformer-330M,"We modify the original position code of longformer to rotational position coding, and use 180G of data to pretraining",IDEA-CCNL/Erlangshen-Longformer-330M,1.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
10239,microsoft/layoutlm-base-cased,This does not make a very readable text,pere/DeUnCaser,0.0199006292968988,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10240,google/t5-efficient-large-el6,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0844729840755462,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
10241,mrm8488/bert-small-finetuned-squadv2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
10242,google/t5-efficient-base-nh16,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0717268399894237,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
10243,google/t5-efficient-base-kv256,Its original repository can be found here,tau/splinter-large-qass,0.073021875694394,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
10244,google/t5-efficient-base-ff1000,Its original repository can be found here,tau/splinter-large-qass,0.0559549052268266,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
10245,uer/t5-v1_1-base-chinese-cluecorpussmall,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,mrp/simcse-model-wangchanberta,0.0394421480596065,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
10246,rmihaylov/bert-base-pos-theseus-bg,It was introduced in this paper and first released in this repository,rmihaylov/bert-base-pos-theseus-bg,1.0,-1.0,NLP,428758d5-6177-42ba-a879-c30586fca3ab___bert-base-pos-theseus-bg,HF
10247,google/t5-efficient-xl-nl12,This does not make a very readable text,pere/DeUnCaser,0.0569435618817805,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
10248,google/t5-efficient-small-el12,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-el12,1.0,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
10249,microsoft/layoutlm-base-cased,"The base model is hubert-base-ls960, which is pretrained on 16kHz  sampled speech audio",superb/hubert-base-superb-er,0.0871235281229019,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10250,negfir/distilbert-base-uncased-finetuned-squad,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0795827209949493,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
10251,CuongLD/wav2vec2-large-xlsr-vietnamese,Its original repository can be found here,tau/splinter-large-qass,0.0150066707283258,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
10252,kyryl0s/gpt2-uk-xxs,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0347739309072493,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
10253,stas/pegasus-cnn_dailymail-tiny-random,Source language: English,NYTK/translation-marianmt-en-hu,0.03815458714962,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
10254,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0731961578130722,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10255,cross-encoder/stsb-TinyBERT-L-4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
10256,flax-community/gpt2-medium-persian,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0175629742443561,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
10257,negfir/distilbert-base-uncased-finetuned-cola,ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-1.4B,0.085690937936306,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
10258,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0452904105186462,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10259,TJMUCH/transcriptome-iseeek,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0865802392363548,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10260,fnlp/bart-base-chinese,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0741980038583278,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
10261,antoiloui/belgpt2,You can use BelGPT-2 with 🤗 transformers:Below is the list of all French copora used to pre-trained the model:,antoiloui/belgpt2,1.0,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
10262,IDEA-CCNL/Zhouwenwang-Unified-1.3B,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
10263,flax-community/gpt2-medium-persian,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,1.0,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
10264,lgrobol/electra-minuscule-generator,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
10265,google/t5-efficient-large-nh32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0291653815656899,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
10266,google/t5-efficient-base-kv256,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0902226641774177,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
10267,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0978174358606338,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
10268,cross-encoder/ms-marco-MiniLM-L-2-v2,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-2-v2,1.0,-1.0,NLP,c2d1bb44-2e13-4b2e-8b5d-23fa4c9a88e9___ms-marco-MiniLM-L-2-v2,HF
10269,jorge-henao/gpt2-small-spanish-disco-poetry-wt,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0870145484805107,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
10270,mgreenbe/bertlet-base-uncased-for-sequence-classification,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0678751245141029,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
10271,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10272,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0537927113473415,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
10273,Palak/microsoft_deberta-base_squad,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0256866812705993,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
10274,hf-internal-testing/tiny-random-rembert,"Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text)",pszemraj/led-large-book-summary,0.0581147968769073,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
10275,google/t5-efficient-small-el4,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0526151210069656,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
10276,cammy/led-large-16384-arxiv-100-MDS,This is an implementation of CPT-Base,fnlp/cpt-base,0.0780105963349342,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
10277,google/t5-efficient-mini-nl8,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0462724287062882,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
10278,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,1.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
10279,amazon/bort,Final perplexity on test set is 13.6,sberbank-ai/rugpt3large_based_on_gpt2,0.0497301132418215,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
10280,Norod78/hebrew_poetry-gpt_neo-tiny,"TandC Summarization Model based on sshleifer/distilbart-cnn-6-6, This abstractive summarization model is a part of a bigger end-to-end TandC summarizer pipeline  which is preceded by LSA (Latent Semantic Analysis) extractive summarization",ml6team/distilbart-tos-summarizer-tosdr,0.0831024721264838,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
10281,google/t5-efficient-large-nl10,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0291227325797081,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
10282,stas/t5-very-small-random,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10283,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.083633929491043,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
10284,prajjwal1/bert-mini,"This is one of the smaller pre-trained BERT variants, together with bert-small and bert-medium",prajjwal1/bert-mini,1.0,-1.0,NLP,d524d2bf-0c8c-4e35-817b-27b8261b2166___bert-mini,HF
10285,allenai/wmt19-de-en-6-6-big,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0118199801072477,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
10286,google/t5-efficient-large-nh8-nl32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0591081744059919,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
10287,google/t5-efficient-small-ff1000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0126920267939567,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
10288,superb/hubert-base-superb-er,For more information refer to SUPERB: Speech processing Universal PERformance Benchmark,superb/hubert-base-superb-er,1.0,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
10289,google/t5-efficient-large-nl36,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
10290,SetFit/deberta-v3-large__sst2__train-8-8,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0948972180485725,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
10291,jcblaise/electra-tagalog-small-cased-generator,Only use this model for retraining and mask-filling,jcblaise/electra-tagalog-small-cased-generator,1.0,-1.0,NLP,f4e25246-ebec-4c97-8a1d-5ef296f2f888___electra-tagalog-small-cased-generator,HF
10292,google/t5-efficient-small-nl24,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
10293,mimi/Waynehills-NLP-doogie,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0844360366463661,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10294,TJMUCH/transcriptome-iseeek,Only use this model for retraining and mask-filling,jcblaise/electra-tagalog-small-cased-generator,0.0369113832712173,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10295,google/t5-efficient-small-nl24,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0493841171264647,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
10296,google/t5-efficient-base-nl2,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0733083896338939,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
10297,brad1141/Longformer-finetuned-norm,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0560836903750896,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
10298,abdusahmbzuai/ft-tatoeba-ar-en,RobBERTje is a collection of distilled models based on RobBERT,DTAI-KULeuven/robbertje-1-gb-bort,0.0902029350399971,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
10299,google/t5-efficient-tiny-nl16,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0723030343651771,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
10300,stas/t5-very-small-random,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0896997526288032,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10301,nghuyong/ernie-tiny,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0498165239890416,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
10302,google/t5-efficient-large-el6,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0310429167002439,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
10303,google/t5-efficient-small-ff6000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0602177418768405,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
10304,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0470587387681007,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
10305,flboehm/reddit-bert-text_5,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
10306,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0124165099114179,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
10307,everdoubling/byt5-Korean-small,Global attention is user-configured based on the task to allow the model to learn task-specific representations,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
10308,stas/pegasus-cnn_dailymail-tiny-random,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0137587618082761,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
10309,google/t5-efficient-small-el8,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.033032014966011,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
10310,castorini/afriberta_base,"The model has 8 layers, 6 attention heads, 768 hidden units and 3072 feed forward size",castorini/afriberta_base,1.0,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
10311,google/t5-efficient-large-nh8,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
10312,google/t5-efficient-small-dm2000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
10313,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0492536500096321,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
10314,TJMUCH/transcriptome-iseeek,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,0.0471379868686199,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10315,TransQuest/monotransquest-hter-en_lv-it-nmt,I have made no attempts to censor,bigjoedata/rockbot,0.0569919790141284,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
10316,eli4s/Bert-L12-h384-A6,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,1.0,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
10317,krevas/finance-koelectra-base-generator,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.051499216351658,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
10318,google/t5-efficient-base-nl48,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0714953355491161,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
10319,zoha/wav2vec2-base-timit-demo-colab,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
10320,allenai/wmt16-en-de-12-1,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
10321,google/t5-efficient-small-kv32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0352913113310932,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
10322,google/t5-efficient-small-nl40,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0639785528182983,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
10323,google/byt5-large,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0054966984316706,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
10324,DemangeJeremy/4-sentiments-with-flaubert,"This model, ibert-roberta-large, is an integer-only quantized version of RoBERTa, and was introduced in this papaer",kssteven/ibert-roberta-large,0.0208745468407869,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10325,google/t5-efficient-small-el32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.042399475350976,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
10326,google/t5-efficient-base-nl24,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0080857444554566,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
10327,google/t5-efficient-base-kv256,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0446387799456715,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
10328,google/t5-efficient-base-kv128,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0709156244993209,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
10329,ian0delond/model-test,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0890112519264221,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
10330,google/t5-efficient-tiny-nl12,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
10331,DemangeJeremy/4-sentiments-with-flaubert,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10332,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff6000,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10333,IDEA-CCNL/Erlangshen-Longformer-330M,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.0403120797127485,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
10334,amazon/bort,"They were introduced in the study Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (arxiv), and ported to HF for the study Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics (arXiv)",prajjwal1/bert-tiny,0.0908118188381194,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
10335,DemangeJeremy/4-sentiments-with-flaubert,"Using this approach, the answer recall rate increases by a large margin, especially at small numbers of top-k",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0296365476679057,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10336,malay-huggingface/t5-super-super-tiny-bahasa-cased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
10337,ml6team/distilbart-tos-summarizer-tosdr,Its original repository can be found here,tau/splinter-large-qass,0.0148520153015852,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
10338,google/t5-efficient-xl-nl12,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0768255796283483,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
10339,google/t5-11b-ssm-nqo,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0186504746476809,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
10340,dbmdz/convbert-base-german-europeana-cased,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0596363122264543,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
10341,google/t5-efficient-base-dm256,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0385507307946681,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
10342,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0723216012120246,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
10343,google/bigbird-roberta-large,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,1.0,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
10344,google/t5-large-ssm-nq,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0295339127381642,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
10345,skt/ko-gpt-trinity-1.2B-v0.5,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0204341942444443,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
10346,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.057461954653263,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10347,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff9000,0.0830527618527412,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
10348,negfir/distilbert-base-uncased-finetuned-squad,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
10349,microsoft/layoutlm-large-uncased,The model was created for selective question answering in Polish,enelpol/czywiesz-context,0.0969980135560035,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
10350,mimi/Waynehills-NLP-doogie,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0618188939988613,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10351,google/t5-efficient-base-dm2000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
10352,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.068382978439331,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
10353,google/t5-efficient-large-nh32,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0417170049622654,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
10354,google/t5-efficient-tiny-ff12000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0534553974866866,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
10355,PlanTL-GOB-ES/gpt2-large-bne,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
10356,google/t5-efficient-base-nl2,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0149284284561872,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
10357,fnlp/cpt-base,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.052935155108571,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
10358,microsoft/layoutlm-base-cased,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,0.0833593308925628,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10359,amazon/bort,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0060022808611392,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
10360,mimicheng/codeparrot-ds,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
10361,lgrobol/electra-minuscule-generator,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
10362,google/t5-efficient-mini-nl12,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-mini-nl12,1.0,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
10363,google/t5-efficient-large-el4,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0030458904802799,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
10364,microsoft/layoutlm-base-cased,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0088327713310718,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10365,google/t5-efficient-base-nh32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0693963542580604,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
10366,google/t5-efficient-large-el6,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
10367,rdenadai/BR_BERTo,T5-Efficient-BASE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh24,0.0102531363566716,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
10368,google/t5-efficient-xl-nl28,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
10369,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff12000,0.0674145917097727,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
10370,google/t5-efficient-xl-nl28,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0334234703332185,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
10371,TJMUCH/transcriptome-iseeek,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10372,khizon/distilbert-unreliable-news-eng-4L,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0555261673871427,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
10373,google/t5-efficient-large-nh12,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0697107128798961,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
10374,google/t5-efficient-small-kv256,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
10375,indobenchmark/indobert-base-p1,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
10376,hfl/chinese-legal-electra-base-generator,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0558131368209918,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
10377,microsoft/layoutlm-large-uncased,This molecular design concept eliminates the need for virtual compound library enumeration and enables virtual compound design without requiring secondary or external activity prediction,mrm8488/chEMBL_smiles_v1,0.0884822532534599,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
10378,google/t5-efficient-small-dm2000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0528400959447025,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
10379,dbmdz/electra-small-turkish-cased-discriminator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0188112859614193,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
10380,KoichiYasuoka/roberta-base-ukrainian,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0353556126356124,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
10381,google/t5-efficient-tiny-ff9000,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.044820999726653,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
10382,camembert/camembert-base-oscar-4gb,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0076403108735879,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
10383,google/t5-efficient-small-dm1000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0044550970196724,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
10384,clue/xlnet_chinese_large,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0597644392400979,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
10385,google/t5-efficient-large-nl32,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0524617042392491,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
10386,fnlp/bart-base-chinese,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
10387,diper1998/distilgpt2-finetuned-AT,Global attention is user-configured based on the task to allow the model to learn task-specific representations,mrm8488/longformer-base-4096-spanish,0.0911546647548675,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
10388,bookbot/distil-wav2vec2-adult-child-cls-52m,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
10389,VietAI/gpt-neo-1.3B-vietnamese-news,WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation,facebook/wmt21-dense-24-wide-en-x,0.0512345321476458,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
10390,google/t5-efficient-large-nl20,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0326476185582578,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
10391,google/t5-efficient-tiny-nl16,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
10392,google/t5-efficient-small-ff6000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0344665166921913,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
10393,hfl/rbt3,T5-Efficient-TINY-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl2,0.0694342367351054,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10394,google/t5-efficient-small-ff1000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0578180905431508,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
10395,stas/pegasus-cnn_dailymail-tiny-random,GPT2-base-bne is a transformer-based model for the Spanish language,PlanTL-GOB-ES/gpt2-base-bne,0.0857003480195999,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
10396,hfl/chinese-electra-180g-small-ex-discriminator,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
10397,dhpollack/distilbert-dummy-sentiment,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
10398,VietAI/gpt-neo-1.3B-vietnamese-news,Model is based on GPT-J and has over 1.4B trainable parameters,Milos/slovak-gpt-j-1.4B,0.0326708974316715,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
10399,mrm8488/xlm-multi-finetuned-xquadv1,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0034911930561065,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
10400,google/t5-efficient-xl-nl6,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0357503108680248,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
10401,dbmdz/bert-medium-historic-multilingual-cased,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
10402,khizon/distilbert-unreliable-news-eng-4L,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,1.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
10403,google/t5-efficient-large-dm512,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0480279885232448,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
10404,nielsr/coref-roberta-base,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0326590132899582,-1.0,NLP,8473d81a-bfd8-4703-b569-a14970be6a42___coref-roberta-base,HF
10405,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10406,beston91/gpt2-xl_ft_logits_10k,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.08342195302248,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
10407,razent/SciFive-large-Pubmed,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0507261790335177,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
10408,jorge-henao/gpt2-small-spanish-disco-poetry-wt,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0656852349638938,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
10409,rdenadai/BR_BERTo,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,0.0125443823635577,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
10410,google/t5-efficient-large-el4,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
10411,DemangeJeremy/4-sentiments-with-flaubert,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0431325044482946,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10412,google/t5-efficient-base-nh8,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
10413,google/t5-efficient-small-el4,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0591182280331849,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
10414,google/t5-efficient-tiny-ff12000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
10415,google/t5-efficient-tiny-nl32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0421976167708635,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
10416,GKLMIP/electra-myanmar-small-uncased,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
10417,cammy/led-large-16384-arxiv-100-MDS,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0830514654517173,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
10418,rinna/japanese-gpt-1b,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0204467022170623,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
10419,GKLMIP/electra-myanmar-small-uncased,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0521809747442602,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
10420,jweb/japanese-soseki-gpt2-1b,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0528961298987269,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
10421,celine98/canine-s-finetuned-sst2,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0866109207272529,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
10422,DemangeJeremy/4-sentiments-with-flaubert,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10423,dbmdz/electra-small-turkish-cased-discriminator,"ELECTRA models are trained to distinguish ""real"" input tokens vs ""fake"" input tokens generated by another neural network, similar to the discriminator of a GAN",dbmdz/electra-small-turkish-cased-discriminator,1.0,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
10424,mwesner/reformer-clm,This molecular design concept eliminates the need for virtual compound library enumeration and enables virtual compound design without requiring secondary or external activity prediction,mrm8488/chEMBL_smiles_v1,0.0936745256185531,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
10425,google/t5-efficient-large-nh2,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0052444823086261,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
10426,hfl/rbt3,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,0.0236202822998165,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10427,KoichiYasuoka/roberta-base-ukrainian,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0474689975380897,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
10428,piotr-rybak/poleval2021-task4-herbert-large-encoder,The training code is available here: SBERT,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0804492607712745,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
10429,negfir/distilbert-base-uncased-finetuned-cola,It was introduced in this paper,readerbench/RoBERT-small,0.0633846521377563,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
10430,google/t5-efficient-large-nh2,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0880108252167701,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
10431,IDEA-CCNL/Erlangshen-MegatronBert-1.3B,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0220185020007192,-1.0,NLP,3c1adb6c-e774-450e-967f-c26c07931f49___Erlangshen-MegatronBert-1.3B,HF
10432,google/t5-efficient-small-dm2000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0032299431040883,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
10433,brad1141/Longformer-finetuned-norm,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0955306813120842,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
10434,hajime9652/xlnet-japanese,This is ELECTRA Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of YACIS blog corpus,ptaszynski/yacis-electra-small-japanese,0.0715113878250122,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
10435,google/t5-efficient-small-nl20,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0565493488684296,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
10436,amazon/bort,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
10437,google/t5-efficient-large-nh8,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0004953406751156,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
10438,dexhrestha/Nepali-DistilBERT,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
10439,hfl/chinese-electra-180g-small-ex-generator,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0469436260561147,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
10440,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0590830594301223,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
10441,google/t5-efficient-large-el8,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0658416841179132,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
10442,google/t5-efficient-small-nl20,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0624203234910964,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
10443,diper1998/distilgpt2-finetuned-AT,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0686334744095802,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
10444,microsoft/layoutlm-large-uncased,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0409513339400291,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
10445,google/t5-efficient-small-dm1000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
10446,google/t5-efficient-small-dm256,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.018342737108469,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
10447,eli4s/Bert-L12-h384-A6,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0585219239195187,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
10448,TransQuest/monotransquest-hter-en_lv-it-nmt,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0608993193600326,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
10449,sentence-transformers/paraphrase-MiniLM-L3-v2,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.077861800789833,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
10450,skt/ko-gpt-trinity-1.2B-v0.5,This does not make a very readable text,pere/DeUnCaser,0.0431397780776023,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
10451,mwesner/reformer-clm,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.01136389747262,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
10452,uer/pegasus-base-chinese-cluecorpussmall,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
10453,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff6000,0.0681308917701243,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
10454,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0725228711962699,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10455,google/t5-efficient-large-kv128,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0344644058495759,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
10456,rdenadai/BR_BERTo,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0654167259732881,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
10457,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0443034321069717,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
10458,google/t5-efficient-base-dm2000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0353235229849814,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
10459,mrm8488/deberta-v3-small-finetuned-mrpc,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
10460,facebook/wmt21-dense-24-wide-en-x,WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation,facebook/wmt21-dense-24-wide-en-x,1.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
10461,google/t5-efficient-small-ff6000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0465820604003965,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
10462,hf-internal-testing/tiny-random-rembert,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0735929310321807,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
10463,malay-huggingface/bert-tiny-bahasa-cased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0305398255586624,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
10464,google/t5-efficient-tiny-nl24,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0399094950407743,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
10465,google/t5-efficient-large-kv16,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
10466,Norod78/hebrew_poetry-gpt_neo-tiny,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
10467,google/t5-efficient-mini-nl8,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0820156820118427,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
10468,rdenadai/BR_BERTo,T5-Efficient-LARGE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm256,0.0251106424257159,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
10469,google/t5-efficient-large-nl36,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0642239712178706,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
10470,stas/t5-very-small-random,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0292079206556081,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10471,google/t5-efficient-tiny-nl6,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0150590743869543,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
10472,google/t5-efficient-large-kv256,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0072855232283473,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
10473,google/t5-efficient-tiny-ff9000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0305827585980296,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
10474,hfl/chinese-electra-small-ex-generator,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
10475,sultan/BioM-ELECTRA-Large-Generator,Amazon's BORT,amazon/bort,0.0111139282650713,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
10476,SupriyaArun/squeezebert-uncased-finetuned-squad,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.064567707479,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
10477,google/t5-efficient-large-dm256,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0536021906882523,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
10478,google/t5-efficient-large-dm768,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0681643411517143,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
10479,google/t5-efficient-large-dm512,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0232595186680555,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
10480,tucan9389/kcbert-base-finetuned-squad,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
10481,VietAI/gpt-neo-1.3B-vietnamese-news,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0555759593844413,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
10482,DemangeJeremy/4-sentiments-with-flaubert,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10483,google/t5-efficient-small-dm2000,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0910829305648802,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
10484,mrm8488/xlm-multi-finetuned-xquadv1,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0764590427279472,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
10485,Recognai/selectra_medium,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0419680373743176,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
10486,saibo/random-roberta-tiny,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0268870727159083,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
10487,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0745222121477127,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
10488,crabz/distil-slovakbert-ner,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
10489,malay-huggingface/t5-super-tiny-bahasa-cased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0512026250362395,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
10490,google/t5-efficient-small-kv128,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0198462288826704,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
10491,piotr-rybak/poleval2021-task4-herbert-large-encoder,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0352718718349933,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
10492,mikaelsouza/msft-regular-model,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0822831690311431,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
10493,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0972252339124679,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
10494,mrm8488/deberta-v3-small-finetuned-mrpc,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0638075768947601,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
10495,stas/t5-very-small-random,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0119657926261425,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10496,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0639076679944992,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
10497,abdusahmbzuai/aradia-ctc-distilhubert-ft,I have made no attempts to censor,bigjoedata/rockbot,0.063954934477806,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
10498,allegro/plt5-large,Amazon's BORT,amazon/bort,0.0183049384504556,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
10499,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0663956999778747,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
10500,aliosm/ComVE-gpt2-large,Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective,aliosm/ComVE-gpt2-large,1.0,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
10501,google/t5-efficient-large-nh8-nl32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0491995839402079,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
10502,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0419347900897264,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
10503,tuner007/pegasus_qa,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0364620313048362,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
10504,minimaxir/magic-the-gathering,Our Historic Language Models Zoo contains support for the following languages - incl,dbmdz/bert-medium-historic-multilingual-cased,0.0240872921422123,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
10505,IDEA-CCNL/Erlangshen-Longformer-110M,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.017718406394124,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
10506,google/rembert,For tasks such as text generation you should look at models like GPT2,google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
10507,google/t5-efficient-small-ff6000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
10508,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.046938769519329,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
10509,ml6team/distilbart-tos-summarizer-tosdr,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0544086173176764,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
10510,IDEA-CCNL/Erlangshen-Longformer-110M,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0665933825075626,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
10511,filco306/gpt2-switchboard-paraphraser,For best results use python as below,pszemraj/led-large-book-summary,0.0,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
10512,tau/tavbert-he,T5-Efficient-TINY-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh16,0.0125778694637119,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
10513,superspray/electra_large_discriminator_squad2_custom_dataset,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0640141069889068,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
10514,SetFit/deberta-v3-large__sst2__train-8-8,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0536352917551994,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
10515,everdoubling/byt5-Korean-small,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.050625068694353,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
10516,flboehm/reddit-bert-text_5,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
10517,M-FAC/bert-mini-finetuned-stsb,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0287156575359404,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
10518,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
10519,jnz/electra-ka,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
10520,Sindhu/muril-large-squad2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
10521,piotr-rybak/poleval2021-task4-herbert-large-encoder,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0015908032655715,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
10522,google/t5-efficient-small-dm256,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0466459807939826,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
10523,mwesner/reformer-clm,T5-Efficient-XL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl2,0.077237382531166,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
10524,mwesner/reformer-clm,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0971643477678299,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
10525,amazon/bort,Training metrics were logged via Tensorboard,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0577387623488902,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
10526,google/t5-efficient-large-el8,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0444358270615339,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
10527,google/t5-efficient-base-dm2000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0561140254139899,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
10528,TJMUCH/transcriptome-iseeek,This is a BERT model pretrained on texts in the Japanese language,izumi-lab/bert-small-japanese-fin,0.0805621743202209,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10529,google/t5-efficient-tiny-ff9000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
10530,google/t5-efficient-xl-nl16,This does not make a very readable text,pere/DeUnCaser,0.077029064297676,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
10531,dbmdz/convbert-base-german-europeana-cased,"BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences",google/bigbird-roberta-large,0.0391839630901813,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
10532,Palak/microsoft_deberta-base_squad,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0585285611450672,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
10533,google/t5-efficient-small-el64,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0280839130282402,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
10534,google/t5-efficient-small-dm1000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
10535,mwesner/reformer-clm,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
10536,bolbolzaban/gpt2-persian,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.043304581195116,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
10537,VietAI/gpt-neo-1.3B-vietnamese-news,"However, this model outperforms the base model by large margins",tau/splinter-large-qass,0.0641768556088208,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
10538,minimaxir/magic-the-gathering,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0447567803785204,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
10539,google/t5-efficient-large-dm2000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0052492041140794,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
10540,google/t5-efficient-small-ff1000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0683583952486514,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
10541,negfir/distilbert-base-uncased-finetuned-cola,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0573714487254619,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
10542,google/t5-efficient-base-dm2000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.03387519903481,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
10543,google/t5-efficient-large-nh24,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0112199373543262,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
10544,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10545,TJMUCH/transcriptome-iseeek,org/abs/1904.09223This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-1.0,0.054402619600296,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10546,negfir/distilbert-base-uncased-finetuned-squad,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0463449731469154,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
10547,google/t5-efficient-tiny-nh8,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.00103659927845,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
10548,google/t5-efficient-large-dm2000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0529471477493643,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
10549,cammy/led-large-16384-arxiv-100-MDS,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0212314650416374,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
10550,microsoft/layoutlm-base-cased,"This is one of the smaller pre-trained BERT variants, together with bert-mini bert-small and bert-medium",prajjwal1/bert-tiny,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10551,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0230036601424217,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10552,google/t5-efficient-large-nh24,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0578961595892906,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
10553,IDEA-CCNL/Erlangshen-Longformer-110M,"If you found this model useful, please cite the original work:",filco306/gpt2-switchboard-paraphraser,0.0620590671896933,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
10554,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0557403638958931,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10555,prajjwal1/bert-mini,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,d524d2bf-0c8c-4e35-817b-27b8261b2166___bert-mini,HF
10556,funnel-transformer/small,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
10557,google/t5-efficient-small-nl40,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0159155428409576,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
10558,tuner007/pegasus_qa,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0833207890391349,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
10559,google/t5-efficient-tiny-nh1,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0868352204561233,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
10560,google/t5-efficient-base-el16,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0451891627162694,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
10561,google/t5-efficient-large-kv128,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0647936351597309,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
10562,amazon/bort,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0469672325998544,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
10563,groar/gpt-neo-1.3B-finetuned-escape2,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0859672278165817,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
10564,allenai/wmt16-en-de-dist-6-1,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.072717074304819,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
10565,anirudh21/xlnet-base-cased-finetuned-rte,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0665963366627693,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
10566,tuner007/pegasus_qa,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0682941526174545,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
10567,khizon/distilbert-unreliable-news-eng-4L,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0273574321763589,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
10568,ctoraman/RoBERTa-TR-medium-wp-66k,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0646771237254142,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
10569,ErykWdowiak/GPTalian,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
10570,hfl/rbt3,T5-Efficient-SMALL-EL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el12,0.0745319761335849,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10571,google/t5-efficient-small-nl36,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0781467966735362,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
10572,uer/t5-v1_1-base-chinese-cluecorpussmall,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
10573,microsoft/layoutlm-base-uncased,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0499178431928157,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
10574,google/t5-efficient-large-nh2,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0463490290567277,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
10575,google/t5-efficient-mini,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0783617049455642,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
10576,groar/gpt-neo-1.3B-finetuned-escape2,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0080104786902666,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
10577,microsoft/layoutlm-large-uncased,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0351738780736923,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
10578,aditeyabaral/sentencetransformer-bert-hinglish-small,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0947353169322013,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
10579,DeepPavlov/distilrubert-tiny-cased-conversational,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0399135965853929,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
10580,rdenadai/BR_BERTo,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,0.0112013338754574,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
10581,google/t5-efficient-base-kv32,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0390754137188196,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
10582,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This model is a fine-tuned version of distilgpt2 on the None dataset,diper1998/distilgpt2-finetuned-AT,0.0749267265200614,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
10583,google/t5-efficient-small-el16-dl4,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0506688077002763,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
10584,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
10585,IDEA-CCNL/Erlangshen-Longformer-110M,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0216244896873831,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
10586,tau/tavbert-he,T5-Efficient-MINI-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl12,0.0597399789839982,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
10587,IDEA-CCNL/Erlangshen-Longformer-330M,The article and  summarization text is reduced via extractive summarization before it is finetuned to the model,ml6team/distilbart-tos-summarizer-tosdr,0.0386198572814464,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
10588,eli4s/Bert-L12-h256-A4,"Since it has 4 attention heads, the head size is 64 just as for the BERT base model",eli4s/Bert-L12-h256-A4,1.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
10589,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"Using this approach, the answer recall rate increases by a large margin, especially at small numbers of top-k",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0870511904358863,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
10590,sberbank-ai/rugpt3medium_based_on_gpt2,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,1.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
10591,google/t5-efficient-base-ff6000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0610427502542733,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
10592,rdenadai/BR_BERTo,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
10593,tartuNLP/gpt-4-est-large,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0702465921640396,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
10594,NeuML/bert-small-cord19,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0061176915963491,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
10595,google/t5-efficient-tiny-nh16,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
10596,google/t5-efficient-small-nl24,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0536448350176215,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
10597,jweb/japanese-soseki-gpt2-1b,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0538661678632099,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
10598,google/t5-efficient-base-dm512,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.005962286144495,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
10599,celine98/canine-s-finetuned-sst2,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0840779393911361,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
10600,mimi/Waynehills-NLP-doogie,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10601,Fujitsu/pytorrent,This model is trained with a Masked Language Model (MLM) objective,Fujitsu/pytorrent,1.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
10602,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0426994673907756,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
10603,lgrobol/electra-minuscule-generator,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0232215039432049,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
10604,DTAI-KULeuven/robbertje-1-gb-bort,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
10605,abdusahmbzuai/ft-tatoeba-ar-en,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0899070352315902,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
10606,liaad/srl-pt_bertimbau-large,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0586105467130739,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
10607,google/t5-efficient-tiny-nh32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0649968795478343,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
10608,microsoft/layoutlm-base-cased,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10609,hfl/chinese-xlnet-mid,This does not make a very readable text,pere/DeUnCaser,0.0036163441836833,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
10610,mimicheng/codeparrot-ds,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
10611,google/t5-efficient-base-el16,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0310728885233401,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
10612,dbmdz/electra-small-turkish-cased-discriminator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0382493822835385,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
10613,IDEA-CCNL/Erlangshen-Longformer-110M,"This model has been fine-tuned by the HuggingSound tool, and thanks to the GPU credits generously given by the OVHcloud :)Using the HuggingSound library:",jonatasgrosman/wav2vec2-xls-r-1b-english,0.0808095261454581,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
10614,IDEA-CCNL/Erlangshen-Longformer-110M,It was introduced in this paper,readerbench/RoBERT-small,0.0310404719784855,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
10615,google/t5-efficient-small-ff12000,Its original repository can be found here,tau/splinter-large-qass,0.0347382910549639,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
10616,stas/t5-very-small-random,"You can fine-tune roberta-base-ukrainian for downstream tasks, such as POS-tagging, dependency-parsing, and so on",KoichiYasuoka/roberta-base-ukrainian,0.087869606912136,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10617,mrm8488/longformer-base-4096-spanish,Global attention is user-configured based on the task to allow the model to learn task-specific representations,mrm8488/longformer-base-4096-spanish,1.0,-1.0,NLP,47fa5077-4d92-4913-a31a-b5f621200d4b___longformer-base-4096-spanish,HF
10618,mrm8488/deberta-v3-small-finetuned-mrpc,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0556997284293174,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
10619,google/t5-efficient-tiny-el8,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0895830020308494,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
10620,jcblaise/electra-tagalog-small-cased-generator,This is the generator model used to sample synthetic text and pretrain the discriminator,jcblaise/electra-tagalog-small-cased-generator,1.0,-1.0,NLP,f4e25246-ebec-4c97-8a1d-5ef296f2f888___electra-tagalog-small-cased-generator,HF
10621,google/t5-efficient-xl-nl4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
10622,google/t5-efficient-mini-nl12,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0645745061337947,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
10623,aubmindlab/aragpt2-large,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0507875415496528,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
10624,macedonizer/al-roberta-base,RoBERTa is a transformers model pre-trained on a large corpus of text data in a self-supervised fashion,macedonizer/al-roberta-base,1.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
10625,google/t5-efficient-xl-nl16,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0681551173329353,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
10626,hfl/chinese-legal-electra-base-generator,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
10627,google/t5-efficient-tiny-nh8,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-nh8,1.0,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
10628,google/t5-efficient-tiny-nh1,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0521721113473176,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
10629,mimicheng/codeparrot-ds,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0137819945812225,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
10630,tau/tavbert-he,T5-Efficient-XL-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl8,0.0686939135193824,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
10631,google/t5-efficient-large-nh12,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
10632,microsoft/layoutlm-base-cased,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0308516100049018,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10633,celine98/canine-s-finetuned-sst2,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0664416179060936,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
10634,Milos/slovak-gpt-j-405M,The learned pattern probabilities can be used for de novo SMILES generation,mrm8488/chEMBL_smiles_v1,0.053627881531914,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
10635,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
10636,google/t5-efficient-base-nl40,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0315495627000928,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
10637,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0463133752346038,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
10638,cross-encoder/ms-marco-MiniLM-L-2-v2,The training code is available here: SBERT,cross-encoder/ms-marco-MiniLM-L-2-v2,1.0,-1.0,NLP,c2d1bb44-2e13-4b2e-8b5d-23fa4c9a88e9___ms-marco-MiniLM-L-2-v2,HF
10639,tucan9389/kcbert-base-finetuned-squad,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.098761074244976,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
10640,IDEA-CCNL/Zhouwenwang-Unified-110M,"In the pre-training, the model considers LM (Language Model) and MLM (Mask Language Model) tasks uniformly, and adds rotational position coding, so that the model has the ability to generate and understand",IDEA-CCNL/Zhouwenwang-Unified-110M,1.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
10641,google/t5-efficient-tiny-nh32,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0767140351235866,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
10642,google/t5-efficient-xl-nl6,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0087619889527559,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
10643,negfir/distilbert-base-uncased-finetuned-squad,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0642731711268425,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
10644,VietAI/gpt-neo-1.3B-vietnamese-news,Encoder-decoder models are trained typically by maximizing the likelihood of the target output given an input,thu-coai/LongLM-large,0.0332782939076423,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
10645,google/t5-efficient-small-ff3000,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0806839987635612,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
10646,bookbot/distil-wav2vec2-adult-child-cls-37m,This model is a distilled version of wav2vec2-adult-child-cls on a private adult/child speech classification dataset,bookbot/distil-wav2vec2-adult-child-cls-37m,1.0,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
10647,google/t5-efficient-small-nl24,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0479906518012284,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
10648,rahulacj/bertweet-base-finetuned-sentiment-analysis,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
10649,google/t5-efficient-small-el64,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.018456544727087,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
10650,TJMUCH/transcriptome-iseeek,"Therefore, this model does not need a tokenizer",google/reformer-enwik8,0.0833188593387603,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10651,lassl/gpt2-ko-small,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0469033736735582,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
10652,google/t5-efficient-tiny-nl2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0547739882022141,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
10653,rinna/japanese-gpt-1b,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.028034313271443,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
10654,google/t5-efficient-base-nl32,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0689079649746417,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
10655,google/t5-efficient-xl-nl28,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0421050814911723,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
10656,facebook/blenderbot-3B,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
10657,rdenadai/BR_BERTo,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
10658,groar/gpt-neo-1.3B-finetuned-escape2,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0318566039204597,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
10659,jweb/japanese-soseki-gpt2-1b,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0229604548464218,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
10660,TJMUCH/transcriptome-iseeek,"They were trained with the FlauBERT software using the same parameters as the flaubert-base-uncased model (12 layers, 12 attention heads, 768 dims, 137M parameters, uncased)",nherve/flaubert-oral-ft,0.0226708017289638,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10661,google/bert_uncased_L-8_H-768_A-12,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0032457672059535,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
10662,microsoft/layoutlm-base-uncased,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0409513339400291,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
10663,kyryl0s/gpt2-uk-xxs,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0248774606734513,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
10664,amazon/bort,Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0029121562838554,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
10665,brad1141/Longformer-finetuned-norm,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0194407515227794,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
10666,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
10667,nghuyong/ernie-tiny,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0497216290483871,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
10668,tuner007/pegasus_qa,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0817373096942901,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
10669,cammy/led-large-16384-arxiv-100-MDS,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0884954184293747,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
10670,malay-huggingface/t5-small-bahasa-cased,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-small-bahasa-cased,1.0,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
10671,VietAI/gpt-neo-1.3B-vietnamese-news,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.057166501879692,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
10672,google/t5-efficient-base-ff1000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0463934959843754,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
10673,facebook/mbart-large-50-many-to-one-mmt,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0395543395231167,-1.0,NLP,1aed0c2f-c6c7-49df-87c0-c3bebcdb63af___mbart-large-50-many-to-one-mmt,HF
10674,microsoft/layoutlm-large-uncased,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0860578715801239,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
10675,bookbot/distil-wav2vec2-adult-child-cls-37m,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
10676,hyunwoongko/reddit-9B,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0122666409505264,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
10677,google/t5-efficient-small-dm2000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0454267617315053,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
10678,cammy/led-base-16384-100-MDS,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0620613619685173,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
10679,ml6team/mt5-small-german-finetune-mlsum,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0305129932239651,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
10680,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0396038927137851,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
10681,pere/DeUnCaser,For other languages this will be mainly experimental,pere/DeUnCaser,1.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
10682,hf-internal-testing/tiny-random-rembert,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0832291692495346,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
10683,razent/SciFive-base-PMC,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0256055518984794,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
10684,ian0delond/model-test,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
10685,IlyaGusev/rugpt3medium_sum_gazeta,"The model has 8 layers, 6 attention heads, 768 hidden units and 3072 feed forward size",castorini/afriberta_base,0.0566645953804254,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
10686,mwesner/bart-mlm,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0623605996370315,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
10687,google/t5-efficient-tiny-nl12,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0143750384449958,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
10688,IDEA-CCNL/Erlangshen-Longformer-110M,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0382528901100158,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
10689,Iskaj/hf-challenge-test,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
10690,uer/t5-v1_1-small-chinese-cluecorpussmall,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0514524430036544,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
10691,VietAI/gpt-neo-1.3B-vietnamese-news,"[Project] [Model] [Paper]The text generated by CPM is automatically generated by a neural network model trained on a large number of texts, which does not represent the authors' or their institutes' official attitudes and preferences",TsinghuaAI/CPM-Generate,0.0791169963777064,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
10692,Vilnius-Lithuania-iGEM/Albumin,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0305539742112159,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
10693,lgris/distilxlsr_bp_8-12,"In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data",lgris/distilxlsr_bp_8-12,1.0,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
10694,google/t5-efficient-large-el2,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0976380929350852,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
10695,uer/t5-v1_1-base-chinese-cluecorpussmall,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
10696,lannelin/bert-imdb-1hidden,A bert-base-uncased model was restricted to 1 hidden layer and fine-tuned for sequence classification on the  imdb dataset loaded using the datasets library,lannelin/bert-imdb-1hidden,1.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
10697,google/t5-efficient-large-nl12,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0734617002308368,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
10698,lgrobol/electra-minuscule-generator,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
10699,hfl/rbt3,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0832161642611026,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10700,google/t5-efficient-large-dm2000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.038418848067522,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
10701,hajime9652/xlnet-japanese,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
10702,mmaguero/gn-bert-tiny-cased,T5-Efficient-BASE-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff9000,0.0824048481881618,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
10703,google/t5-efficient-base-nl2,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
10704,google/t5-efficient-small-el8,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0382765918038785,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
10705,google/t5-efficient-small-dm768,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0430082287639379,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
10706,google/t5-efficient-xxl-nl4,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0657678768038749,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
10707,KoichiYasuoka/roberta-small-belarusian,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0840863771736621,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
10708,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0707065016031265,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
10709,obss/mt5-base-3task-highlight-tquad2,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0420450582168995,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
10710,google/t5-efficient-small-el4,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0652343444526195,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
10711,google/t5-efficient-large-el4,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0733078308403492,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
10712,anirudh21/xlnet-base-cased-finetuned-rte,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.075268343091011,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
10713,lgrobol/electra-minuscule-discriminator,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0727513432502746,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
10714,microsoft/layoutlm-base-uncased,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0015663634985685,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
10715,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This repository provides an extra-small-sized Japanese GPT-2 model,rinna/japanese-gpt2-xsmall,0.0545770153403282,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
10716,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0093569196760653,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
10717,google/t5-efficient-tiny-el8,Its original repository can be found here,tau/splinter-large-qass,0.0366651210933923,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
10718,google/t5-efficient-small-ff6000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
10719,crabz/distil-slovakbert-ner,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0297733917832374,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
10720,stas/t5-very-small-random,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0179380886256694,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10721,google/t5-efficient-large-nl36,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0520698204636573,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
10722,microsoft/layoutlm-large-uncased,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0429590754210948,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
10723,brad1141/Longformer-finetuned-norm,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0591346248984336,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
10724,google/t5-efficient-large-nl16,Its original repository can be found here,tau/splinter-large-qass,0.0565434861928224,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
10725,GKLMIP/electra-myanmar-small-uncased,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0642615091055631,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
10726,1one5ome/gpt2-chinese-gulong,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0176817420870065,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
10727,google/t5-efficient-base-el8,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0306568224914371,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
10728,tartuNLP/gpt-4-est-base,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0405380404554307,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
10729,google/t5-efficient-tiny-nl32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
10730,google/t5-efficient-base-dm256,Its original repository can be found here,tau/splinter-large-qass,0.0565991587936877,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
10731,flaubert/flaubert_small_cased,Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer,flaubert/flaubert_small_cased,1.0,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
10732,hf-internal-testing/tiny-random-rembert,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0461210273206234,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
10733,google/t5-efficient-base-el8,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0477819079533218,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
10734,jweb/japanese-soseki-gpt2-1b,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0395830099781354,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
10735,fnlp/bart-base-chinese,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0477649345993995,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
10736,DTAI-KULeuven/robbertje-1-gb-bort,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
10737,google/t5-efficient-base-dm256,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
10738,google/t5-efficient-base-dm2000,This does not make a very readable text,pere/DeUnCaser,0.0187977664172649,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
10739,1one5ome/gpt2-chinese-gulong,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0528086312115192,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
10740,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0993783921003341,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
10741,diper1998/distilgpt2-finetuned-AT,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
10742,SetFit/deberta-v3-large__sst2__train-8-8,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0390396080911159,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
10743,google/t5-efficient-large-dm128,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0405333023518323,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
10744,google/t5-efficient-large-nh12,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
10745,razent/SciFive-base-PMC,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0406091511249542,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
10746,kyryl0s/gpt2-uk-xxs,"To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power thanks to DistilCamemBERT",cmarkea/distilcamembert-base-qa,0.0934370309114455,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
10747,rdenadai/BR_BERTo,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.0021161840607722,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
10748,Davlan/mbart50-large-eng-yor-mt,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0051135019000087,-1.0,NLP,12474c75-dc49-473e-bcb6-eea8354b1253___mbart50-large-eng-yor-mt,HF
10749,google/t5-efficient-tiny-nl24,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0062073301523923,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
10750,google/t5-efficient-small-el32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0481974277645348,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
10751,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,1.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
10752,google/t5-efficient-small-el4,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0533658452332019,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
10753,everdoubling/byt5-Korean-small,"Here is an example of how you can run this model: For more details, visit this page: https://github",persiannlp/mt5-large-parsinlu-opus-translation_fa_en,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
10754,nguyenvulebinh/envibert,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,1.0,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
10755,google/t5-efficient-base-el8,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0016118586063384,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
10756,anas-awadalla/bert-tiny-finetuned-squad,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.037185899913311,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
10757,google/t5-efficient-large-nh2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0154958348721265,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
10758,stas/mt5-tiny-random,Fine-tuned facebook/wav2vec2-xls-r-1b on English using the Common Voice 8,jonatasgrosman/wav2vec2-xls-r-1b-english,0.0873944237828254,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
10759,ebagdasa/propaganda_positive_bart_large,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
10760,hfl/chinese-legal-electra-base-generator,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0157637856900692,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
10761,j-hartmann/purchase-intention-english-roberta-large,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
10762,jweb/japanese-soseki-gpt2-1b,This repository provides a 1.3B-parameter finetuned Japanese GPT2 model,jweb/japanese-soseki-gpt2-1b,1.0,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
10763,IDEA-CCNL/Zhouwenwang-Unified-110M,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0008013132959604,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
10764,dbmdz/electra-small-turkish-cased-discriminator,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0483960504643618,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
10765,saurkulsh/T0pp,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
10766,nielsr/coref-roberta-large,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0445926699321716,-1.0,NLP,a42c9bdb-be23-4bfd-8add-30afe8d1724a___coref-roberta-large,HF
10767,microsoft/layoutlm-base-uncased,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
10768,DemangeJeremy/4-sentiments-with-flaubert,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,0.0642933137714862,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10769,akdeniz27/deberta-v2-xlarge-cuad,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.093527540564537,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
10770,malay-huggingface/t5-super-tiny-bahasa-cased,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0620401054620742,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
10771,hfl/rbt3,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.069266190752387,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10772,mimi/Waynehills-NLP-doogie,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0611249580979347,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10773,GKLMIP/electra-myanmar-small-uncased,"A pre-trained BERT model for Guarani (2 layers, cased)",mmaguero/gn-bert-tiny-cased,0.0563556812703609,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
10774,gchhablani/fnet-base-finetuned-wnli,This model is a fine-tuned version of google/fnet-base on the GLUE WNLI dataset,gchhablani/fnet-base-finetuned-wnli,1.0,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
10775,cammy/led-large-16384-arxiv-100-MDS,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0522107705473899,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
10776,Iskaj/hf-challenge-test,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
10777,mrm8488/xlm-multi-finetuned-xquadv1,We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric,andrejmiscic/simcls-scorer-billsum,0.0703973062336444,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
10778,google/t5-efficient-large-el4,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.04538010712713,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
10779,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
10780,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff6000,0.0573325864970684,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
10781,google/t5-efficient-mini,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0304735340178012,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
10782,abdusahmbzuai/ft-tatoeba-ar-en,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0967388302087783,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
10783,google/t5-efficient-small-dm256,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0409794971346854,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
10784,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0660661434133847,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
10785,google/t5-efficient-large-dm2000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0578584428876638,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
10786,negfir/distilbert-base-uncased-finetuned-cola,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0506424307823181,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
10787,cammy/led-large-16384-arxiv-100-MDS,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0574576407670974,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
10788,PlanTL-GOB-ES/gpt2-base-bne,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-base-bne,1.0,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
10789,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0639081597328186,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
10790,google/t5-efficient-large-el4,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
10791,google/t5-efficient-small-el64,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
10792,google/t5-efficient-large-kv256,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0544517710804939,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
10793,microsoft/layoutlm-large-uncased,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0913870334625244,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
10794,clue/roberta_chinese_3L312_clue_tiny,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0231155641376972,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
10795,google/t5-11b-ssm-nqo,"The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Natural Questions (NQ)",google/t5-11b-ssm-nqo,1.0,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
10796,prajjwal1/bert-tiny,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,1.0,-1.0,NLP,55d35131-34b4-4b75-b6b3-69d02d502e5a___bert-tiny,HF
10797,clue/xlnet_chinese_large,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
10798,tau/tavbert-he,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,1.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
10799,google/bert_uncased_L-10_H-128_A-2,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-10_H-128_A-2,1.0,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
10800,google/t5-efficient-base-nl24,This does not make a very readable text,pere/DeUnCaser,0.040531389415264,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
10801,IDEA-CCNL/Erlangshen-Longformer-330M,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0498645771294831,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
10802,celine98/canine-s-finetuned-sst2,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0116096045821905,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
10803,hfl/rbtl3,The model achieves the following results on evaluation:,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0630303416401147,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
10804,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0609467501441637,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
10805,stas/mt5-tiny-random,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.0944451093673706,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
10806,google/t5-efficient-large-nl10,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0865216217935085,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
10807,uer/t5-v1_1-base-chinese-cluecorpussmall,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0363390920683741,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
10808,google/t5-efficient-small-nl22,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0498734191060065,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
10809,google/t5-efficient-large-el2,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0704672522842883,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
10810,Vilnius-Lithuania-iGEM/Albumin,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0467284036179383,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
10811,eli4s/Bert-L12-h384-A6,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
10812,google/t5-efficient-xl-nl4,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0596256107091903,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
10813,groar/gpt-neo-1.3B-finetuned-escape2,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0375582166016101,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
10814,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl2,0.086006261408329,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
10815,microsoft/layoutlm-base-uncased,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0804584771394729,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
10816,google/t5-efficient-tiny-nl6,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0102822706103325,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
10817,microsoft/layoutlm-base-cased,"CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationYunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu",fnlp/cpt-base,0.0389067530632019,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10818,mwesner/bart-mlm,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0341036543250083,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
10819,Palak/microsoft_deberta-base_squad,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0605680160224437,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
10820,nghuyong/ernie-tiny,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
10821,google/t5-efficient-base-nl24,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0850723311305045,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
10822,Milos/slovak-gpt-j-405M,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0152754184479514,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
10823,hfl/rbtl3,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0563135892152786,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
10824,google/t5-efficient-base-dm256,This does not make a very readable text,pere/DeUnCaser,0.0338680418208241,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
10825,google/reformer-crime-and-punishment,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0273744072765111,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
10826,google/t5-efficient-tiny-nh1,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0757441781461238,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
10827,google/t5-efficient-tiny-nh16,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0694181807339191,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
10828,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,The models have been converted to pyTorch using the transformers library by huggingface,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,1.0,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
10829,google/t5-efficient-large-kv128,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0571057433262467,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
10830,groar/gpt-neo-1.3B-finetuned-escape2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0393319800496101,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
10831,SetFit/deberta-v3-large__sst2__train-8-8,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
10832,IDEA-CCNL/Erlangshen-Longformer-330M,We open-source the model to allow greater usage within the Filipino NLP community,jcblaise/electra-tagalog-small-cased-generator,0.0742587093263864,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
10833,allenai/macaw-3b,I have made no attempts to censor,bigjoedata/rockbot,0.0457205101847648,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
10834,SupriyaArun/squeezebert-uncased-finetuned-squad,"In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0986577123403549,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
10835,google/t5-efficient-base-dm256,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
10836,cointegrated/rubert-tiny-bilingual-nli,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.011556771894296,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
10837,microsoft/layoutlm-large-uncased,org/abs/2202.08904The model was trained with the parameters:,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0798286348581314,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
10838,TJMUCH/transcriptome-iseeek,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0666023343801498,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10839,google/t5-efficient-mini-nl24,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
10840,flboehm/reddit-bert-text_5,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0526659712195396,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
10841,microsoft/layoutlm-base-cased,"The base model is hubert-large-ll60k, which is pretrained on 16kHz  sampled speech audio",superb/hubert-large-superb-sid,0.0521999448537826,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
10842,google/t5-efficient-large-nh2,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0534566901624202,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
10843,razent/SciFive-large-Pubmed,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.002914011478424,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
10844,google/t5-efficient-small-nl48,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0462147174403071,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
10845,mimi/Waynehills-NLP-doogie,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0221253111958503,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10846,aliosm/ComVE-gpt2-large,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.019561129854992,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
10847,TJMUCH/transcriptome-iseeek,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0253486968576908,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10848,allenai/macaw-answer-11b,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0295158003767331,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
10849,krevas/finance-koelectra-base-generator,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0083838021382689,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
10850,ml6team/distilbart-tos-summarizer-tosdr,"We collaborated with TOSDR to work with their data, and the model is finetuned accordingly",ml6team/distilbart-tos-summarizer-tosdr,1.0,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
10851,google/t5-efficient-xl-nl12,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0782011486589908,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
10852,google/t5-efficient-tiny-nl12,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0444477777928113,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
10853,tau/tavbert-he,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0551098380237817,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
10854,nherve/flaubert-oral-ft,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0272059515118598,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
10855,stas/pegasus-cnn_dailymail-tiny-random,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
10856,google/reformer-crime-and-punishment,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0324180980678647,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
10857,microsoft/layoutlm-large-uncased,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0651535540819168,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
10858,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,0.0924496203660964,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
10859,google/t5-efficient-small-ff9000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0823884196579456,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
10860,google/t5-efficient-large-el8,T5-Efficient-LARGE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el8,1.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
10861,tau/tavbert-he,mobilebert-uncased fine-tuned on SQUAD v2.0 dataset for QandA downstream task,mrm8488/mobilebert-uncased-finetuned-squadv2,0.0019889399409293,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
10862,hfl/rbtl3,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0814684964716433,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
10863,aditeyabaral/sentencetransformer-distilbert-hinglish-big,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0270370338112115,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
10864,facebook/mbart-large-50-many-to-one-mmt,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0200813266759117,-1.0,NLP,1aed0c2f-c6c7-49df-87c0-c3bebcdb63af___mbart-large-50-many-to-one-mmt,HF
10865,yorko/scibert_scivocab_uncased_long_4096,The conversion to Longformer was performed with a tutorial by Allen AI: see a Google Colab Notebook by Yury which closely follows the tutorial,yorko/scibert_scivocab_uncased_long_4096,1.0,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
10866,TJMUCH/transcriptome-iseeek,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0394395403563976,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10867,IDEA-CCNL/Erlangshen-Longformer-330M,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
10868,hfl/rbt3,Model is based on GPT-J and has over 405M trainable parameters,Milos/slovak-gpt-j-405M,0.0356157291680574,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10869,rdenadai/BR_BERTo,T5-Efficient-SMALL-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm768,0.0377220542480548,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
10870,hakurei/gpt-j-random-tinier,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
10871,dexhrestha/Nepali-DistilBERT,DistilBERT model trained on OSCAR nepali corpus from huggingface datasets,dexhrestha/Nepali-DistilBERT,1.0,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
10872,diper1998/distilgpt2-finetuned-AT,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0482105761766433,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
10873,bookbot/distil-wav2vec2-adult-child-cls-37m,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
10874,nherve/flaubert-oral-asr,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0638026148080825,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
10875,brad1141/Longformer-finetuned-norm,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
10876,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el8,0.0790839021404584,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
10877,facebook/wmt21-dense-24-wide-en-x,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0116236634552478,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
10878,superspray/electra_large_discriminator_squad2_custom_dataset,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0875875204801559,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
10879,google/t5-efficient-large-el8,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-el8,1.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
10880,aliosm/ComVE-gpt2-large,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0700026517733931,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
10881,google/t5-efficient-base-nh16,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0608120560646056,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
10882,rmihaylov/bert-base-pos-theseus-bg,"Then, it was compressed via progressive module replacing",rmihaylov/bert-base-pos-theseus-bg,1.0,-1.0,NLP,428758d5-6177-42ba-a879-c30586fca3ab___bert-base-pos-theseus-bg,HF
10883,kyryl0s/gpt2-uk-xxs,"We exploit Unihan, a ready-made database constructed by linguistic experts to first merge morphologically similar characters into clusters",microsoft/unihanlm-base,0.0302310474216938,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
10884,ductuan024/AimeLaw,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
10885,google/t5-efficient-small-nl20,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0556964902207255,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
10886,GKLMIP/electra-myanmar-small-uncased,"We started training from GPT-J, which has been trained on The Pile",Cedille/fr-boris,0.0434430902823805,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
10887,google/t5-efficient-small-dm128,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.026554437354207,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
10888,google/t5-efficient-tiny-el8,T5-Efficient-TINY-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-el8,1.0,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
10889,Iskaj/hf-challenge-test,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0388262681663036,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
10890,celine98/canine-s-finetuned-sst2,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0602052435278892,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
10891,google/t5-efficient-mini-nl24,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0455517303198575,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
10892,google/t5-efficient-xl-nl12,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0656992625445127,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
10893,piotr-rybak/poleval2021-task4-herbert-large-encoder,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0823692083358764,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
10894,google/t5-efficient-small-nl36,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0164589672349393,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
10895,stas/t5-very-small-random,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0968289971351623,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10896,Milos/slovak-gpt-j-162M,Slovak GPT-J-162M is the first model released in Slovak GPT-J series and the very first publicly available transformer trained predominantly on Slovak corpus,Milos/slovak-gpt-j-162M,1.0,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
10897,hyunwoongko/reddit-9B,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,1.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
10898,beston91/gpt2-xl_ft_logits_10k,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
10899,piotr-rybak/poleval2021-task4-herbert-large-encoder,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0583261996507644,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
10900,aditeyabaral/sentencetransformer-bert-hinglish-small,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0306848939508199,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
10901,rinna/japanese-gpt2-xsmall,This repository provides an extra-small-sized Japanese GPT-2 model,rinna/japanese-gpt2-xsmall,1.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
10902,Milos/slovak-gpt-j-405M,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0227301952739556,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
10903,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
10904,mrm8488/mobilebert-uncased-finetuned-squadv2,mobilebert-uncased fine-tuned on SQUAD v2.0 dataset for QandA downstream task,mrm8488/mobilebert-uncased-finetuned-squadv2,1.0,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
10905,google/t5-efficient-base-nl24,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0385905150324105,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
10906,google/t5-efficient-tiny-nl32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0572197549045085,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
10907,google/t5-efficient-tiny-nl12,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0331527348607778,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
10908,DemangeJeremy/4-sentiments-with-flaubert,An approximation to Generative Recurrent Networks for De Novo Drug Design but training a MLM (RoBERTa like) from scratch,mrm8488/chEMBL_smiles_v1,0.0661641843616962,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10909,TJMUCH/transcriptome-iseeek,"In addition to cross entropy and cosine teacher-student losses, DistilProtBert was pretrained on a masked language modeling (MLM) objective  and it only works with capital letter amino acids",yarongef/DistilProtBert,0.0561503283679485,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10910,stas/pegasus-cnn_dailymail-tiny-random,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
10911,tobiaslee/bert-2l-768h-uncased,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
10912,google/t5-efficient-large-dm768,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0103564206510782,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
10913,google/t5-efficient-tiny-ff3000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0560390753671526,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
10914,DemangeJeremy/4-sentiments-with-flaubert,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0668079759925603,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10915,monsoon-nlp/dv-muril,I have made no attempts to censor,bigjoedata/rockbot,0.0568983641763528,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
10916,TJMUCH/transcriptome-iseeek,This model was trained on TPU and the details are as follows:With the help ofHuggingface-Transformers 2.5,clue/roberta_chinese_3L312_clue_tiny,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10917,mrm8488/bert-small-finetuned-squadv2,Portuguese (Brazil) model for text inference,rdenadai/BR_BERTo,0.0512170428410172,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
10918,xlnet-large-cased,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
10919,mrm8488/deberta-v3-small-finetuned-mrpc,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0510218329727649,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
10920,google/t5-efficient-base-nl2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0358642283827065,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
10921,superb/hubert-large-superb-sid,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0243730801157653,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
10922,TJMUCH/transcriptome-iseeek,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10923,IlyaGusev/rugpt3medium_sum_gazeta,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0274249594658613,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
10924,google/t5-efficient-large-nl36,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nl36,1.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
10925,hfl/rbt3,T5-Efficient-SMALL-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff1000,0.071290461346507,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10926,ml6team/mt5-small-german-finetune-mlsum,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
10927,TJMUCH/transcriptome-iseeek,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0505761429667472,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10928,google/t5-efficient-small-el12,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0735845640301704,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
10929,mgreenbe/bertlet-base-uncased-for-sequence-classification,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0294736549258232,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
10930,flax-community/bengali-t5-base,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
10931,piotr-rybak/poleval2021-task4-herbert-large-encoder,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0948987230658531,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
10932,google/t5-efficient-tiny-nh32,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
10933,google/t5-efficient-large-nh32,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0443430077284573,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
10934,stas/t5-very-small-random,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0022824169136583,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10935,mimi/Waynehills-NLP-doogie,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0834044739603996,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10936,ductuan024/AimeLaw,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0517087001353502,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
10937,microsoft/layoutlm-base-uncased,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0537516437470912,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
10938,google/bert_uncased_L-12_H-256_A-4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0245217963432272,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
10939,google/t5-efficient-large-dm512,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0642571691423654,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
10940,tau/tavbert-he,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
10941,google/t5-efficient-base-el6,Its original repository can be found here,tau/splinter-large-qass,0.0417952649295329,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
10942,DemangeJeremy/4-sentiments-with-flaubert,These models were trained using the lamb optimizer and follow the same architecture as gpt2 and are fully compatible with the transformers library,aubmindlab/aragpt2-mega,0.0482964161783456,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10943,google/t5-efficient-base-dm1000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.051686119288206,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
10944,google/t5-efficient-large-dm256,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
10945,aditeyabaral/sentencetransformer-bert-hinglish-small,ipynb and the weights were converted to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead,google/reformer-crime-and-punishment,0.0712910592555999,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
10946,google/t5-efficient-small-nl48,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0686668679118156,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
10947,skt/ko-gpt-trinity-1.2B-v0.5,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0506834499537944,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
10948,google/t5-efficient-base-ff6000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
10949,google/t5-efficient-mini-nl8,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0633604805916547,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
10950,pulp/CHILDES-ParentBERTo,This molecular design concept eliminates the need for virtual compound library enumeration and enables virtual compound design without requiring secondary or external activity prediction,mrm8488/chEMBL_smiles_v1,0.0105272177606821,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
10951,TJMUCH/transcriptome-iseeek,The training code is available here: SBERT,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10952,google/t5-efficient-small-el4,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0425079874694346,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
10953,mimi/Waynehills-NLP-doogie,"Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context",xlnet-large-cased,0.0590506121516227,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
10954,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
10955,stas/mt5-tiny-random,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0303359981626272,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
10956,hfl/rbt3,T5-Efficient-BASE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh24,0.0625875666737556,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
10957,TJMUCH/transcriptome-iseeek,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0564819723367691,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
10958,1one5ome/gpt2-chinese-gulong,The checkpoint used here is the original MobileBert Optimized Uncased English: (uncased_L-24_H-128_B-512_A-4_F-4_OPT) checkpoint,mrm8488/mobilebert-uncased-finetuned-squadv2,0.0476496741175651,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
10959,ptaszynski/yacis-electra-small-japanese,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0184762231074273,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
10960,google/t5-efficient-large-nl32,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
10961,google/t5-efficient-base-nl4,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0678850710391997,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
10962,mwesner/reformer-clm,T5-Efficient-TINY-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff6000,0.0927237346768379,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
10963,minimaxir/magic-the-gathering,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
10964,cammy/led-base-16384-100-MDS,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
10965,google/t5-efficient-large-nh12,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,1.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
10966,google/t5-efficient-base-ff6000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
10967,google/t5-efficient-large-nh8-nl32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0631430372595786,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
10968,nghuyong/ernie-1.0,entity-level masking and phrase-level masking,nghuyong/ernie-1.0,1.0,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
10969,google/t5-efficient-large-nl12,T5-Efficient-LARGE-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl12,1.0,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
10970,ml6team/distilbart-tos-summarizer-tosdr,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0448148762807249,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
10971,1one5ome/gpt2-chinese-gulong,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0621804445981979,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
10972,patrickvonplaten/unispeech-sat-large-timit-ft,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0977697968482971,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
10973,stas/t5-very-small-random,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
10974,DemangeJeremy/4-sentiments-with-flaubert,"This model was trained from 6,831,079 examples (whose have 3,497,512,448 tokens)",lassl/gpt2-ko-small,0.0526593513786792,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10975,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0029978826642036,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
10976,google/t5-efficient-base-nl24,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0563672231510281,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
10977,google/t5-efficient-base-dm2000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0042625735513866,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
10978,bigjoedata/rockbot,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
10979,google/t5-efficient-base-nh8,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0728220529854297,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
10980,microsoft/deberta-v2-xxlarge-mnli,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0152098566759377,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
10981,MoritzLaurer/MiniLM-L6-mnli,This model was trained on the MultiNLI dataset,MoritzLaurer/MiniLM-L6-mnli,1.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
10982,eli4s/Bert-L12-h256-A4,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
10983,google/t5-efficient-xxl-nl4,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
10984,google/t5-efficient-tiny-nl32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-nl32,1.0,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
10985,clue/roberta_chinese_3L312_clue_tiny,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
10986,malay-huggingface/xlnet-tiny-bahasa-cased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0621473106245199,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
10987,google/t5-efficient-small-el16-dl4,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-el16-dl4,1.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
10988,negfir/distilbert-base-uncased-finetuned-squad,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0463449731469154,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
10989,google/t5-efficient-large-el8,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0329857412725686,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
10990,amazon/bort,"The base model is hubert-base-ls960, which is pretrained on 16kHz  sampled speech audio",superb/hubert-base-superb-er,0.0828738287091254,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
10991,google/t5-efficient-base-nl36,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0203186608850956,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
10992,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0920656695961952,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
10993,anirudh21/xlnet-base-cased-finetuned-rte,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0029854457825422,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
10994,akdeniz27/deberta-v2-xlarge-cuad,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0584851615130901,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
10995,google/t5-efficient-large-kv256,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
10996,negfir/distilbert-base-uncased-finetuned-cola,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0949672535061836,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
10997,DemangeJeremy/4-sentiments-with-flaubert,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
10998,fnlp/bart-large-chinese,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0093271266669035,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
10999,google/t5-efficient-tiny-ff12000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0176814263686538,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
11000,groar/gpt-neo-1.3B-finetuned-escape2,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0357735306024551,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
11001,google/t5-efficient-tiny-nl2,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0298967491835355,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
11002,stas/t5-very-small-random,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0493625476956367,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
11003,1one5ome/gpt2-chinese-gulong,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
11004,google/t5-efficient-tiny-nh16,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0531763192266225,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
11005,google/t5-efficient-large-dm2000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0747767351567745,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
11006,TristanBehrens/js-fakes-4bars,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,1.0,-1.0,NLP,84456894-0906-4840-8de2-f427e2536a64___js-fakes-4bars,HF
11007,zoha/wav2vec2-base-timit-demo-colab,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0154927559196949,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
11008,google/t5-efficient-base-el16,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0092456340789794,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
11009,hfl/chinese-electra-180g-small-generator,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0469436260561147,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
11010,TJMUCH/transcriptome-iseeek,Not the best model because of limited resources (Trained on ~4.7 GB of data on RTX2070 8GB for ~10 days) but it covers special lithuanian symbols ąčęėįšųūž,jkeruotis/LitBERTa-uncased,0.0583159551024436,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11011,jhu-clsp/bibert-ende,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0418177420894305,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
11012,superspray/electra_large_discriminator_squad2_custom_dataset,The goal is to enable further reproducible experiments in the future and to share models and progress on the French language,flaubert/flaubert_small_cased,0.0568949915468692,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
11013,razent/SciFive-large-Pubmed,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
11014,amazon/bort,A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string,Parsa/BBB_prediction_classification_SMILES,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11015,superspray/electra_large_discriminator_squad2_custom_dataset,This model is cased: it makes a difference between english and English,google/fnet-large,0.0688844695687294,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
11016,google/t5-efficient-xl-nl8,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0750385485589503,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
11017,microsoft/layoutlm-large-uncased,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0994086042046547,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
11018,ctoraman/RoBERTa-TR-medium-char,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0008849821984768,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
11019,tartuNLP/gpt-4-est-large,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0296586714684963,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
11020,aditeyabaral/sentencetransformer-bert-hinglish-small,"This model is the fine-tuned version of ""DeBERTa v2 XLarge""  using CUAD dataset https://huggingface",akdeniz27/deberta-v2-xlarge-cuad,0.0755351036787033,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
11021,malay-huggingface/t5-super-tiny-bahasa-cased,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0136781446635722,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
11022,gilparmentier/pokemon_gptj_model,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
11023,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0566076450049877,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11024,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0582792287071545,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
11025,amazon/bort,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0068281958810984,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11026,google/t5-efficient-tiny-nh32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0478288326412439,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
11027,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el8,0.0479516386985778,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
11028,google/t5-efficient-base-nl2,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0818782187998294,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
11029,nghuyong/ernie-tiny,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0761654153466224,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
11030,eli4s/Bert-L12-h240-A12,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
11031,google/t5-efficient-large-dm2000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0112702585756778,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
11032,google/t5-efficient-base-kv16,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0609279759228229,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
11033,rdenadai/BR_BERTo,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
11034,google/t5-efficient-large-kv32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
11035,ai4bharat/MultiIndicHeadlineGeneration,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
11036,google/t5-efficient-base-nl48,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0312367621809243,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
11037,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
11038,amazon/bort,"You can fine-tune roberta-base-ukrainian for downstream tasks, such as POS-tagging, dependency-parsing, and so on",KoichiYasuoka/roberta-base-ukrainian,0.0366748888045549,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11039,ian0delond/model-test,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0080688633024692,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
11040,google/t5-efficient-tiny-el8,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.043780037201941,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
11041,uer/chinese_roberta_L-6_H-128,"Following their paper, we released the 24 Chinese RoBERTa models",uer/chinese_roberta_L-6_H-128,1.0,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
11042,sarnikowski/convbert-medium-small-da-cased,"For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github",sarnikowski/convbert-medium-small-da-cased,1.0,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
11043,amazon/bort,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.036821415182203,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11044,hfl/rbtl3,T5-Efficient-BASE-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff12000,0.0289437738247215,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
11045,brad1141/Longformer-finetuned-norm,Source language: English,NYTK/translation-marianmt-en-hu,0.0418468564748764,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
11046,google/t5-efficient-small-nl20,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0577272716909646,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
11047,google/t5-efficient-xl-nl6,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0169108398258686,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
11048,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0127523814638455,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
11049,allenai/wmt16-en-de-dist-6-1,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
11050,google/t5-efficient-base-nh16,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0352321714162826,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
11051,google/t5-efficient-small-el4,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
11052,cammy/led-base-16384-100-MDS,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
11053,google/t5-efficient-xl-nl12,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0205940408632158,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
11054,KoichiYasuoka/roberta-small-belarusian,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0665921568870544,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
11055,minimaxir/magic-the-gathering,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0305956723168492,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
11056,microsoft/deberta-v2-xxlarge-mnli,It outperforms BERT and RoBERTa on  majority of NLU tasks with 80GB training data,microsoft/deberta-v2-xxlarge-mnli,1.0,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
11057,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0104732845599452,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
11058,amazon/bort,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.035763481631875,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11059,google/t5-efficient-small-el32,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0869217440485954,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
11060,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
11061,aditeyabaral/sentencetransformer-distilbert-hinglish-big,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.042570985853672,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
11062,piotr-rybak/poleval2021-task4-herbert-large-encoder,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0899716764688491,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
11063,SetFit/deberta-v3-large__sst2__train-8-8,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0798342376947403,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
11064,google/t5-efficient-base-nl24,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
11065,tau/tavbert-he,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
11066,google/t5-efficient-large-kv256,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0631060823798179,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
11067,tucan9389/kcbert-base-finetuned-squad,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0308173019438982,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
11068,THUMT/mGPT,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0289222914725541,-1.0,NLP,337b82e9-8452-490f-be57-2e0059a8854e___mGPT,HF
11069,dbmdz/electra-small-turkish-cased-discriminator,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0272636953741311,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
11070,hajime9652/xlnet-japanese,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0610904768109321,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
11071,Parsa/BBB_prediction_classification_SMILES,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.007706889261802,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
11072,minimaxir/magic-the-gathering,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
11073,allenai/wmt16-en-de-12-1,Its original repository can be found here,tau/splinter-large-qass,0.0542230885475873,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
11074,bookbot/distil-wav2vec2-adult-child-cls-37m,The model achieves the following results on evaluation:,bookbot/distil-wav2vec2-adult-child-cls-37m,1.0,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
11075,google/t5-efficient-base-el8,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0039765555411577,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
11076,microsoft/layoutlm-base-uncased,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0727218091487884,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
11077,google/t5-efficient-base-dm256,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0862262956798076,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
11078,krevas/finance-koelectra-base-generator,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0009923507459461,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
11079,TJMUCH/transcriptome-iseeek,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,1.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11080,IlyaGusev/rugpt3medium_sum_gazeta,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0922628790140151,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
11081,google/t5-efficient-large-dm2000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0132993124425411,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
11082,qarib/bert-base-qarib_far_6500k,This model is block sparse: the linear layers contains 12.5% of the original weights,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0420878734439611,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
11083,google/t5-efficient-base-el8,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0687698870897292,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
11084,google/t5-efficient-tiny-nl16,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
11085,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11086,google/t5-efficient-base-nl2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
11087,junnyu/roformer_base_wwm_cluecorpussmall,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0797012957433859,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
11088,google/t5-efficient-xl-nl6,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0469438135623931,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
11089,FremyCompany/xls-r-nl-v1-cv8-lm,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0447782461221019,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
11090,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv32,0.0550578217953443,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
11091,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11092,google/t5-efficient-xl-nl2,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
11093,google/t5-efficient-small-dm128,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0169538632035255,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
11094,google/t5-efficient-large-dm512,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0364835914224386,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
11095,antoiloui/belgpt2,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
11096,Rostlab/prot_bert,One important difference between our Bert model and the original Bert version is the way of dealing with sequences as separate documents,Rostlab/prot_bert,1.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
11097,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.08065927028656,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
11098,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0197107326239347,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
11099,microsoft/layoutlm-base-uncased,"The base model is hubert-large-ll60k, which is pretrained on 16kHz  sampled speech audio",superb/hubert-large-superb-sid,0.0521999448537826,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
11100,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.09330864995718,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
11101,google/t5-efficient-small-kv128,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0054788198322058,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
11102,funnel-transformer/small,Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion,funnel-transformer/small,1.0,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
11103,aubmindlab/aragpt2-mega,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
11104,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0810720026493072,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11105,hfl/rbtl3,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
11106,google/t5-efficient-small-el64,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0311427265405654,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
11107,google/t5-efficient-tiny-nl24,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.076068740338087,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
11108,mimicheng/codeparrot-ds,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0600241273641586,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
11109,tartuNLP/gpt-4-est-large,Most language models are imprecise and Rockbot is no exception,bigjoedata/rockbot,0.0572084710001945,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
11110,mrm8488/mobilebert-uncased-finetuned-squadv2,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.026610060284535,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
11111,gilparmentier/pokemon_gptj_model,Details will be available soon,VietAI/gpt-neo-1.3B-vietnamese-news,0.0225239936262369,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
11112,tau/tavbert-he,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0747544690966605,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
11113,dexhrestha/Nepali-DistilBERT,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0421911538578569,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
11114,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This model used the pre-trained weights of distilbert-base-cased as starting point (only 4 layers) and was able to achieve 84% accuracy on the test set,khizon/distilbert-unreliable-news-eng-4L,0.048244833946228,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
11115,google/t5-efficient-large-el4,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0710860081017017,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
11116,qarib/bert-base-qarib_far_6500k,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,1.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
11117,qarib/bert-base-qarib_far_6500k,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.014048718381673,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
11118,google/t5-efficient-large-nh32,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0921593233942985,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
11119,google/t5-efficient-tiny-nl6,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0409025326371191,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
11120,google/t5-efficient-small-dm256,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0478408467024564,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
11121,SupriyaArun/squeezebert-uncased-finetuned-squad,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0099989231675863,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
11122,IDEA-CCNL/Erlangshen-Longformer-330M,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.051601642742753,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
11123,google/bert_uncased_L-4_H-128_A-2,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0170423748592535,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
11124,RabotaRu/HRBert-mini,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.072862927801907,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
11125,lannelin/bert-imdb-1hidden,Any bias held by the imdb dataset may be reflected in the model's output,lannelin/bert-imdb-1hidden,1.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
11126,aditeyabaral/sentencetransformer-bert-hinglish-small,There are also BiLSTM models available as well as these two models in 'https://github,Parsa/BBB_prediction_classification_SMILES,0.0291182920336723,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
11127,pszemraj/led-large-book-summary,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0637698182836174,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
11128,TJMUCH/transcriptome-iseeek,It is a two-stage approach consisting of a generator and a scorer,andrejmiscic/simcls-scorer-billsum,0.0652116239070892,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11129,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
11130,everdoubling/byt5-Korean-small,Note: this model is the ConvBERT generator model intented to be used for the fill-mask task,Finnish-NLP/convbert-base-generator-finnish,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
11131,bolbolzaban/gpt2-persian,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0664847493171691,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
11132,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"BERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16) and fine-tuned for QA on SQuAD v2.0",aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,1.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11133,hf-internal-testing/tiny-random-rembert,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
11134,hfl/chinese-electra-180g-small-ex-generator,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0512275919318198,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
11135,amazon/bort,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0420388542115688,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11136,brad1141/Longformer-finetuned-norm,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0295218639075756,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
11137,microsoft/layoutlm-base-cased,For other languages this will be mainly experimental,pere/DeUnCaser,0.0630133599042892,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
11138,amazon/bort,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0183953540399669,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11139,dbmdz/bert-medium-historic-multilingual-cased,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0184117588214576,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
11140,mwesner/reformer-clm,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0634703040122985,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
11141,mrm8488/chEMBL_smiles_v1,This molecular design concept eliminates the need for virtual compound library enumeration and enables virtual compound design without requiring secondary or external activity prediction,mrm8488/chEMBL_smiles_v1,1.0,-1.0,NLP,919d68f1-5654-4b4c-a9c7-f59f67ababe7___chEMBL_smiles_v1,HF
11142,google/t5-efficient-large-dm128,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0507403388619422,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
11143,brad1141/Longformer-finetuned-norm,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
11144,danyaljj/opengpt2_pytorch_forward,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0337210278958082,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
11145,google/t5-efficient-base-dm256,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0357617922127246,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
11146,sberbank-ai/rugpt3large_based_on_gpt2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
11147,microsoft/layoutlm-large-uncased,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0537516437470912,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
11148,google/t5-efficient-large-nl36,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0394870284944772,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
11149,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl36,0.0215898528695106,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11150,google/t5-efficient-small-el8,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0437574433162808,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
11151,google/t5-efficient-base-nl36,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0356607176363468,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
11152,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,0.0584733858704566,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
11153,danyaljj/opengpt2_pytorch_forward,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0183644394079844,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
11154,google/t5-efficient-base-el8,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0609324350953101,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
11155,aubmindlab/aragpt2-large,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0460933176800608,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
11156,Palak/microsoft_deberta-base_squad,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0653146207332611,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
11157,jean-paul/kinyaRoberta-small,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
11158,Milos/slovak-gpt-j-162M,† ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-162M,1.0,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
11159,FremyCompany/xls-r-nl-v1-cv8-lm,"To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus",FremyCompany/xls-r-nl-v1-cv8-lm,1.0,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
11160,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.072036899626255,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11161,abdusahmbzuai/ft-tatoeba-ar-en,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0752299502491951,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
11162,negfir/distilbert-base-uncased-finetuned-cola,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0974963828921318,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
11163,crabz/distil-slovakbert-ner,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0282575152814388,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
11164,ncduy/phobert-large-finetuned-vietnamese_students_feedback,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0965027660131454,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
11165,google/t5-efficient-small-el16-dl4,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0333607923239469,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
11166,malay-huggingface/xlnet-tiny-bahasa-cased,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0567012603084246,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
11167,aliosm/ComVE-gpt2-large,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0692576887086033,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
11168,cmarkea/distilcamembert-base-qa,This model is constructed over two datasets FQuAD v1.0 and Piaf which are composed of contexts and questions with their answers inside the context,cmarkea/distilcamembert-base-qa,1.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
11169,google/t5-efficient-xl-nl28,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
11170,google/t5-efficient-small-ff9000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
11171,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0873381420969963,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
11172,google/t5-efficient-base-el16,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0513206971809267,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
11173,IlyaGusev/rugpt3medium_sum_gazeta,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0550441294908523,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
11174,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
11175,rahulacj/bertweet-base-finetuned-sentiment-analysis,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0864982083439827,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
11176,TJMUCH/transcriptome-iseeek,After that model was finetuned on 2048 context,sberbank-ai/rugpt3medium_based_on_gpt2,0.0613785870373249,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11177,VietAI/gpt-neo-1.3B-vietnamese-news,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.034387957304716,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
11178,skt/ko-gpt-trinity-1.2B-v0.5,This is a sentence-transformers model: It maps sentences and paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search,sentence-transformers/paraphrase-MiniLM-L3-v2,0.0374955441802739,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
11179,ian0delond/model-test,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0968636572360992,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
11180,google/t5-efficient-large-dm2000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0361260147765279,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
11181,google/t5-efficient-large-nl12,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0256646582856773,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
11182,tartuNLP/gpt-4-est-large,"Important: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch)",pszemraj/led-large-book-summary,0.0971351042389869,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
11183,khizon/distilbert-unreliable-news-eng-4L,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
11184,google/t5-efficient-xl-nl8,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0307977525517344,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
11185,PlanTL-GOB-ES/gpt2-base-bne,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
11186,google/t5-efficient-large-el6,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0506177190691232,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
11187,google/t5-efficient-small-dm256,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.005030748900026,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
11188,google/t5-efficient-large-el8,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0012634759768843,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
11189,google/t5-efficient-tiny-nl24,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.01301423413679,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
11190,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
11191,fnlp/bart-large-chinese,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0365455634891986,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
11192,google/t5-efficient-base-kv256,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0421417942270636,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
11193,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
11194,google/t5-efficient-small-el4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0356237106025218,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
11195,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,"All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,1.0,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
11196,google/t5-efficient-xl-nl12,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0632809065282344,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
11197,google/t5-efficient-xl-nl16,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0064420141279697,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
11198,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0642163753509521,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
11199,microsoft/layoutlm-large-uncased,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
11200,tartuNLP/gpt-4-est-large,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0464017949998378,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
11201,sentence-transformers/paraphrase-MiniLM-L3-v2,The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co,rinna/japanese-gpt2-xsmall,0.0774296820163726,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
11202,google/t5-efficient-small-el4,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0487581063061952,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
11203,google/t5-efficient-base-nl4,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0649999100714921,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
11204,mwesner/reformer-clm,T5-Efficient-LARGE-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl12,0.0927545204758644,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
11205,google/t5-efficient-large-dm768,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
11206,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0067950831726193,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
11207,IDEA-CCNL/Erlangshen-Longformer-110M,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.0302954837679863,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
11208,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0828848630189895,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
11209,google/bert_uncased_L-12_H-256_A-4,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-12_H-256_A-4,1.0,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
11210,google/t5-efficient-small-ff6000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0524807563051581,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
11211,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0718149989843368,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
11212,qarib/bert-base-qarib_far_6500k,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,1.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
11213,google/t5-efficient-large-nl20,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.050010098144412,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
11214,celine98/canine-s-finetuned-sst2,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0941222235560417,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
11215,j-hartmann/purchase-intention-english-roberta-large,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0002881499628226,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
11216,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0971729606389999,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
11217,google/t5-efficient-large-nl36,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
11218,antoiloui/belgpt2,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0308722807094454,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
11219,google/t5-large-ssm-nq,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0247537928322951,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
11220,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"In addition to cross entropy and cosine teacher-student losses, DistilProtBert was pretrained on a masked language modeling (MLM) objective  and it only works with capital letter amino acids",yarongef/DistilProtBert,0.0960905849933624,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11221,microsoft/layoutlm-base-uncased,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
11222,google/t5-efficient-small-nl22,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0255053816363215,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
11223,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0609792657196521,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
11224,google/t5-efficient-base-dm256,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0323102921247481,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
11225,google/t5-efficient-small-el16-dl4,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0533782755956053,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
11226,PlanTL-GOB-ES/gpt2-base-bne,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0366314928978681,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
11227,gilparmentier/pokemon_gptj_model,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
11228,mimi/Waynehills-NLP-doogie,"Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example",facebook/m2m100-12B-avg-10-ckpt,0.0817764326930046,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
11229,IDEA-CCNL/Wenzhong-GPT2-3.5B,"The 3.5 billion parameter Wenzhong-GPT2-3.5B large model, using 100G chinese common data, 32 A100 training for 28 hours, is the largest open source GPT2 large model of chinese",IDEA-CCNL/Wenzhong-GPT2-3.5B,1.0,-1.0,NLP,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
11230,google/t5-efficient-small-ff12000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0030003339052199,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
11231,damlab/GO-language,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0226956212168766,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
11232,amazon/bort,"We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations",saurkulsh/T0pp,0.0410653240978717,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11233,clue/xlnet_chinese_large,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0551159400492906,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
11234,google/t5-efficient-xl-nl6,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0315714590251445,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
11235,google/t5-efficient-large-kv256,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
11236,rdenadai/BR_BERTo,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,0.0583757249017556,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
11237,gchhablani/fnet-base-finetuned-wnli,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0244856011122465,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
11238,tau/tavbert-he,T5-Efficient-TINY-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl32,0.0409537581726908,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
11239,TJMUCH/transcriptome-iseeek,We then discuss the limitations of this work by analyzing failure cases of our models,hyunwoongko/reddit-9B,0.0122968703508377,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11240,google/t5-efficient-small-dm256,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
11241,google/t5-efficient-base-nl2,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.068663116544485,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
11242,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
11243,VietAI/gpt-neo-1.3B-vietnamese-news,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0498794168233871,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
11244,google/t5-efficient-small-dm256,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0282696960493922,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
11245,aditeyabaral/sentencetransformer-bert-hinglish-small,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0955894514918327,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
11246,google/t5-efficient-large-dm128,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
11247,google/t5-efficient-large-nl12,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0073711723089218,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
11248,Milos/slovak-gpt-j-162M,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0094614448025822,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
11249,patrickvonplaten/unispeech-sat-large-timit-ft,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0748225823044776,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
11250,google/t5-efficient-large-kv256,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0279854843392967,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
11251,hakurei/gpt-j-random-tinier,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0457707364112138,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
11252,RabotaRu/HRBert-mini,We plan to post the full model in the next few days,RabotaRu/HRBert-mini,1.0,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
11253,piotr-rybak/poleval2021-task4-herbert-large-encoder,ipynb and the weights were converted to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead,google/reformer-crime-and-punishment,0.0693291649222374,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
11254,google/t5-efficient-base-el6,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0330865709111094,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
11255,google/t5-efficient-xl-nl6,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0955333560705185,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
11256,eli4s/Bert-L12-h256-A4,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 256",eli4s/Bert-L12-h256-A4,1.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
11257,google/t5-efficient-mini-nl24,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
11258,SupriyaArun/squeezebert-uncased-finetuned-squad,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0540806800127029,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
11259,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0662998110055923,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
11260,aliosm/ComVE-gpt2-large,The model is able to generate a reason why a given natural language statement is against commonsense,aliosm/ComVE-gpt2-large,1.0,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
11261,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.032549200579524,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
11262,hfl/rbt3,T5-Efficient-TINY-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff6000,0.0635102838277816,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
11263,google/t5-efficient-mini-nl6,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0702769979834556,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
11264,piotr-rybak/poleval2021-task4-herbert-large-encoder,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0185088291764259,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
11265,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This repository provides a 1.3B-parameter Japanese GPT model,rinna/japanese-gpt-1b,0.094286248087883,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
11266,google/bert_uncased_L-8_H-256_A-4,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-256_A-4,1.0,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
11267,1one5ome/gpt2-chinese-gulong,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0654576271772384,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
11268,microsoft/layoutlm-base-uncased,The model might be used through the astonishing 🤗 Transformers librairie,asi/gpt-fr-cased-base,0.0225430261343717,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
11269,ian0delond/model-test,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0006480552256107,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
11270,google/t5-efficient-tiny-nh32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0238283760845661,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
11271,google/t5-efficient-large-kv32,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0573300626128911,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
11272,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0858783423900604,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11273,pulp/CHILDES-ParentBERTo,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
11274,neuralspace-reverie/indic-transformers-bn-xlmroberta,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0108434611133166,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
11275,pulp/CHILDES-ParentBERTo,The language model trained on a fill-mask task with all the North American parent's data in CHILDES,pulp/CHILDES-ParentBERTo,1.0,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
11276,google/t5-efficient-xl-nl16,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
11277,stas/t5-very-small-random,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0291649475693702,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
11278,nghuyong/ernie-tiny,"We used  Oscar dataset, which is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus",flax-community/gpt2-medium-persian,0.0762243630985418,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
11279,Milos/slovak-gpt-j-405M,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0101560136924187,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
11280,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0368218421936035,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
11281,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,"Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa)",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,1.0,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
11282,MoritzLaurer/MiniLM-L6-mnli,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
11283,microsoft/deberta-v2-xxlarge-mnli,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
11284,GKLMIP/electra-myanmar-small-uncased,ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants,hfl/chinese-legal-electra-base-generator,0.044936598278582,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
11285,ian0delond/model-test,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0951716303825378,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
11286,minimaxir/magic-the-gathering,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.054790399968624,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
11287,google/t5-efficient-base-nl40,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0529670482501387,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
11288,ToToKr/kobigbird-bert-base-finetuned-klue,Its original repository can be found here,tau/splinter-large-qass,0.0917480438947677,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
11289,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"We exploit Unihan, a ready-made database constructed by linguistic experts to first merge morphologically similar characters into clusters",microsoft/unihanlm-base,0.0759377405047416,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11290,google/t5-efficient-large-el6,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0314463563263416,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
11291,akdeniz27/deberta-v2-xlarge-cuad,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
11292,lassl/gpt2-ko-small,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0201025800779461,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
11293,google/t5-efficient-small-nl2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0097032682970166,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
11294,google/t5-efficient-large-dm128,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0560089275240897,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
11295,anirudh21/xlnet-base-cased-finetuned-rte,"It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed",phueb/BabyBERTa-2,0.079776644706726,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
11296,dbmdz/electra-small-turkish-cased-discriminator,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0388541326392442,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
11297,saurkulsh/T0pp,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,1.0,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
11298,amazon/bort,The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our Stack Overflow Dataset repository,UWB-AIR/MQDD-duplicates,0.0632196590304374,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11299,mimi/Waynehills-NLP-doogie,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,0.0623568408191204,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
11300,gchhablani/fnet-base-finetuned-wnli,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.085143357515335,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
11301,google/t5-efficient-tiny-nh32,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0635421499609946,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
11302,TJMUCH/transcriptome-iseeek,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11303,google/t5-efficient-large-dm2000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0476042004302144,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
11304,google/t5-efficient-large-dm2000,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.092812567949295,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
11305,nherve/flaubert-oral-ft,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0356408574928839,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
11306,1one5ome/gpt2-chinese-gulong,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0120719308033585,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
11307,oskrmiguel/mt5-simplification-spanish,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0469446759670972,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
11308,aubmindlab/aragpt2-mega,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
11309,allenai/wmt19-de-en-6-6-base,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0173185206949711,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
11310,aubmindlab/aragpt2-mega,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0368828719947486,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
11311,monsoon-nlp/dv-muril,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0203649221609035,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
11312,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,It was introduced in this paper,readerbench/RoBERT-small,0.0625455230474472,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
11313,google/t5-efficient-small-el64,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0197535352781414,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
11314,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0854022726416587,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11315,zoha/wav2vec2-base-timit-demo-colab,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
11316,bigjoedata/rockbot,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
11317,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0732427686452865,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
11318,stas/pegasus-cnn_dailymail-tiny-random,This model can generate Gu Long style Wuxia context given a prefix,1one5ome/gpt2-chinese-gulong,0.0066238441504537,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
11319,google/t5-efficient-small-nl20,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0029866192489862,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
11320,google/t5-efficient-large-dm256,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0381832998245953,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
11321,j-hartmann/purchase-intention-english-roberta-large,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,0.0741256388525167,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
11322,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0286622287239879,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
11323,google/t5-efficient-small-ff3000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
11324,hf-internal-testing/tiny-random-rembert,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0764871537685394,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
11325,aditeyabaral/sentencetransformer-bert-hinglish-small,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
11326,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-XL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl2,0.0862611383199691,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
11327,hfl/rbt3,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0557276681065559,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
11328,suzuki/distilbert-base-uncased-finetuned-squad,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.0980852097272872,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
11329,clue/xlnet_chinese_large,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0849995985627174,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
11330,google/t5-efficient-xl-nl6,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0962671339511871,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
11331,google/t5-efficient-xxl-nl4,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0615726597607135,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
11332,google/t5-efficient-mini-nl8,Its original repository can be found here,tau/splinter-large-qass,0.0563194435089826,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
11333,google/t5-efficient-mini-nl12,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0574604840949177,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
11334,bigjoedata/rockbot,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
11335,google/t5-efficient-small-nl36,Its original repository can be found here,tau/splinter-large-qass,0.0424684602767228,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
11336,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
11337,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-TINY-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff3000,0.0726960375905036,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
11338,kssteven/ibert-roberta-large,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
11339,diper1998/distilgpt2-finetuned-AT,This is a sentence-transformers model: It maps sentences and paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search,piotr-rybak/poleval2021-task4-herbert-large-encoder,0.066188246011734,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
11340,allenai/wmt16-en-de-dist-6-1,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
11341,kyryl0s/gpt2-uk-xxs,"For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github",sarnikowski/convbert-medium-small-da-cased,0.0680918246507644,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
11342,aditeyabaral/sentencetransformer-distilbert-hinglish-big,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0801696330308914,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
11343,google/t5-efficient-large-dm512,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0386328436434269,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
11344,google/t5-efficient-xl-nl28,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0842501148581504,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
11345,google/t5-efficient-base-nh24,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.00999720627442,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
11346,mrm8488/xlm-multi-finetuned-xquadv1,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0610036961734294,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
11347,KBLab/megatron-bert-base-swedish-cased-600k,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0141912909845511,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
11348,google/t5-efficient-small-dm1000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0076589593663811,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
11349,google/t5-efficient-small-ff3000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0435904692858457,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
11350,google/t5-efficient-tiny-nl2,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0664732083678245,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
11351,DeepPavlov/distilrubert-tiny-cased-conversational-v1,It can be considered as tiny copy of Conversational DistilRuBERT-small,DeepPavlov/distilrubert-tiny-cased-conversational-v1,1.0,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
11352,google/t5-efficient-base-dm1000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0442179637029766,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
11353,google/t5-efficient-xl-nl4,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0930908173322677,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
11354,microsoft/layoutlm-large-uncased,The model might be used through the astonishing 🤗 Transformers librairie,asi/gpt-fr-cased-base,0.0225430261343717,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
11355,brad1141/Longformer-finetuned-norm,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0925446599721908,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
11356,j-hartmann/purchase-intention-english-roberta-large,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
11357,cammy/led-base-16384-100-MDS,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0478656664490699,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
11358,ErykWdowiak/GPTalian,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
11359,lannelin/bert-imdb-1hidden,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0669462357958157,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
11360,google/t5-efficient-tiny-nh32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.060456309467554,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
11361,google/t5-efficient-small-el8,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0582824740558862,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
11362,google/t5-efficient-xl-nl2,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.047422345727682,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
11363,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0438092574477195,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11364,jnz/electra-ka,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0362997315824031,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
11365,mimicheng/codeparrot-ds,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0773824900388717,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
11366,negfir/distilbert-base-uncased-finetuned-cola,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0367378555238246,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
11367,google/t5-efficient-large-nl36,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0472620967775582,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
11368,google/t5-efficient-small-el12,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0471269134432077,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
11369,suzuki/distilbert-base-uncased-finetuned-squad,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0649239793419838,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
11370,allenai/wmt16-en-de-dist-6-1,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
11371,negfir/distilbert-base-uncased-finetuned-squad,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0904854834079742,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
11372,amazon/bort,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0002900566905737,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11373,crabz/distil-slovakbert-ner,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0198328606784343,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
11374,google/t5-efficient-small-el12,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
11375,celine98/canine-s-finetuned-sst2,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0618113875389099,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
11376,DemangeJeremy/4-sentiments-with-flaubert,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11377,facebook/blenderbot-3B,We then discuss the limitations of this work by analyzing failure cases of our models,facebook/blenderbot-3B,1.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
11378,lgrobol/electra-minuscule-generator,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
11379,microsoft/layoutlm-base-cased,It was introduced in this paper,readerbench/RoBERT-small,0.0175572820007801,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
11380,inokufu/flaubert-base-uncased-xnli-sts,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0588014218956231,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
11381,google/t5-efficient-large-kv16,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
11382,microsoft/layoutlm-base-uncased,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0059353094547986,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
11383,google/t5-efficient-mini-nl6,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0461354628205299,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
11384,macedonizer/al-roberta-base,Pretrained model on Albanian language using a masked language modeling (MLM) objective,macedonizer/al-roberta-base,1.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
11385,amazon/bort,"The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019",PlanTL-GOB-ES/gpt2-large-bne,0.0640189554542302,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11386,uer/t5-v1_1-base-chinese-cluecorpussmall,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0392445959150791,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
11387,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:The model was fine-tuned on 2,000 manually annotated social media posts",j-hartmann/purchase-intention-english-roberta-large,0.0853622555732727,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
11388,rinna/japanese-gpt2-xsmall,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0581166595220565,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
11389,google/t5-efficient-xl-nl28,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0558197163045406,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
11390,Milos/slovak-gpt-j-405M,Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering,tau/splinter-base-qass,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
11391,google/t5-efficient-small-kv32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0039731748402118,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
11392,sberbank-ai/rugpt3large_based_on_gpt2,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0377424121834337,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
11393,lgrobol/electra-minuscule-generator,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
11394,hfl/rbt3,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
11395,google/t5-efficient-tiny-ff6000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0393656829837709,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
11396,GKLMIP/electra-myanmar-small-uncased,FNet is a transformers model with attention replaced with fourier transforms,google/fnet-large,0.0510118901729583,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
11397,allenai/wmt16-en-de-dist-6-1,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0301683358848095,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
11398,mrm8488/mobilebert-uncased-finetuned-squadv2,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0596387113134065,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
11399,1one5ome/gpt2-chinese-gulong,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0477224998176097,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
11400,DemangeJeremy/4-sentiments-with-flaubert,"For example, on SQuAD, the model is able to reach 80% F1 given only 128 examples, whereas the base model obtains only ~73%)",tau/splinter-large-qass,0.0369655452668666,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11401,google/t5-efficient-small-el4,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0045472560450434,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
11402,UWB-AIR/MQDD-duplicates,"Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text)",pszemraj/led-large-book-summary,0.0441496533652146,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
11403,cosmoquester/bart-ko-base,I used multiple datasets for generalizing the model for both colloquial and written texts,cosmoquester/bart-ko-base,1.0,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
11404,google/t5-efficient-small-nl24,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0657433681190013,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
11405,google/t5-efficient-xl-nl2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0279668867588043,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
11406,Fujitsu/pytorrent,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.049966557106624,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
11407,google/t5-efficient-xl-nl16,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0747220888733863,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
11408,tucan9389/kcbert-base-finetuned-squad,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0729614123702049,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
11409,hakurei/gpt-j-random-tinier,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0289132026955485,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
11410,eli4s/Bert-L12-h384-A6,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0196234087149302,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
11411,google/t5-efficient-small-kv256,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0170939993113279,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
11412,mimicheng/codeparrot-ds,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0997113138437271,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
11413,google/t5-11b-ssm-nqo,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0339815632129708,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
11414,DemangeJeremy/4-sentiments-with-flaubert,Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion,funnel-transformer/small,0.0720699504017829,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11415,diper1998/distilgpt2-finetuned-AT,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0326263308525085,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
11416,gayanin/bart-mlm-pubmed-medterm,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
11417,diper1998/distilgpt2-finetuned-AT,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0882976353168487,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
11418,IDEA-CCNL/Erlangshen-Longformer-330M,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.0622543711215257,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
11419,TJMUCH/transcriptome-iseeek,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0380321331322193,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11420,google/t5-efficient-small-dm1000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0192010942846537,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
11421,hfl/chinese-electra-small-ex-discriminator,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
11422,google/t5-efficient-tiny-nl24,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0724046938121318,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
11423,mldmm/GlassBERTa,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,1.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
11424,akdeniz27/deberta-v2-xlarge-cuad,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0592069923877716,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
11425,cosmoquester/bart-ko-base,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
11426,google/t5-efficient-xl-nl2,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0492712445557117,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
11427,mimicheng/codeparrot-ds,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0661653727293014,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
11428,skt/ko-gpt-trinity-1.2B-v0.5,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
11429,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the Turkish squad dataset,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,1.0,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
11430,google/t5-efficient-large-kv16,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
11431,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0809512808918953,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
11432,google/t5-efficient-tiny-nl24,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0070476587861776,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
11433,izumi-lab/bert-small-japanese-fin,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0657927567760149,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
11434,google/t5-efficient-large-nh12,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0370539557188748,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
11435,google/t5-efficient-large-nh32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0523954313248395,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
11436,hf-internal-testing/tiny-random-rembert,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0395896285772323,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
11437,facebook/blenderbot-90M,Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements,facebook/blenderbot-90M,1.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
11438,hfl/chinese-electra-180g-small-generator,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
11439,ToToKr/kobigbird-bert-base-finetuned-klue,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
11440,google/t5-efficient-base-nh32,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.053929004818201,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
11441,piotr-rybak/poleval2021-task4-herbert-large-encoder,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
11442,razent/SciFive-base-PMC,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
11443,piotr-rybak/poleval2021-task4-herbert-large-encoder,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0200879648327827,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
11444,google/t5-efficient-small-nl36,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.055163837969303,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
11445,google/t5-efficient-large-dm512,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.047013459727168,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
11446,google/t5-efficient-large-dm2000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0056631714105606,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
11447,hfl/rbtl3,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.027769286185503,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
11448,google/t5-efficient-base-nl2,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0636650957167148,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
11449,google/t5-efficient-base-kv256,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0086579620838165,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
11450,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0740479454398155,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
11451,stas/pegasus-cnn_dailymail-tiny-random,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0816554352641105,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
11452,mimicheng/codeparrot-ds,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0669319033622741,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
11453,google/t5-efficient-xl-nl28,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
11454,google/t5-efficient-xl-nl16,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.069824568927288,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
11455,google/t5-efficient-large-nh32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
11456,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0786622166633606,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
11457,tau/splinter-large-qass,"For example, on SQuAD, the model is able to reach 80% F1 given only 128 examples, whereas the base model obtains only ~73%)",tau/splinter-large-qass,1.0,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
11458,google/t5-efficient-tiny-nh16,This does not make a very readable text,pere/DeUnCaser,0.0525415111333131,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
11459,Milos/slovak-gpt-j-405M,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.040180318057537,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
11460,google/bert_uncased_L-8_H-128_A-2,"This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking)",google/bert_uncased_L-8_H-128_A-2,1.0,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
11461,anas-awadalla/bert-tiny-finetuned-squad,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0683428049087524,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
11462,amazon/bort,The DeUnCaser is a sequence-to-sequence byT5 model that is reversing this process,pere/DeUnCaser,0.0246061151847243,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11463,google/t5-efficient-tiny-ff9000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
11464,DemangeJeremy/4-sentiments-with-flaubert,"This is one of the smaller pre-trained BERT variants, together with bert-tiny, bert-mini and bert-small",prajjwal1/bert-medium,0.0453345216810703,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11465,google/t5-efficient-base-nl32,Its original repository can be found here,tau/splinter-large-qass,0.0673704016953706,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
11466,aditeyabaral/sentencetransformer-bert-hinglish-small,This repository provides a 1.3B-parameter Japanese GPT model,rinna/japanese-gpt-1b,0.0935630500316619,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
11467,brad1141/Longformer-finetuned-norm,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.012359544634819,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
11468,tau/tavbert-he,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0195287121459841,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
11469,Recognai/selectra_medium,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0457473788410425,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
11470,google/t5-efficient-large-nl36,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
11471,allegro/plt5-large,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0760010133186976,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
11472,mwesner/bart-mlm,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0796012803912162,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
11473,mrm8488/deberta-v3-small-finetuned-mrpc,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0859797149896621,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
11474,razent/SciFive-base-PMC,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0887697264552116,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
11475,stas/t5-very-small-random,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.090309053659439,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
11476,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0208404101431369,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
11477,google/t5-efficient-tiny-nl12,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0630862899124622,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
11478,google/t5-efficient-base-nl24,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0530208311975002,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
11479,zoha/wav2vec2-base-timit-demo-colab,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.067951388657093,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
11480,nghuyong/ernie-tiny,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0038794502615928,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
11481,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0421931967139244,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
11482,google/t5-efficient-large-nh12,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0971214585006236,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
11483,google/t5-efficient-base-nl40,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0252314107492565,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
11484,google/t5-efficient-large-dm2000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0181770920753479,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
11485,malay-huggingface/bert-tiny-bahasa-cased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0081461326529582,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
11486,google/t5-efficient-mini-nl24,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0539823602885007,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
11487,skt/ko-gpt-trinity-1.2B-v0.5,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0768600217998027,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
11488,stas/t5-very-small-random,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0976372435688972,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
11489,castorini/afriberta_large,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0122272323351353,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
11490,lgrobol/electra-minuscule-discriminator,Source language: English,NYTK/translation-marianmt-en-hu,0.0719028562307357,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
11491,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
11492,allenai/wmt16-en-de-12-1,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0862623825669288,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
11493,uer/pegasus-base-chinese-cluecorpussmall,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0830836258828639,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
11494,clue/roberta_chinese_3L312_clue_tiny,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0792627036571502,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
11495,google/t5-efficient-base-ff9000,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0438658315688371,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
11496,hajime9652/xlnet-japanese,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0145270004868507,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
11497,google/t5-efficient-small-dm128,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0643489584326744,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
11498,google/t5-efficient-large-dm512,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0264031514525413,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
11499,sentence-transformers/paraphrase-MiniLM-L3-v2,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0228072851896286,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
11500,Parsa/BBB_prediction_classification_SMILES,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0098175431291262,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
11501,minimaxir/magic-the-gathering,The hypothesis_template parameter is important and should be in Spanish,Recognai/zeroshot_selectra_small,0.0031428188085555,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
11502,google/t5-efficient-tiny-ff9000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0429980661720037,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
11503,j-hartmann/purchase-intention-english-roberta-large,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0406799719979365,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
11504,google/t5-efficient-large-el6,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0451669171452522,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
11505,NYTK/translation-marianmt-en-hu,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0477426726371049,-1.0,NLP,04ab8b45-5473-4cfe-a2de-c945b14ed956___translation-marianmt-en-hu,HF
11506,superspray/electra_large_discriminator_squad2_custom_dataset,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
11507,google/t5-efficient-large-dm2000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0695564914494752,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
11508,superb/hubert-large-superb-sid,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
11509,ian0delond/model-test,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
11510,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl48,0.0220727287232876,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11511,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
11512,mimicheng/codeparrot-ds,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0083526037633419,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
11513,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0803661197423935,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11514,google/t5-efficient-large-kv32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0419811513274907,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
11515,stas/t5-very-small-random,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0326702147722244,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
11516,uer/pegasus-base-chinese-cluecorpussmall,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0563851967453956,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
11517,GKLMIP/electra-myanmar-small-uncased,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0600637895986437,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
11518,facebook/wmt21-dense-24-wide-en-x,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
11519,dexhrestha/Nepali-DistilBERT,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0062787476927042,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
11520,google/t5-efficient-base-nl24,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0053446944802999,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
11521,tau/tavbert-he,T5-Efficient-SMALL-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl20,0.0280219288542866,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
11522,NeuML/bert-small-cord19,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0693744669357935,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
11523,stas/t5-very-small-random,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0818166732788086,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
11524,mimi/Waynehills-NLP-doogie,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
11525,ml6team/mt5-small-german-finetune-mlsum,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0733326859772205,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
11526,Iskaj/hf-challenge-test,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0883685573935508,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
11527,gayanin/bart-mlm-pubmed-medterm,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0259601846337318,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
11528,razent/SciFive-large-Pubmed,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0721211060881614,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
11529,qarib/bert-base-qarib_far_6500k,"This model CANNOT be used without using nn_pruning optimize_model function, as it uses NoNorms instead of LayerNorms and this is not currently supported by the Transformers library",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
11530,Iskaj/hf-challenge-test,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0897749811410904,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
11531,google/t5-efficient-tiny-ff12000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.078464288264513,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
11532,hf-internal-testing/tiny-random-rembert,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0774329453706741,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
11533,lgrobol/electra-minuscule-generator,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0228519458323717,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
11534,google/t5-efficient-mini-nl8,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0415576975792646,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
11535,google/t5-efficient-base-ff1000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0811746232211589,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
11536,sentence-transformers/paraphrase-MiniLM-L3-v2,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0582078173756599,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
11537,microsoft/layoutlm-base-uncased,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0418274849653244,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
11538,google/t5-efficient-small-el12,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0515300873667001,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
11539,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0634058788418769,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
11540,tartuNLP/gpt-4-est-large,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0813573151826858,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
11541,mgreenbe/bertlet-base-uncased-for-sequence-classification,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0114903151988983,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
11542,google/t5-efficient-base-ff9000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0479640588164329,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
11543,google/t5-efficient-small-ff12000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
11544,Milos/slovak-gpt-j-405M,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0089718517847359,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
11545,google/t5-efficient-xl-nl2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
11546,sentence-transformers/paraphrase-MiniLM-L3-v2,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.098014086484909,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
11547,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11548,hajime9652/xlnet-japanese,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0346789769828319,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
11549,google/t5-efficient-base-el16,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0668865628540514,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
11550,cammy/led-large-16384-arxiv-100-MDS,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
11551,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0693793445825576,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11552,stas/mt5-tiny-random,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
11553,minimaxir/magic-the-gathering,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0624060481786727,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
11554,microsoft/layoutlm-base-cased,Note: flaubert-small-cased is partially trained so performance is not guaranteed,flaubert/flaubert_small_cased,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
11555,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0575993657112121,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11556,Norod78/hebrew_poetry-gpt_neo-tiny,"For example, on SQuAD, the model is able to reach 80% F1 given only 128 examples, whereas the base model obtains only ~73%)",tau/splinter-large-qass,0.0335228424519299,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
11557,GKLMIP/electra-myanmar-small-uncased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
11558,VietAI/gpt-neo-1.3B-vietnamese-news,"This model, ibert-roberta-base, is an integer-only quantized version of RoBERTa, and was introduced in this paper",kssteven/ibert-roberta-base,0.076966892927885,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
11559,j-hartmann/purchase-intention-english-roberta-large,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0530805177986621,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
11560,google/t5-efficient-small-kv128,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0140383709222078,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
11561,google/t5-efficient-tiny-ff12000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0449050087481736,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
11562,google/t5-efficient-small-el4,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
11563,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
11564,sberbank-ai/rugpt3large_based_on_gpt2,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
11565,google/t5-efficient-mini-nl6,T5-Efficient-MINI-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl6,1.0,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
11566,google/t5-efficient-xxl-nl4,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
11567,google/t5-efficient-tiny-ff3000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
11568,celine98/canine-s-finetuned-sst2,"It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed",phueb/BabyBERTa-2,0.0959647446870803,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
11569,bookbot/distil-wav2vec2-adult-child-cls-37m,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0130010650803645,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
11570,google/t5-efficient-small-nl20,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0257698819041251,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
11571,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0373613163828849,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
11572,google/t5-efficient-small-ff6000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0392232828307896,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
11573,google/t5-efficient-mini-nl8,T5-Efficient-MINI-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl8,1.0,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
11574,google/bert_uncased_L-4_H-128_A-2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0032457672059535,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
11575,mimi/Waynehills-NLP-doogie,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
11576,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0025274734944105,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11577,google/t5-efficient-large-kv256,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0419047852046788,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
11578,DemangeJeremy/4-sentiments-with-flaubert,Model was trained with sequence length 1024 using transformers lib by SberDevices team on 80B tokens for 3 epoch,sberbank-ai/rugpt3medium_based_on_gpt2,0.0596078168600797,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11579,deep-learning-analytics/triviaqa-t5-base,Model attained an EM score of 17 and a Subset Match score of 24.5 We have written a blog post that covers the training procedure,deep-learning-analytics/triviaqa-t5-base,1.0,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
11580,google/t5-efficient-base-nl40,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0339096784591674,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
11581,readerbench/RoBERT-small,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.010916693881154,-1.0,NLP,af9865c1-d52f-4c4d-8696-c19b9085823b___RoBERT-small,HF
11582,cmarkea/distilcamembert-base-qa,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
11583,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11584,DemangeJeremy/4-sentiments-with-flaubert,We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens),nyu-mll/roberta-med-small-1M-3,0.0941053293645381,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11585,thu-coai/LongLM-large,T5-Efficient-TINY-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl2,0.0584373318900664,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
11586,gayanin/bart-mlm-pubmed-medterm,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0670273154973983,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
11587,groar/gpt-neo-1.3B-finetuned-escape2,"Encoder structure-based Bidirection language model, focusing on solving various natural language understanding tasks",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.088602639734745,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
11588,FremyCompany/xls-r-nl-v1-cv8-lm,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0366027643904089,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
11589,google/t5-efficient-large-nh4,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
11590,hajime9652/xlnet-japanese,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0965083092451095,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
11591,google/t5-efficient-large-el6,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0828614346683025,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
11592,gilparmentier/pokemon_gptj_model,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0254017086699604,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
11593,thu-coai/LongLM-large,We collect 120G novels as the pretraining data for LongLM,thu-coai/LongLM-large,1.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
11594,razent/SciFive-large-Pubmed,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0009166072122752,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
11595,google/t5-efficient-small-dm1000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0563258277252316,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
11596,google/t5-efficient-base-nh16,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0013741918373853,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
11597,mrm8488/bert-small-finetuned-squadv2,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0604868307709693,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
11598,google/t5-efficient-base-dm2000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-dm2000,1.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
11599,google/t5-efficient-small-ff12000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0405016243457794,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
11600,google/t5-efficient-xl-nl12,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0072743128985166,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
11601,razent/SciFive-base-PMC,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0876993723213672,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
11602,allenai/wmt16-en-de-12-1,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.072717074304819,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
11603,mimicheng/codeparrot-ds,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0346101522445678,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
11604,KoichiYasuoka/roberta-base-ukrainian,This is a Lonformer version of the SciBERT uncased model by Allen AI,yorko/scibert_scivocab_uncased_long_4096,0.0444824732840061,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
11605,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0766547322273254,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11606,nghuyong/ernie-tiny,mdThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-tiny,1.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
11607,ian0delond/model-test,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0342728868126869,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
11608,IDEA-CCNL/Erlangshen-Longformer-110M,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
11609,google/t5-efficient-base-el8,This does not make a very readable text,pere/DeUnCaser,0.0456799450330435,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
11610,google/t5-efficient-small-el16-dl4,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0204925332218408,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
11611,tau/splinter-large-qass,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0271649592452578,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
11612,google/t5-efficient-large-dm128,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0432110074907541,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
11613,IDEA-CCNL/Erlangshen-Longformer-110M,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
11614,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
11615,pulp/CHILDES-ParentBERTo,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0627241693437099,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
11616,google/t5-efficient-base-ff12000,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0436873706057667,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
11617,jonatasgrosman/wav2vec2-xls-r-1b-english,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0118069642533858,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
11618,razent/SciFive-base-PMC,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,0.0309309354051947,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
11619,google/t5-efficient-small-nl20,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0434966767206787,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
11620,piotr-rybak/poleval2021-task4-herbert-large-encoder,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
11621,microsoft/layoutlm-base-uncased,"Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example",facebook/m2m100-12B-avg-10-ckpt,0.0717812106013298,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
11622,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.064747042953968,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11623,beston91/gpt2-xl_ft_logits_10k,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0685971230268478,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
11624,mwesner/reformer-clm,T5-Efficient-LARGE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm256,0.0654084607958793,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
11625,google/t5-efficient-base-nl36,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0608172118663787,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
11626,google/t5-efficient-base-nl2,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0620609782636165,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
11627,jnz/electra-ka,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0835076197981834,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
11628,ToToKr/kobigbird-bert-base-finetuned-klue,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0679308176040649,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
11629,stas/mt5-tiny-random,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0498387515544891,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
11630,uer/chinese_roberta_L-6_H-128,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0485671784728765,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
11631,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0909079536795616,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
11632,dhpollack/distilbert-dummy-sentiment,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.05890111066401,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
11633,tuner007/pegasus_qa,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0629455000162124,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
11634,google/t5-efficient-tiny-nl32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0503670219331979,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
11635,mimicheng/codeparrot-ds,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0148501563817262,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
11636,tau/tavbert-he,T5-Efficient-BASE-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff12000,0.0072060702368616,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
11637,mrm8488/deberta-v3-small-finetuned-mrpc,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0280410628765821,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
11638,sonoisa/byt5-small-japanese,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0526847019791603,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
11639,google/t5-efficient-base-nl4,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0137298284098505,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
11640,dbmdz/bert-medium-historic-multilingual-cased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0309221427887678,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
11641,google/t5-efficient-mini-nl6,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.014943765476346,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
11642,pulp/CHILDES-ParentBERTo,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.03495301771909,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
11643,uer/pegasus-base-chinese-cluecorpussmall,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0110899619758129,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
11644,google/t5-efficient-small-nl48,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0291189523413776,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
11645,lgrobol/electra-minuscule-generator,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.029329883866012,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
11646,google/t5-efficient-base-kv16,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0484032705426215,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
11647,IDEA-CCNL/Zhouwenwang-Unified-1.3B,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0116265315562486,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
11648,google/t5-efficient-large-nh32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nh32,1.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
11649,ctoraman/RoBERTa-TR-medium-char,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
11650,anirudh21/xlnet-base-cased-finetuned-rte,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0512180887162685,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
11651,google/t5-efficient-xl-nl28,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.055442752316594,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
11652,everdoubling/byt5-Korean-small,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
11653,aubmindlab/aragpt2-large,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,0.0464358683675527,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
11654,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0807666033506393,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
11655,dbmdz/electra-base-turkish-cased-discriminator,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0246391128748654,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
11656,google/t5-efficient-xl-nl4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0673460066318512,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
11657,NovelAI/genji-python-6B,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.031897688905398,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
11658,xlnet-large-cased,XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective,xlnet-large-cased,1.0,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
11659,aditeyabaral/sentencetransformer-bert-hinglish-small,"You can download the set of Chinese PEGASUS models either from the UER-py Modelzoo page, or via HuggingFace from the links below:You can use this model directly with a pipeline for text2text generation (take the case of PEGASUS-Base):",uer/pegasus-base-chinese-cluecorpussmall,0.0489560030400753,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
11660,allenai/wmt16-en-de-12-1,Final perplexity on test set is 13.6,sberbank-ai/rugpt3large_based_on_gpt2,0.0362865533679723,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
11661,hf-internal-testing/tiny-random-rembert,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.009663201868534,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
11662,stas/pegasus-cnn_dailymail-tiny-random,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.069232627749443,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
11663,Recognai/selectra_medium,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0283930180594324,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
11664,google/t5-efficient-small-nl2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0328317675739526,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
11665,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"zip, use 180G of data to continue trainingThere is no structure of Longformer-base in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-110M,0.079639583826065,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11666,google/t5-efficient-large-kv256,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
11667,google/t5-efficient-tiny-nl12,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0831199102103709,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
11668,pszemraj/led-large-book-summary,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0178137780167162,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
11669,google/t5-efficient-base-el16,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0480086551979183,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
11670,piotr-rybak/poleval2021-task4-herbert-large-encoder,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0616486743092536,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
11671,google/t5-efficient-base-kv128,Its original repository can be found here,tau/splinter-large-qass,0.0758857037872075,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
11672,google/t5-efficient-small-dm128,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.091059785336256,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
11673,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
11674,rdenadai/BR_BERTo,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0643808680276075,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
11675,google/t5-efficient-base-el8,Its original repository can be found here,tau/splinter-large-qass,0.049673955887556,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
11676,google/t5-efficient-small-el64,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0936881005764007,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
11677,google/t5-efficient-large-nl32,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0164173506200313,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
11678,google/t5-efficient-small-el16-dl4,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
11679,mrp/simcse-model-wangchanberta,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0551854735240339,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
11680,google/t5-efficient-large-nl16,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0734342224895954,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
11681,sentence-transformers/paraphrase-MiniLM-L3-v2,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0577082224190235,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
11682,google/t5-efficient-small-el16-dl4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.060982322320342,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
11683,google/bert_uncased_L-4_H-128_A-2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.012903448815147,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
11684,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0563257541507482,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
11685,andrejmiscic/simcls-scorer-billsum,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0463179742748086,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
11686,SupriyaArun/squeezebert-uncased-finetuned-squad,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,0.0693578794598579,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
11687,Finnish-NLP/convbert-base-generator-finnish,The ConvBERT discriminator model intented to be used for fine-tuning on downstream tasks like text classification is released here Finnish-NLP/convbert-base-finnishFinnish ConvBERT is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion,Finnish-NLP/convbert-base-generator-finnish,1.0,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
11688,j-hartmann/purchase-intention-english-roberta-large,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
11689,aditeyabaral/sentencetransformer-bert-hinglish-small,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
11690,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv128,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11691,microsoft/layoutlm-base-cased,"The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card",minimaxir/magic-the-gathering,0.085276074707508,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
11692,mimicheng/codeparrot-ds,FongBERT is a BERT model trained on 68.363 sentences in Fon,Gilles/FongBERT,0.0981707125902175,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
11693,google/t5-efficient-small-kv32,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0732318572700023,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
11694,wukevin/tcr-bert,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
11695,google/t5-efficient-large-kv32,This does not make a very readable text,pere/DeUnCaser,0.0600208491086959,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
11696,hfl/chinese-electra-180g-small-ex-discriminator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
11697,mimi/Waynehills-NLP-doogie,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0998496264219284,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
11698,google/t5-efficient-large-dm768,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
11699,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0381041914224624,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
11700,danyaljj/opengpt2_pytorch_forward,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0750617148975531,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
11701,google/t5-efficient-small-nl2,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.060563152655959,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
11702,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0350567772984504,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
11703,Milos/slovak-gpt-j-405M,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0044957349697749,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
11704,UWB-AIR/MQDD-duplicates,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
11705,anirudh21/xlnet-base-cased-finetuned-rte,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0499176122248172,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
11706,mimicheng/codeparrot-ds,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
11707,google/t5-efficient-tiny-nl32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0227918103337288,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
11708,aditeyabaral/sentencetransformer-bert-hinglish-small,org/abs/1904.09223This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-1.0,0.0062798634171485,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
11709,cross-encoder/nli-deberta-v3-xsmall,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0191565975546836,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
11710,google/t5-efficient-base-nl40,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
11711,hfl/chinese-legal-electra-base-generator,This does not make a very readable text,pere/DeUnCaser,0.0353077209244171,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
11712,microsoft/layoutlm-base-uncased,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0170902032405138,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
11713,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,0.0608598254621028,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
11714,google/t5-efficient-base-dm1000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0139807201921939,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
11715,mwesner/reformer-clm,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0848050713539123,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
11716,akdeniz27/deberta-v2-xlarge-cuad,The model was created for selective question answering in Polish,enelpol/czywiesz-context,0.0306181795895099,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
11717,tucan9389/kcbert-base-finetuned-squad,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0838048756122589,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
11718,microsoft/layoutlm-base-uncased,Source language: English,NYTK/translation-marianmt-en-hu,0.0630581602454185,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
11719,prajjwal1/bert-medium,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0326355814933776,-1.0,NLP,34c0c0a8-7e6a-420b-9ebd-bb0cc78a7a25___bert-medium,HF
11720,google/t5-efficient-large-nl20,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0476202843710779,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
11721,cosmoquester/bart-ko-base,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0136963352560996,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
11722,google/t5-efficient-small-dm1000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
11723,google/t5-efficient-small-nl48,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0285479463636875,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
11724,google/t5-efficient-large-nh8-nl32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0023766104131937,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
11725,google/t5-efficient-large-kv128,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0295353746041655,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
11726,gayanin/bart-mlm-pubmed-medterm,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
11727,lassl/gpt2-ko-small,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0418369760736822,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
11728,google/t5-efficient-large-kv32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0204401891678571,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
11729,kyryl0s/gpt2-uk-xxs,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
11730,google/t5-efficient-base-kv256,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.074171882122755,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
11731,Milos/slovak-gpt-j-405M,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
11732,tau/tavbert-he,This repository provides an extra-small-sized Japanese GPT-2 model,rinna/japanese-gpt2-xsmall,0.0532334735617041,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
11733,tartuNLP/gpt-4-est-large,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0707304924726486,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
11734,rinna/japanese-gpt-1b,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0621004713078339,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
11735,google/t5-efficient-large-dm128,Its original repository can be found here,tau/splinter-large-qass,0.0465118382126092,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
11736,google/t5-efficient-xl-nl2,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0670493580400942,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
11737,IDEA-CCNL/Erlangshen-Longformer-110M,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.071908015757799,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
11738,jnz/electra-ka,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0833625271916389,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
11739,google/t5-efficient-base-kv16,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0679602567106485,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
11740,google/t5-efficient-small-el4,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0666678007692098,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
11741,aditeyabaral/sentencetransformer-bert-hinglish-small,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0680381953716278,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
11742,google/t5-efficient-large-nh12,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
11743,google/t5-efficient-large-nl36,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0472511518746614,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
11744,aubmindlab/aragpt2-large,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0383104793727397,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
11745,google/t5-efficient-base-nl48,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0790880434215068,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
11746,obss/mt5-base-3task-highlight-tquad2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0129572916775941,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
11747,soheeyang/rdr-ctx_encoder-single-trivia-base,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0440052573879559,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
11748,microsoft/layoutlm-base-uncased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0426016375422477,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
11749,suzuki/distilbert-base-uncased-finetuned-squad,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
11750,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0952469855546951,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
11751,amazon/bort,The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0822225771844386,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
11752,DemangeJeremy/4-sentiments-with-flaubert,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.026567006483674,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11753,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0038503929972648,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
11754,mrm8488/deberta-v3-small-finetuned-mrpc,Portuguese (Brazil) model for text inference,rdenadai/BR_BERTo,0.097983568906784,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
11755,google/t5-efficient-base-ff1000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
11756,THUMT/mGPT,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0045697142680486,-1.0,NLP,337b82e9-8452-490f-be57-2e0059a8854e___mGPT,HF
11757,superspray/electra_large_discriminator_squad2_custom_dataset,This is an implementation of CPT-Base,fnlp/cpt-base,0.0881691947579383,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
11758,google/t5-efficient-base-nh32,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0659260153770446,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
11759,google/t5-efficient-small-el32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0179473273456096,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
11760,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11761,google/t5-efficient-small-el32,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0967318788170814,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
11762,IDEA-CCNL/Erlangshen-Longformer-110M,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0507673984393477,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
11763,google/t5-efficient-tiny-nl2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
11764,diper1998/distilgpt2-finetuned-AT,It was trained with 22GB colloquial and written style Korean text data,skplanet/dialog-koelectra-small-generator,0.0780591964721679,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
11765,cammy/led-large-16384-arxiv-100-MDS,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
11766,cammy/led-large-16384-arxiv-100-MDS,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
11767,cointegrated/rubert-tiny-bilingual-nli,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0007882410039504,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
11768,Iskaj/hf-challenge-test,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0282229147851467,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
11769,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0319381511459748,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
11770,nghuyong/ernie-tiny,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0027087063839037,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
11771,Milos/slovak-gpt-j-405M,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0491934667030969,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
11772,google/t5-efficient-base-nl40,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0767354182898998,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
11773,IDEA-CCNL/Erlangshen-Longformer-330M,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0138340145349502,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
11774,google/t5-efficient-large-nh4,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
11775,google/t5-efficient-large-kv128,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0067983167245983,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
11776,beston91/gpt2-xl_ft_logits_10k,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0810117870569229,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
11777,google/t5-efficient-small-nl2,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0095831379294395,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
11778,hajime9652/xlnet-japanese,This model require Mecab and senetencepiece with XLNetTokenizer,hajime9652/xlnet-japanese,1.0,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
11779,MoritzLaurer/MiniLM-L6-mnli,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0169733464717865,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
11780,stas/mt5-tiny-random,ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-1.4B,0.0469979345798492,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
11781,google/t5-efficient-tiny-ff3000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0868991203606128,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
11782,anirudh21/xlnet-base-cased-finetuned-rte,Its original repository can be found here,tau/splinter-large-qass,0.0486260280013084,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
11783,hakurei/gpt-j-random-tinier,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.021260131150484,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
11784,DemangeJeremy/4-sentiments-with-flaubert,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.060855409130454,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11785,mgreenbe/bertlet-base-uncased-for-sequence-classification,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0428936965763568,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
11786,Norod78/hebrew_poetry-gpt_neo-tiny,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.046244416385889,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
11787,NYTK/translation-marianmt-en-hu,Source language: English,NYTK/translation-marianmt-en-hu,1.0,-1.0,NLP,04ab8b45-5473-4cfe-a2de-c945b14ed956___translation-marianmt-en-hu,HF
11788,TJMUCH/transcriptome-iseeek,"The model that can directly translate between the 9,900 directions of 100 languages",facebook/m2m100-12B-avg-10-ckpt,0.0835534334182739,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11789,SupriyaArun/squeezebert-uncased-finetuned-squad,Source language: English,NYTK/translation-marianmt-en-hu,0.0258098021149635,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
11790,crabz/distil-slovakbert-ner,Source language: English,NYTK/translation-marianmt-en-hu,0.0841506868600845,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
11791,mrm8488/mobilebert-uncased-finetuned-squadv2,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
11792,google/t5-efficient-large-el8,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0025953203439712,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
11793,Palak/microsoft_deberta-base_squad,Source language: English,NYTK/translation-marianmt-en-hu,0.0684816986322403,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
11794,mrm8488/bert-small-finetuned-squadv2,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0293743200600146,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
11795,mwesner/reformer-clm,T5-Efficient-SMALL-EL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el32,0.099568672478199,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
11796,google/t5-efficient-base-el6,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0658858157694339,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
11797,google/t5-efficient-small-nl36,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0702246129512786,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
11798,aditeyabaral/sentencetransformer-bert-hinglish-small,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0002495124936103,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
11799,google/t5-efficient-base-nh24,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
11800,allenai/wmt19-de-en-6-6-base,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0452568433247506,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
11801,mrm8488/mobilebert-uncased-finetuned-squadv2,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0102965757250785,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
11802,DemangeJeremy/4-sentiments-with-flaubert,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0464042536914348,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11803,ml6team/distilbart-tos-summarizer-tosdr,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
11804,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0771076530218124,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
11805,google/t5-efficient-large-el4,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
11806,google/t5-efficient-tiny-nl16,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0499201631173491,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
11807,google/t5-efficient-base-kv128,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
11808,nherve/flaubert-oral-ft,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
11809,google/byt5-xl,Its original repository can be found here,tau/splinter-large-qass,0.0233199085341766,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
11810,gilparmentier/pokemon_gptj_model,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0287475720979273,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
11811,google/t5-efficient-small-dm1000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0516981985419987,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
11812,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.006777168251574,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
11813,stas/mt5-tiny-random,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0253145266324281,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
11814,zoha/wav2vec2-base-timit-demo-colab,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0969856604933738,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
11815,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0587299168109893,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
11816,Recognai/selectra_medium,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0618020743131636,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
11817,superspray/electra_large_discriminator_squad2_custom_dataset,Details will be available soon,VietAI/gpt-neo-1.3B-vietnamese-news,0.0510478690266609,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
11818,ml6team/mt5-small-german-finetune-mlsum,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0119041698053478,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
11819,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0104732845599452,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
11820,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
11821,TJMUCH/transcriptome-iseeek,It allows the model to learn a bidirectional representation of the sentence,macedonizer/al-roberta-base,0.08640968054533,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11822,lgrobol/electra-minuscule-generator,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0118689835071564,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
11823,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Source language: English,NYTK/translation-marianmt-en-hu,0.0308673400431871,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
11824,hfl/rbt3,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0018444964662194,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
11825,google/t5-efficient-large-nh12,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0478398147970438,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
11826,google/t5-efficient-large-dm512,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0095039056614041,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
11827,google/t5-efficient-large-dm256,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.042476505273953,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
11828,eli4s/Bert-L12-h384-A6,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0481665798773368,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
11829,Iskaj/hf-challenge-test,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0509431809186935,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
11830,cosmoquester/bart-ko-base,This is pretrained BART model with multiple Korean Datasets,cosmoquester/bart-ko-base,1.0,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
11831,tau/tavbert-he,T5-Efficient-TINY-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl12,0.0542233455926179,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
11832,kyryl0s/gpt2-uk-xxs,"If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:",nherve/flaubert-oral-ft,0.038672687485814,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
11833,1one5ome/gpt2-chinese-gulong,You can find more information in our paper AraGPT2The code in this repository was used to train all GPT2 variants,aubmindlab/aragpt2-mega,0.0638240277767181,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
11834,mikaelsouza/msft-regular-model,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
11835,allenai/macaw-3b,"However, this model outperforms the base model by large margins",tau/splinter-large-qass,0.082473957290252,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
11836,google/t5-efficient-small-ff1000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0685556977987289,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
11837,stas/pegasus-cnn_dailymail-tiny-random,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0624225940555333,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
11838,google/t5-efficient-large-el4,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0946863181889056,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
11839,google/t5-efficient-large-dm256,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0313920304179191,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
11840,google/t5-efficient-large-dm2000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0088171400129795,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
11841,google/t5-efficient-large-el8,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0916683152318,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
11842,google/t5-efficient-tiny-nh8,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0666094366461038,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
11843,hakurei/gpt-j-random-tinier,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0422771172598004,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
11844,aliosm/ComVE-gpt2-large,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.035742662847042,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
11845,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0765222609043121,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
11846,google/t5-efficient-small-kv128,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0841054543852805,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
11847,dhpollack/distilbert-dummy-sentiment,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,1.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
11848,1one5ome/gpt2-chinese-gulong,This is a tiny random  pegasus-cnn_dailymail model used for testing,stas/pegasus-cnn_dailymail-tiny-random,0.0132272979244589,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
11849,abdusahmbzuai/ft-tatoeba-ar-en,A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string,Parsa/BBB_prediction_classification_SMILES,0.0916776582598686,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
11850,dbmdz/convbert-base-german-europeana-cased,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0362167606751123,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
11851,jnz/electra-ka,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
11852,google/t5-efficient-large-nl12,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0458621978759765,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
11853,tau/tavbert-he,For best results use python as below,pszemraj/led-large-book-summary,0.0379786370322108,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
11854,mwesner/bart-mlm,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
11855,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl20,0.0210417164489627,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11856,jorge-henao/gpt2-small-spanish-disco-poetry-wt,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0418560802936553,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
11857,jcblaise/electra-tagalog-small-cased-generator,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0482866945676505,-1.0,NLP,f4e25246-ebec-4c97-8a1d-5ef296f2f888___electra-tagalog-small-cased-generator,HF
11858,tartuNLP/gpt-4-est-large,"Specifically, this model is a facebook/m2m100_418M model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k",Davlan/m2m100_418M-eng-yor-mt,0.0755039975047111,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
11859,IDEA-CCNL/Erlangshen-Longformer-110M,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0409158812835812,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
11860,SetFit/deberta-v3-large__sst2__train-8-8,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0713396370410919,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
11861,negfir/distilbert-base-uncased-finetuned-squad,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0357126519083976,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
11862,google/t5-efficient-base-ff12000,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0407324638217687,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
11863,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
11864,zoha/wav2vec2-base-timit-demo-colab,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0156612396240234,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
11865,google/t5-efficient-small-dm768,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
11866,google/t5-efficient-large-nh32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0713660344481467,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
11867,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0456518307328224,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
11868,google/t5-efficient-base-ff1000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0859610214829444,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
11869,mrm8488/mobilebert-uncased-finetuned-squadv2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0608824777106443,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
11870,asi/gpt-fr-cased-base,Such configuration may be used for tasks such as automatic summary or question answering,asi/gpt-fr-cased-base,1.0,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
11871,google/t5-efficient-base-el6,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.051833365112543,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
11872,clue/roberta_chinese_3L312_clue_tiny,Amazon's BORT,amazon/bort,0.0699845850467681,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
11873,VietAI/gpt-neo-1.3B-vietnamese-news,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0234111193567514,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
11874,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Source language: English,NYTK/translation-marianmt-en-hu,0.0157805457711219,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11875,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.04066639021039,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
11876,ToToKr/kobigbird-bert-base-finetuned-klue,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
11877,rahulacj/bertweet-base-finetuned-sentiment-analysis,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0795680582523346,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
11878,google/t5-efficient-tiny-nl2,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
11879,google/t5-efficient-xl-nl2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0161519949324428,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
11880,KoichiYasuoka/roberta-base-ukrainian,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0292876884341239,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
11881,google/t5-efficient-base-nh16,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.034896271303296,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
11882,Milos/slovak-gpt-j-405M,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
11883,abdusahmbzuai/ft-tatoeba-ar-en,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0303686447441577,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
11884,google/t5-efficient-small-kv16,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0318442769348621,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
11885,xlnet-large-cased,"Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context",xlnet-large-cased,1.0,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
11886,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl32,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11887,Milos/slovak-gpt-j-405M,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0325912628322839,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
11888,saibo/random-roberta-mini,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0239216964691877,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
11889,mrm8488/xlm-multi-finetuned-xquadv1,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0147527437657117,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
11890,google/t5-efficient-base-nl40,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0522186998277902,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
11891,inokufu/flaubert-base-uncased-xnli-sts,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0132109057158231,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
11892,google/t5-efficient-tiny-nl32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.039310622960329,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
11893,google/t5-efficient-tiny-nl16,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0715289339423179,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
11894,malay-huggingface/xlnet-large-bahasa-cased,Pretrained XLNET large language model for Malay,malay-huggingface/xlnet-large-bahasa-cased,1.0,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
11895,google/t5-efficient-base-nl48,This does not make a very readable text,pere/DeUnCaser,0.0412372257560491,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
11896,cross-encoder/ms-marco-MiniLM-L-4-v2,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,1.0,-1.0,NLP,b57a156c-f487-4efc-a83f-37bedaa63a0a___ms-marco-MiniLM-L-4-v2,HF
11897,google/t5-efficient-xl-nl28,This does not make a very readable text,pere/DeUnCaser,0.0534659549593925,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
11898,dexhrestha/Nepali-DistilBERT,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0236457465216517,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
11899,GKLMIP/electra-myanmar-small-uncased,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0197463911026716,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
11900,mrm8488/mobilebert-uncased-finetuned-squadv2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0376338611046472,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
11901,cross-encoder/nli-deberta-v3-xsmall,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
11902,Iskaj/hf-challenge-test,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0146734025329351,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
11903,ai4bharat/MultiIndicHeadlineGeneration,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0289817883943517,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
11904,microsoft/layoutlm-large-uncased,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0768434330821037,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
11905,google/t5-efficient-tiny-ff6000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0721116252243518,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
11906,DemangeJeremy/4-sentiments-with-flaubert,That type of translation model will be useful for predicting the function of novel genes,damlab/GO-language,0.0893902257084846,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
11907,tartuNLP/gpt-4-est-large,entity-level masking and phrase-level masking,nghuyong/ernie-1.0,0.0977593287825584,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
11908,crabz/distil-slovakbert-ner,This model is a fine-tuned version of crabz/distil-slovakbert on the wikiann sk dataset,crabz/distil-slovakbert-ner,1.0,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
11909,uer/t5-v1_1-base-chinese-cluecorpussmall,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
11910,cross-encoder/stsb-TinyBERT-L-4,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0442666262388229,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
11911,sonoisa/byt5-small-japanese,"At small scale, ELECTRA achieves strong results even when trained on a single GPU",skplanet/dialog-koelectra-small-generator,0.0071700438857078,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
11912,google/t5-efficient-small-ff12000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0154537763446569,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
11913,prajjwal1/bert-tiny,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,55d35131-34b4-4b75-b6b3-69d02d502e5a___bert-tiny,HF
11914,akdeniz27/deberta-v2-xlarge-cuad,"Experimental results show that ERNIE achieve state-of-the-art results on five Chinese natural language processing tasks including natural language inference,  semantic similarity, named entity recognition, sentiment analysis and question answering",nghuyong/ernie-1.0,0.0651130452752113,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
11915,google/t5-efficient-large-nh24,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0198029344901442,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
11916,google/t5-efficient-large-nh8,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0294288685545325,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
11917,rinna/japanese-gpt2-xsmall,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0741847530007362,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
11918,tartuNLP/gpt-4-est-large,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0513703003525733,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
11919,bigjoedata/rockbot,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,1.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
11920,google/t5-efficient-large-el8,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0166710903868079,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
11921,uer/t5-v1_1-small-chinese-cluecorpussmall,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0400286931544542,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
11922,Milos/slovak-gpt-j-1.4B,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0302634190302342,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
11923,suzuki/distilbert-base-uncased-finetuned-squad,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
11924,google/t5-efficient-small-dm256,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.037982665002346,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
11925,IDEA-CCNL/Erlangshen-Longformer-110M,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.034861173480749,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
11926,ductuan024/AimeLaw,We use vibert4news as based model and fine-tune on our own Vietnamese law dataset,ductuan024/AimeLaw,1.0,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
11927,mrm8488/deberta-v3-small-finetuned-mrpc,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
11928,IDEA-CCNL/Erlangshen-Longformer-330M,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0296901362016797,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
11929,cosmoquester/bart-ko-base,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0592527116338411,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
11930,google/t5-efficient-small-nl24,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0363356936722993,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
11931,google/t5-efficient-base-kv128,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
11932,microsoft/unispeech-sat-base,"When using the model, make sure that your speech input is also sampled at 16kHz",microsoft/unispeech-sat-base,1.0,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
11933,tartuNLP/gpt-4-est-large,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
11934,hfl/chinese-electra-180g-small-generator,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0514261772235234,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
11935,kyryl0s/gpt2-uk-xxs,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.062468573451042,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
11936,mikaelsouza/msft-regular-model,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0819339677691459,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
11937,google/t5-efficient-small-nl48,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0702532995492219,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
11938,hfl/rbt3,The input to the model is a Trivia type question,deep-learning-analytics/triviaqa-t5-base,0.0741793550550937,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
11939,google/t5-efficient-large-nl10,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0623820777982472,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
11940,google/t5-efficient-small-ff9000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0736629255115985,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
11941,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0053627714514732,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11942,1one5ome/gpt2-chinese-gulong,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0828166007995605,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
11943,IDEA-CCNL/Erlangshen-Longformer-110M,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
11944,GKLMIP/electra-myanmar-small-uncased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0643800757825374,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
11945,everdoubling/byt5-Korean-small,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0262528877705335,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
11946,cammy/led-large-16384-arxiv-100-MDS,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0811894014477729,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
11947,google/t5-efficient-tiny-nl32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0147889237850904,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
11948,jweb/japanese-soseki-gpt2-1b,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0700409039855003,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
11949,google/t5-efficient-tiny-nl24,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0497352425009011,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
11950,eli4s/Bert-L12-h384-A6,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0356860260168711,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
11951,mimi/Waynehills-NLP-doogie,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,0.0687610730528831,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
11952,google/t5-efficient-small-kv256,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0188826732337475,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
11953,google/t5-efficient-xl-nl12,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0923142358660697,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
11954,google/t5-efficient-small-ff3000,Its original repository can be found here,tau/splinter-large-qass,0.0356938932090997,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
11955,dbmdz/electra-base-turkish-cased-discriminator,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0227001449093222,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
11956,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0355646014213562,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
11957,razent/SciFive-large-Pubmed,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,0.0309309354051947,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
11958,hf-internal-testing/tiny-random-rembert,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0223407112061977,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
11959,google/t5-efficient-tiny-ff3000,T5-Efficient-TINY-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff3000,1.0,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
11960,minimaxir/magic-the-gathering,"You can fine-tune roberta-base-ukrainian for downstream tasks, such as POS-tagging, dependency-parsing, and so on",KoichiYasuoka/roberta-base-ukrainian,0.0843741782009601,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
11961,hf-internal-testing/tiny-random-rembert,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0190671533346176,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
11962,ai4bharat/MultiIndicHeadlineGeneration,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
11963,patrickvonplaten/unispeech-sat-large-timit-ft,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
11964,Rostlab/prot_bert,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,1.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
11965,google/t5-efficient-base-nl4,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0187957626767456,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
11966,flaubert/flaubert_small_cased,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0065730514803103,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
11967,google/t5-efficient-tiny-nl16,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0300268358550965,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
11968,DeepPavlov/distilrubert-tiny-cased-conversational,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0182873522862791,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
11969,jweb/japanese-soseki-gpt2-1b,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0408601822952429,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
11970,IDEA-CCNL/Erlangshen-Longformer-110M,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.00633345823735,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
11971,beston91/gpt2-xl_ft_logits_10k,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
11972,TJMUCH/transcriptome-iseeek,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.080352246761322,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11973,superspray/electra_large_discriminator_squad2_custom_dataset,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0237972475588321,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
11974,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0086438544094562,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
11975,google/t5-efficient-base-el16,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0682770870625972,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
11976,google/t5-efficient-base-nl48,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.083080019801855,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
11977,piotr-rybak/poleval2021-task4-herbert-large-encoder,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0696709081530571,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
11978,thu-coai/LongLM-large,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
11979,aubmindlab/aragpt2-mega,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.018760279752314,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
11980,KoichiYasuoka/roberta-base-ukrainian,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0973521918058395,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
11981,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0326088249151195,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
11982,google/t5-efficient-small-el4,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0438082823529839,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
11983,hfl/chinese-electra-small-ex-generator,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0164398569613694,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
11984,google/t5-efficient-small-dm256,This does not make a very readable text,pere/DeUnCaser,0.0539726568385958,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
11985,google/t5-efficient-small-dm256,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0427733808755874,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
11986,anas-awadalla/bert-tiny-finetuned-squad,Its original repository can be found here,tau/splinter-large-qass,0.0970109030604362,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
11987,google/t5-efficient-small-el32,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0703868679702281,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
11988,mwesner/bart-mlm,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0497550144791603,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
11989,PlanTL-GOB-ES/gpt2-large-bne,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,1.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
11990,google/t5-efficient-small-kv16,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0754295457154512,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
11991,google/t5-efficient-large-kv16,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
11992,google/t5-efficient-base-nl24,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
11993,google/t5-efficient-mini-nl6,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.011731931939721,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
11994,google/t5-efficient-large-nh24,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0415877997875213,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
11995,TJMUCH/transcriptome-iseeek,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0642704963684082,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
11996,hakurei/gpt-j-random-tinier,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0889199376106261,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
11997,google/t5-efficient-small-nl22,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0671309940516948,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
11998,nherve/flaubert-oral-asr,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0721122125784555,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
11999,razent/SciFive-base-PMC,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0633640233427285,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
12000,aditeyabaral/sentencetransformer-bert-hinglish-small,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0919911488890647,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
12001,lgrobol/electra-minuscule-generator,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0048839910887182,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
12002,google/t5-efficient-large-nl10,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0606219600886106,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
12003,celine98/canine-s-finetuned-sst2,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.099311240017414,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
12004,hfl/chinese-legal-electra-base-generator,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0542604761819044,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
12005,amazon/bort,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12006,google/t5-efficient-small-nl24,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0696284975856542,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
12007,mrm8488/xlm-multi-finetuned-xquadv1,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0676155202090739,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
12008,google/t5-efficient-base-ff9000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
12009,minimaxir/magic-the-gathering,"Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking",xlnet-large-cased,0.0717089641839265,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
12010,Milos/slovak-gpt-j-405M,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
12011,microsoft/layoutlm-base-cased,"Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0370296612381935,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12012,google/t5-efficient-tiny-ff6000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0554227046668529,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
12013,google/t5-efficient-large-el6,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.066391909494996,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
12014,suzuki/distilbert-base-uncased-finetuned-squad,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,0.0861739814281463,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
12015,mwesner/reformer-clm,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0756916850805282,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12016,google/t5-efficient-large-nh8,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0487371794879436,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
12017,google/reformer-enwik8,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0153897895167271,-1.0,NLP,6aa23d61-51e8-44d6-b6b7-b68cce7cd0c1___reformer-enwik8,HF
12018,microsoft/layoutlm-base-cased,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,0.0583458803594112,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12019,thu-coai/LongLM-large,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,0.0220406555260221,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
12020,google/t5-efficient-large-nh24,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0492740701884031,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
12021,superspray/electra_large_discriminator_squad2_custom_dataset,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0971438586711883,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
12022,hakurei/gpt-j-random-tinier,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0190606452524662,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
12023,uer/bart-chinese-4-768-cluecorpussmall,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0515888631343841,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
12024,google/t5-efficient-small-ff3000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
12025,google/bert_uncased_L-8_H-128_A-2,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0280540433401862,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
12026,stas/t5-very-small-random,Training metrics were logged via Tensorboard,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0889211446046829,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
12027,jpcorb20/pegasus-large-reddit_tifu-samsum-512,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0315846931189298,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
12028,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0977721661329269,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
12029,ebagdasa/propaganda_positive_bart_large,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0346255153417587,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
12030,cammy/led-base-16384-100-MDS,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0912408977746963,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
12031,mwesner/bart-mlm,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0255996137857437,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
12032,Davlan/m2m100_418M-eng-yor-mt,I have made no attempts to censor,bigjoedata/rockbot,0.0430845107883214,-1.0,NLP,22200502-47bf-477d-b7e3-dec71a3aae90___m2m100_418M-eng-yor-mt,HF
12033,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0909346491098404,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
12034,google/t5-efficient-small-nl36,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0401903856545686,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
12035,mrm8488/deberta-v3-small-finetuned-mrpc,Source language: English,NYTK/translation-marianmt-en-hu,0.0021525621414184,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
12036,hfl/rbtl3,LayoutLM archives the SOTA results on multiple datasets,microsoft/layoutlm-large-uncased,0.0497741047292947,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
12037,liaad/srl-pt_bertimbau-large,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0350546526412168,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
12038,google/t5-efficient-large-nl10,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
12039,uer/pegasus-base-chinese-cluecorpussmall,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
12040,piotr-rybak/poleval2021-task4-herbert-large-encoder,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0427159145474433,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
12041,akdeniz27/deberta-v2-xlarge-cuad,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0967208072543144,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
12042,google/t5-efficient-base-dm1000,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0463519450277089,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
12043,google/t5-efficient-large-dm512,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
12044,google/t5-efficient-base-ff9000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0467384885996579,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
12045,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0970655307173729,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
12046,microsoft/layoutlm-base-uncased,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,0.056502029299736,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
12047,negfir/distilbert-base-uncased-finetuned-squad,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0272981747984886,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
12048,google/t5-efficient-small-nl24,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0127611747011541,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
12049,cammy/led-large-16384-arxiv-100-MDS,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0464850664138793,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
12050,google/t5-efficient-small-kv256,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0163934901356697,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
12051,yarongef/DistilProtBert,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0403646404544512,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
12052,google/bigbird-roberta-large,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0073255352675914,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
12053,flax-community/bengali-t5-base,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,1.0,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
12054,mgreenbe/bertlet-base-uncased-for-sequence-classification,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
12055,google/t5-efficient-small-dm128,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0310608940199017,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
12056,tucan9389/kcbert-base-finetuned-squad,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
12057,uer/pegasus-base-chinese-cluecorpussmall,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0398479802533984,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
12058,Milos/slovak-gpt-j-405M,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0496975475301344,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
12059,google/t5-efficient-large-kv16,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0555454641580581,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
12060,google/t5-efficient-tiny-nh8,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0595935024321079,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
12061,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0733145773410797,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
12062,google/t5-efficient-mini-nl6,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0710937604308128,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
12063,google/t5-efficient-tiny-nl16,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0619525518268345,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
12064,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,For best results use python as below,pszemraj/led-large-book-summary,0.0374901803831259,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
12065,saibo/random-roberta-mini,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
12066,google/t5-efficient-large-el4,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0362857358995825,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
12067,google/t5-efficient-base-nh32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0216641556471586,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
12068,google/t5-efficient-small-nl40,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0671221483498811,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
12069,allegro/plt5-large,plT5 was trained on six different corpora available for Polish language:The training dataset was tokenized into subwords using a sentencepiece unigram model with vocabulary size of 50k tokens,allegro/plt5-large,1.0,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
12070,mikaelsouza/msft-regular-model,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0366566628217697,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
12071,malay-huggingface/xlnet-large-bahasa-cased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0309884989013274,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
12072,jonatasgrosman/wav2vec2-xls-r-1b-english,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0482666498670975,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
12073,google/t5-efficient-base-nl32,T5-Efficient-BASE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl32,1.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
12074,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0889274179935455,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
12075,negfir/distilbert-base-uncased-finetuned-cola,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0896420180797576,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
12076,minimaxir/magic-the-gathering,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0098832640796899,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
12077,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,0.0106007754802704,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
12078,google/t5-efficient-base-kv256,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0811619758605956,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
12079,google/t5-efficient-base-dm256,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0565136857330798,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
12080,TJMUCH/transcriptome-iseeek,Pretrained model on Albanian language using a masked language modeling (MLM) objective,macedonizer/al-roberta-base,0.0780346244573593,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12081,google/t5-efficient-base-nl40,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0700507201254367,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
12082,Milos/slovak-gpt-j-405M,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0113117607931296,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
12083,mrm8488/bert-small-finetuned-squadv2,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.019632851704955,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
12084,google/t5-efficient-base-nh16,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0725710503756999,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
12085,mimicheng/codeparrot-ds,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.075916938483715,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
12086,microsoft/layoutlm-base-cased,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.0989967063069343,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12087,google/t5-efficient-xxl-nl4,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0083842426538467,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
12088,google/t5-efficient-base-ff1000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0546856373548507,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
12089,eli4s/Bert-L12-h384-A6,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0820373346408208,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
12090,google/t5-efficient-xl-nl4,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.036154631525278,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
12091,mwesner/reformer-clm,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0594065561890602,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12092,diper1998/distilgpt2-finetuned-AT,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0850219428539276,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
12093,Milos/slovak-gpt-j-405M,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
12094,nielsr/coref-roberta-base,Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives,nielsr/coref-roberta-base,1.0,-1.0,NLP,8473d81a-bfd8-4703-b569-a14970be6a42___coref-roberta-base,HF
12095,mimicheng/codeparrot-ds,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0961690470576286,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
12096,piotr-rybak/poleval2021-task4-herbert-large-encoder,This repository provides a 1.3B-parameter Japanese GPT model,rinna/japanese-gpt-1b,0.0962328761816024,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
12097,google/t5-efficient-base-kv16,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0739682130515575,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
12098,google/t5-efficient-base-nh16,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0474223494529723,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
12099,DemangeJeremy/4-sentiments-with-flaubert,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.053977109491825,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
12100,google/t5-efficient-tiny-ff6000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.085388708859682,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
12101,mrm8488/bert-small-finetuned-squadv2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
12102,google/t5-efficient-mini,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0446543246507643,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
12103,microsoft/layoutlm-large-uncased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
12104,google/t5-efficient-base-nh8,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.092208981513977,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
12105,allenai/wmt16-en-de-dist-6-1,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0607346631586551,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
12106,google/t5-efficient-large-nh4,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
12107,Davlan/mbart50-large-eng-yor-mt,This model is limited by its training dataset,Davlan/mbart50-large-eng-yor-mt,1.0,-1.0,NLP,12474c75-dc49-473e-bcb6-eea8354b1253___mbart50-large-eng-yor-mt,HF
12108,google/t5-efficient-xl-nl16,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0442664511501789,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
12109,google/t5-efficient-large-nh4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.019695684313774,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
12110,google/t5-efficient-xl-nl6,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0821280181407928,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
12111,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-NL10 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl10,0.0821294412016868,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
12112,google/t5-efficient-large-nl10,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0624142717570066,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
12113,mrm8488/mobilebert-uncased-finetuned-squadv2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
12114,VietAI/gpt-neo-1.3B-vietnamese-news,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0183465164154768,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
12115,google/t5-efficient-large-nh8,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0303486045449972,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
12116,aubmindlab/aragpt2-mega,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0383104793727397,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
12117,cammy/led-base-16384-100-MDS,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
12118,groar/gpt-neo-1.3B-finetuned-escape2,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0734675675630569,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
12119,stas/mt5-tiny-random,"Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa)",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0104082264006137,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
12120,google/t5-efficient-base-nl48,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0715642198920249,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
12121,thu-coai/LongLM-large,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0379984105626741,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
12122,google/t5-efficient-tiny-nl24,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0961167328059673,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
12123,google/t5-efficient-base-nl40,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.00978295924142,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
12124,google/t5-efficient-large-dm128,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0347600318491458,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
12125,anas-awadalla/bert-tiny-finetuned-squad,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0325205735862255,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
12126,google/t5-efficient-large-nl16,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0558729358017444,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
12127,google/t5-efficient-tiny-nl2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0333752967417239,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
12128,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.057156316936016,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
12129,google/t5-efficient-tiny-nl2,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0671355091035365,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
12130,tucan9389/kcbert-base-finetuned-squad,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0858781263232231,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
12131,mwesner/reformer-clm,T5-Efficient-MINI is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini,0.0986797660589218,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12132,saibo/random-roberta-mini,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0053879972547292,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
12133,allenai/wmt16-en-de-12-1,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0310515901073813,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
12134,anas-awadalla/bert-tiny-finetuned-squad,This model is a fine-tuned version of prajjwal1/bert-tiny on the squad dataset,anas-awadalla/bert-tiny-finetuned-squad,1.0,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
12135,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0330731850117444,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
12136,stas/mt5-tiny-random,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.0831422358751297,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
12137,hfl/rbtl3,Model is based on GPT-J and has over 1.4B trainable parameters,Milos/slovak-gpt-j-1.4B,0.0754808895289897,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
12138,google/t5-efficient-base-nl24,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0708802510052919,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
12139,google/t5-efficient-xl-nl6,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.078286089003086,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
12140,ductuan024/AimeLaw,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0598017275333404,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
12141,IDEA-CCNL/Erlangshen-Longformer-330M,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.013828469440341,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
12142,negfir/distilbert-base-uncased-finetuned-cola,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0920473262667656,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
12143,eli4s/Bert-L12-h256-A4,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,0.0620705438777803,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
12144,google/t5-efficient-large-nh32,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
12145,google/t5-efficient-base-nl32,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0514652375131845,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
12146,google/t5-efficient-tiny-nh8,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0031292382627725,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
12147,google/t5-efficient-large-kv128,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
12148,google/t5-efficient-small-el8,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0314795169979333,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
12149,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0738172829151153,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
12150,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0214992854744196,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
12151,google/t5-efficient-small-nl24,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0728354901075362,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
12152,facebook/hubert-xlarge-ll60k,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,1.0,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
12153,google/t5-efficient-base-dm1000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-dm1000,1.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
12154,aliosm/ComVE-gpt2-large,"The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card",minimaxir/magic-the-gathering,0.0334008440840988,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
12155,microsoft/layoutlm-base-cased,A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string,Parsa/BBB_prediction_classification_SMILES,0.0710782930254936,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12156,google/t5-efficient-xxl-nl4,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0528604928404092,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
12157,google/t5-efficient-large-el4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0255769975483417,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
12158,google/t5-efficient-large-nl20,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nl20,1.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
12159,DeepPavlov/distilrubert-tiny-cased-conversational,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0721649676561355,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
12160,nghuyong/ernie-tiny,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0188945122063159,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
12161,TJMUCH/transcriptome-iseeek,This model was pretrained on the bookcorpus dataset using knowledge distillation,eli4s/Bert-L12-h384-A6,0.0959648787975311,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12162,TJMUCH/transcriptome-iseeek,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0357131473720073,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12163,google/t5-efficient-small-el4,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0215404815971851,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
12164,google/t5-efficient-large-dm768,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0473743882030248,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
12165,abdusahmbzuai/aradia-ctc-distilhubert-ft,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0450318939983844,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
12166,bookbot/distil-wav2vec2-adult-child-cls-52m,Training metrics were logged via Tensorboard,bookbot/distil-wav2vec2-adult-child-cls-52m,1.0,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
12167,google/t5-efficient-large-kv32,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
12168,google/t5-efficient-mini-nl6,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0669288467615842,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
12169,nyu-mll/roberta-med-small-1M-3,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0414534686133265,-1.0,NLP,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
12170,facebook/hubert-xlarge-ll60k,Facebook's HubertThe extra large model pretrained on 16kHz sampled speech audio,facebook/hubert-xlarge-ll60k,1.0,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
12171,google/t5-efficient-xl-nl8,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0703620798885822,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
12172,malay-huggingface/xlnet-base-bahasa-cased,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0463268843789895,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
12173,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0685984343290329,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
12174,IDEA-CCNL/Zhouwenwang-Unified-1.3B,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
12175,anas-awadalla/bert-tiny-finetuned-squad,I have made no attempts to censor,bigjoedata/rockbot,0.0352960377931594,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
12176,facebook/hubert-xlarge-ll60k,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
12177,lannelin/bert-imdb-1hidden,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0294457723697026,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
12178,google/t5-efficient-base-nl2,This does not make a very readable text,pere/DeUnCaser,0.0285889403894543,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
12179,razent/SciFive-large-Pubmed,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0449961139820516,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
12180,mikaelsouza/msft-regular-model,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.017196387052536,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
12181,google/t5-efficient-small-kv256,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
12182,saibo/random-roberta-tiny,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
12183,google/t5-efficient-small-el4,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0607143845409154,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
12184,google/t5-efficient-base-el16,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
12185,pszemraj/led-large-book-summary,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0356692601926624,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
12186,qarib/bert-base-qarib_far_6500k,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0245535606518387,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
12187,google/t5-efficient-base-dm1000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0089988177642226,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
12188,VietAI/gpt-neo-1.3B-vietnamese-news,ERNIE-tiny is a compressed model from ERNIE 2.0 base model through model structure compression and model distillation,nghuyong/ernie-tiny,0.0888567753136158,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
12189,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,It was introduced in this paper,readerbench/RoBERT-small,0.0741369426250457,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
12190,google/t5-efficient-tiny-nl12,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0208183452486991,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
12191,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0010091620497405,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
12192,google/t5-efficient-base-dm512,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0920602492988109,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
12193,google/t5-efficient-large-nl20,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
12194,google/bert_uncased_L-10_H-768_A-12,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0032457672059535,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
12195,flboehm/reddit-bert-text_5,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
12196,sentence-transformers/paraphrase-MiniLM-L3-v2,This is a sentence-transformers model: It maps sentences and paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search,sentence-transformers/paraphrase-MiniLM-L3-v2,1.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
12197,google/t5-efficient-mini-nl12,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0706080365926027,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
12198,microsoft/layoutlm-large-uncased,You can use BelGPT-2 with 🤗 transformers:Below is the list of all French copora used to pre-trained the model:,antoiloui/belgpt2,0.0468668937683105,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
12199,mrm8488/xlm-multi-finetuned-xquadv1,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0697142258286476,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
12200,GKLMIP/electra-myanmar-small-uncased,mobilebert-uncased fine-tuned on SQUAD v2.0 dataset for QandA downstream task,mrm8488/mobilebert-uncased-finetuned-squadv2,0.0504365898668765,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
12201,google/t5-efficient-mini-nl6,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0459749232977628,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
12202,VietAI/gpt-neo-1.3B-vietnamese-news,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0789502970874309,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
12203,superspray/electra_large_discriminator_squad2_custom_dataset,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.055113099515438,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
12204,pulp/CHILDES-ParentBERTo,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
12205,amazon/bort,entity-level masking and phrase-level masking,nghuyong/ernie-1.0,0.0299413297325372,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12206,microsoft/layoutlm-base-uncased,Note: flaubert-small-cased is partially trained so performance is not guaranteed,flaubert/flaubert_small_cased,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
12207,google/t5-efficient-large-nl16,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0450309598818421,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
12208,google/t5-efficient-small-dm128,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0476536471396683,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
12209,google/t5-efficient-large-kv32,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0422788485884666,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
12210,sentence-transformers/paraphrase-MiniLM-L3-v2,A direct export of the model checkpoint was first made available in this repository,google/rembert,0.0244345273822546,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
12211,readerbench/RoBERT-small,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,af9865c1-d52f-4c4d-8696-c19b9085823b___RoBERT-small,HF
12212,google/t5-efficient-small-kv128,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0035094153136015,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
12213,allenai/wmt16-en-de-12-1,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
12214,prajjwal1/bert-mini,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0360650323331355,-1.0,NLP,d524d2bf-0c8c-4e35-817b-27b8261b2166___bert-mini,HF
12215,google/reformer-enwik8,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0427703292419512,-1.0,NLP,6aa23d61-51e8-44d6-b6b7-b68cce7cd0c1___reformer-enwik8,HF
12216,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
12217,microsoft/layoutlm-base-cased,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0512148439884185,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12218,google/t5-efficient-base-nh24,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0861960500478744,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
12219,skt/ko-gpt-trinity-1.2B-v0.5,Source language: English,NYTK/translation-marianmt-en-hu,0.0800797827541827,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
12220,bigjoedata/rockbot,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
12221,google/t5-efficient-base-nl40,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.097543217241764,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
12222,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,I have made no attempts to censor,bigjoedata/rockbot,0.01929303817451,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
12223,TsinghuaAI/CPM-Generate,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0103604053812367,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
12224,google/t5-efficient-small-dm256,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0179595947265625,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
12225,google/t5-efficient-xl-nl28,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0597264580428599,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
12226,fnlp/bart-base-chinese,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
12227,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0051067524279157,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
12228,google/t5-efficient-small-el8,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0402158284559845,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
12229,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0786046758294105,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
12230,google/t5-efficient-small-el64,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0886052586138248,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
12231,malay-huggingface/t5-small-bahasa-cased,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0119959240158399,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
12232,google/t5-efficient-small-el8,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0678962059319019,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
12233,Parsa/BBB_prediction_classification_SMILES,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0192626410474379,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
12234,google/t5-efficient-xl-nl16,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0611225292086601,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
12235,TJMUCH/transcriptome-iseeek,This is a dummy model that can be used for testing the transformers pipeline with the task sentiment-analysis,dhpollack/distilbert-dummy-sentiment,0.0183770433068275,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12236,google/t5-efficient-small-dm1000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0330777149647474,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
12237,google/t5-efficient-tiny-nh32,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
12238,superspray/electra_large_discriminator_squad2_custom_dataset,It was introduced in this paper,readerbench/RoBERT-small,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
12239,google/t5-efficient-small-nl24,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0731355808675288,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
12240,oskrmiguel/mt5-simplification-spanish,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0535979662090539,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
12241,cammy/led-base-16384-100-MDS,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0563493371009826,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
12242,groar/gpt-neo-1.3B-finetuned-escape2,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0984302759170532,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
12243,beston91/gpt2-xl_ft_logits_10k,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0142734423279762,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
12244,superspray/electra_large_discriminator_squad2_custom_dataset,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0881586819887161,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
12245,google/t5-efficient-small-nl48,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.071843821555376,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
12246,google/t5-efficient-tiny-nl32,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0904268100857734,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
12247,indobenchmark/indobert-base-p1,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0191578188290198,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
12248,DemangeJeremy/4-sentiments-with-flaubert,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0237521519884467,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
12249,zoha/wav2vec2-base-timit-demo-colab,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0602572634816169,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
12250,superspray/electra_large_discriminator_squad2_custom_dataset,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.0610702857375144,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
12251,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0626753270626068,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
12252,minimaxir/magic-the-gathering,As a consequence the model still has good performance in English language,Cedille/fr-boris,0.0618595983833074,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
12253,google/t5-efficient-small-nl22,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0503618232905864,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
12254,nghuyong/ernie-tiny,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,1.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
12255,stas/mt5-tiny-random,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0468706712126731,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
12256,stas/t5-very-small-random,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.091063916683197,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
12257,amazon/bort,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12258,stas/t5-very-small-random,"We exploit Unihan, a ready-made database constructed by linguistic experts to first merge morphologically similar characters into clusters",microsoft/unihanlm-base,0.062717966735363,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
12259,mikaelsouza/msft-regular-model,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0968201756477356,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
12260,akdeniz27/deberta-v2-xlarge-cuad,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0855384841561317,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
12261,krevas/finance-koelectra-base-generator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0288606509566307,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
12262,allenai/wmt19-de-en-6-6-base,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0580794773995876,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
12263,hajime9652/xlnet-japanese,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0646482408046722,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
12264,Recognai/zeroshot_selectra_small,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0238055611650149,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
12265,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0525044463574886,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
12266,gilparmentier/pokemon_gptj_model,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
12267,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,This is an mT5-based model for machine translation (Persian -&gt; English),persiannlp/mt5-large-parsinlu-opus-translation_fa_en,1.0,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
12268,google/t5-efficient-large-dm128,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0487880092114209,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
12269,IDEA-CCNL/Erlangshen-Longformer-110M,We do hope our model might be used for both academic and industrial applications,asi/gpt-fr-cased-base,0.0661124568432569,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
12270,ian0delond/model-test,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0201747491955757,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
12271,monsoon-nlp/dv-muril,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0522927707061171,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
12272,microsoft/layoutlm-base-cased,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0597820356488227,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12273,diper1998/distilgpt2-finetuned-AT,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
12274,google/t5-efficient-large-nl32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.006070140749216,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
12275,google/t5-efficient-base-kv128,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0344035271555185,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
12276,tau/tavbert-he,You can find more information in our paper AraGPT2The code in this repository was used to train all GPT2 variants,aubmindlab/aragpt2-mega,0.0672487337142229,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
12277,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
12278,flax-community/gpt2-medium-persian,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0105279317746559,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
12279,google/t5-efficient-tiny-nl24,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0309737883508205,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
12280,yarongef/DistilProtBert,"For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github",sarnikowski/convbert-medium-small-da-cased,0.0797797056535879,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
12281,tuner007/pegasus_qa,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0837507098913192,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
12282,google/t5-efficient-large-kv256,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0417026951909064,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
12283,tau/tavbert-he,T5-Efficient-SMALL-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl48,0.0339206121861934,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
12284,allenai/wmt16-en-de-12-1,I have made no attempts to censor,bigjoedata/rockbot,0.0700647141784429,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
12285,crabz/distil-slovakbert-ner,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0548165068030357,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
12286,lgrobol/electra-minuscule-generator,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0282030925154685,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
12287,google/t5-efficient-large-el4,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0183192361146211,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
12288,google/t5-efficient-large-nh2,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0821763202548026,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
12289,KoichiYasuoka/roberta-base-ukrainian,"This model uses ELECTRA Small model settings, 12 layers, 128 dimensions of hidden states, and 12 attention heads",ptaszynski/yacis-electra-small-japanese,0.0718545988202095,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
12290,mwesner/reformer-clm,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,0.0851107537746429,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12291,FremyCompany/xls-r-nl-v1-cv8-lm,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0442567504942417,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
12292,google/t5-efficient-large-nh8-nl32,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
12293,superb/hubert-base-superb-er,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
12294,zoha/wav2vec2-base-timit-demo-colab,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0336288958787918,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
12295,IDEA-CCNL/Erlangshen-Longformer-330M,The model is case-sensitive,tau/splinter-large-qass,0.0017636353150009,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
12296,google/t5-efficient-large-el2,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0577056761831045,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
12297,google/t5-efficient-tiny-ff9000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.048067926429212,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
12298,hfl/rbt3,T5-Efficient-SMALL-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl48,0.0577547317370772,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
12299,google/t5-efficient-tiny-nl6,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0552210025489329,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
12300,kyryl0s/gpt2-uk-xxs,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0441652918234467,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
12301,google/t5-efficient-large-nh8-nl32,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0602715481072663,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
12302,Milos/slovak-gpt-j-162M,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0637889616191386,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
12303,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Disclaimer: This model card has been written by gchhablani,google/fnet-large,0.0870213359594345,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12304,google/t5-efficient-small-el12,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0647279284894466,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
12305,ErykWdowiak/GPTalian,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.006959100998938,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
12306,google/t5-efficient-small-ff6000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0544866081327199,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
12307,SetFit/deberta-v3-large__sst2__train-8-8,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
12308,superspray/electra_large_discriminator_squad2_custom_dataset,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
12309,google/t5-efficient-tiny-ff3000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0387141546234488,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
12310,google/t5-efficient-tiny-nh16,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
12311,negfir/distilbert-base-uncased-finetuned-squad,It was introduced in this paper,readerbench/RoBERT-small,0.0020536361262202,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
12312,nherve/flaubert-oral-asr,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0318330774704615,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
12313,SupriyaArun/squeezebert-uncased-finetuned-squad,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0750943720340728,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
12314,google/t5-efficient-small-kv16,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0586241520941257,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
12315,nghuyong/ernie-tiny,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0641390395661194,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
12316,anirudh21/xlnet-base-cased-finetuned-rte,"Using this approach, the answer recall rate increases by a large margin, especially at small numbers of top-k",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0528438091278076,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
12317,google/bert_uncased_L-8_H-256_A-4,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0170423748592535,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
12318,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12319,google/t5-efficient-tiny-nl12,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0471270233392715,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
12320,cammy/led-large-16384-arxiv-100-MDS,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.042650531977415,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
12321,google/t5-efficient-base-nh32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0325348312035202,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
12322,google/t5-efficient-small-el16-dl4,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
12323,jonatasgrosman/wav2vec2-xls-r-1b-english,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0765310376882552,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
12324,uer/bart-chinese-4-768-cluecorpussmall,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0253036497160791,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
12325,DemangeJeremy/4-sentiments-with-flaubert,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0931547917425631,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
12326,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.0377774015069007,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
12327,saibo/random-roberta-tiny,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
12328,camembert/camembert-base-oscar-4gb,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
12329,jpcorb20/pegasus-large-reddit_tifu-samsum-512,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
12330,mmaguero/gn-bert-tiny-cased,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.094262022525072,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
12331,lgrobol/electra-minuscule-discriminator,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0662632472813128,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
12332,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Pre-Training with Whole Word Masking for Chinese BERTYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping HuThis repository is developed based on：https://github",hfl/rbtl3,0.0861742943525314,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
12333,allenai/wmt19-de-en-6-6-base,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0614419691264629,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
12334,google/t5-efficient-large-dm512,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0325710354372858,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
12335,google/t5-efficient-small-nl48,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-nl48,1.0,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
12336,google/t5-efficient-base-nl36,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0748308040201663,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
12337,google/t5-efficient-large-nl20,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0505354069173335,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
12338,ian0delond/model-test,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0475495979189872,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
12339,uer/t5-v1_1-small-chinese-cluecorpussmall,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0329882903024554,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
12340,hfl/rbtl3,T5-Efficient-BASE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el8,0.0206696055829524,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
12341,Davlan/mbart50-large-eng-yor-mt,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0507109324846948,-1.0,NLP,12474c75-dc49-473e-bcb6-eea8354b1253___mbart50-large-eng-yor-mt,HF
12342,google/t5-efficient-xl-nl2,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0588439106941222,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
12343,malay-huggingface/t5-small-bahasa-cased,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0682907265921433,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
12344,hfl/rbtl3,T5-Efficient-SMALL-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff3000,0.0566063998267054,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
12345,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0767721720039844,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
12346,dexhrestha/Nepali-DistilBERT,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,1.0,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
12347,google/t5-efficient-base-nl24,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0123570635914802,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
12348,google/t5-efficient-small-el64,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0417286455631255,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
12349,beston91/gpt2-xl_ft_logits_10k,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0847465246915817,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
12350,google/t5-efficient-base-el8,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0529218688607215,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
12351,TJMUCH/transcriptome-iseeek,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.052560344338417,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12352,Parsa/BBB_prediction_classification_SMILES,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0309349677215019,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
12353,mldmm/GlassBERTa,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0230697018560021,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
12354,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0356674827635288,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
12355,DTAI-KULeuven/robbertje-1-gb-bort,"Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa)",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0431905134270588,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
12356,Gilles/FongBERT,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
12357,mimicheng/codeparrot-ds,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
12358,DTAI-KULeuven/robbertje-1-gb-bort,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
12359,google/t5-11b-ssm-nqo,Note: The model was fine-tuned on 90% of the train splits of Natural Questions (NQ) for 20k steps and validated on the held-out 10% of the train split,google/t5-11b-ssm-nqo,1.0,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
12360,microsoft/layoutlm-base-uncased,"Hebrew poetry text generation model, fined tuned upon hebrew-gpt_neo-tiny which was trained using EleutherAI's gpt-neo",Norod78/hebrew_poetry-gpt_neo-tiny,0.0490710325539112,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
12361,stas/pegasus-cnn_dailymail-tiny-random,Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0402813656255602,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
12362,google/t5-efficient-small-dm128,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0536740086972713,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
12363,1one5ome/gpt2-chinese-gulong,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0580341890454292,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
12364,ToToKr/kobigbird-bert-base-finetuned-klue,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
12365,facebook/blenderbot-90M,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,1.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
12366,tau/tavbert-he,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
12367,everdoubling/byt5-Korean-small,The architecture requires two separate models,enelpol/czywiesz-context,0.0335384368896483,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
12368,google/t5-efficient-base-ff1000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.052876342087984,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
12369,IDEA-CCNL/Erlangshen-MegatronBert-1.3B,"The Erlangshen series will continue to be optimized in terms of model scale, knowledge integration, and supervision task assistance",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,1.0,-1.0,NLP,3c1adb6c-e774-450e-967f-c26c07931f49___Erlangshen-MegatronBert-1.3B,HF
12370,google/t5-efficient-large-nl10,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0338633563369512,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
12371,tartuNLP/gpt-4-est-large,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.078912153840065,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
12372,IDEA-CCNL/Zhouwenwang-Unified-110M,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
12373,lannelin/bert-imdb-1hidden,It was introduced in this paper,readerbench/RoBERT-small,0.0311030795176823,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
12374,IDEA-CCNL/Erlangshen-Longformer-110M,We welcome all experts and scholars to download and use this model,hfl/chinese-xlnet-mid,0.0211765673011541,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
12375,google/t5-efficient-small-ff3000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0532722398638724,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
12376,stas/pegasus-cnn_dailymail-tiny-random,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0499115288257598,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
12377,stas/pegasus-cnn_dailymail-tiny-random,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0688026454299688,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
12378,google/t5-efficient-small-nl36,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
12379,google/t5-efficient-small-ff6000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0848604515194892,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
12380,ptaszynski/yacis-electra-small-japanese,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
12381,google/t5-efficient-large-el4,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0480402708053588,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
12382,google/t5-efficient-tiny-nl24,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0345969647169112,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
12383,google/t5-efficient-base-ff9000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0546999983489512,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
12384,wukevin/tcr-bert,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0395104903727769,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
12385,diper1998/distilgpt2-finetuned-AT,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
12386,superspray/electra_large_discriminator_squad2_custom_dataset,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0412447452545166,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
12387,microsoft/layoutlm-large-uncased,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,0.056502029299736,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
12388,minimaxir/magic-the-gathering,Check out our paper DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts for more details,yarongef/DistilProtBert,0.0663182400166988,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
12389,google/t5-efficient-small-kv256,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-kv256,1.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
12390,google/t5-efficient-base-nl32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0623496025800704,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
12391,sentence-transformers/paraphrase-MiniLM-L3-v2,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
12392,aditeyabaral/sentencetransformer-bert-hinglish-small,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0600237175822258,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
12393,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0254561696201562,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
12394,beston91/gpt2-xl_ft_logits_10k,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0352670475840568,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
12395,google/t5-efficient-large-nl36,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0342629281803965,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
12396,google/t5-efficient-tiny-nl24,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.047063861042261,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
12397,google/t5-efficient-small-dm2000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0216536354273558,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
12398,google/t5-efficient-large-dm2000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0739517845213413,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
12399,google/t5-efficient-small-nl24,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0131108528003096,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
12400,hakurei/gpt-j-random-tinier,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0206717308610677,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
12401,microsoft/layoutlm-large-uncased,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0349774472415447,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
12402,google/t5-efficient-tiny-ff6000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
12403,google/t5-efficient-large-kv32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
12404,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0599882118403911,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
12405,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0708710476756095,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12406,mimicheng/codeparrot-ds,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0963732451200485,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
12407,fnlp/bart-large-chinese,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.033745314925909,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
12408,TJMUCH/transcriptome-iseeek,This model is a fine-tuned version of vinai/phobert-large on the vietnamese_students_feedback dataset,ncduy/phobert-large-finetuned-vietnamese_students_feedback,0.0569222196936607,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12409,google/t5-efficient-small-ff6000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.06013710424304,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
12410,google/t5-efficient-tiny-nh1,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-nh1,1.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
12411,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team,funnel-transformer/small,0.043842939659953,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
12412,Norod78/hebrew_poetry-gpt_neo-tiny,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0426998897455633,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
12413,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12414,Fujitsu/pytorrent,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
12415,CuongLD/wav2vec2-large-xlsr-vietnamese,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0449249371886253,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
12416,google/t5-efficient-large-nl10,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0453184377402066,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
12417,google/t5-efficient-tiny-nl12,This does not make a very readable text,pere/DeUnCaser,0.0729837603867053,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
12418,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12419,google/t5-efficient-xxl-nl4,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.06204797886312,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
12420,allenai/wmt19-de-en-6-6-base,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
12421,aditeyabaral/sentencetransformer-bert-hinglish-small,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0241021066904068,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
12422,oskrmiguel/mt5-simplification-spanish,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.040848733857274,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
12423,skt/ko-gpt-trinity-1.2B-v0.5,Tokenization algorithm is WordPiece,ctoraman/RoBERTa-TR-medium-wp-66k,0.0630808155983685,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
12424,stas/mt5-tiny-random,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0764215737581253,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
12425,1one5ome/gpt2-chinese-gulong,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0069548310711979,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
12426,google/t5-efficient-small-nl22,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0633642300963401,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
12427,camembert/camembert-base-oscar-4gb,CamemBERT is a state-of-the-art language model for French based on the RoBERTa model,camembert/camembert-base-oscar-4gb,1.0,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
12428,KoichiYasuoka/roberta-base-ukrainian,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 240",eli4s/Bert-L12-h240-A12,0.0439542420208454,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
12429,funnel-transformer/large-base,Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team,funnel-transformer/large-base,1.0,-1.0,NLP,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
12430,tuner007/pegasus_qa,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0960351526737213,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
12431,hfl/rbtl3,T5-Efficient-BASE-EL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el16,0.0273123085498808,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
12432,rinna/japanese-gpt-1b,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0270598928133646,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
12433,google/t5-efficient-small-el64,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0495528923347592,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
12434,google/t5-efficient-mini,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0678978227078914,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
12435,anirudh21/xlnet-base-cased-finetuned-rte,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0007186084985733,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
12436,amazon/bort,Model is based on GPT-J and has over 405M trainable parameters,Milos/slovak-gpt-j-405M,0.0615930315107106,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12437,google/t5-efficient-base-kv32,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0961226113140582,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
12438,tartuNLP/gpt-4-est-large,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
12439,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-SMALL-EL64 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el64,0.0829984471201896,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
12440,google/t5-efficient-small-dm768,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
12441,microsoft/layoutlm-large-uncased,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0499178431928157,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
12442,mmaguero/gn-bert-tiny-cased,T5-Efficient-BASE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el6,0.0818604715168475,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
12443,google/t5-efficient-large-el4,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
12444,thu-coai/LongLM-large,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0369028622905413,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
12445,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0672690346837043,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
12446,allenai/wmt19-de-en-6-6-base,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0230974424630403,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
12447,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0600646398961544,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
12448,google/t5-efficient-small-dm2000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0660488456487655,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
12449,google/t5-efficient-large-nh24,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0750076733529567,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
12450,google/t5-efficient-base-nl2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0360043477267026,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
12451,tartuNLP/gpt-4-est-large,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0409591495990753,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
12452,google/t5-efficient-tiny-ff9000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0575290583074091,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
12453,mwesner/reformer-clm,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12454,google/t5-efficient-small-dm256,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0479972120374441,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
12455,google/t5-efficient-base-nl4,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0664505399763583,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
12456,microsoft/layoutlm-base-cased,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-768_A-12,0.0504998192191123,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12457,Milos/slovak-gpt-j-1.4B,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0006782889831811,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
12458,google/t5-efficient-xl-nl6,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0686512812972068,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
12459,google/t5-efficient-small-ff1000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
12460,tucan9389/kcbert-base-finetuned-squad,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0498542785644531,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
12461,google/t5-efficient-base-nl2,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0564019083976745,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
12462,kyryl0s/gpt2-uk-xxs,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,1.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
12463,jhu-clsp/bibert-ende,This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU,kssteven/ibert-roberta-large,0.0181181225925684,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
12464,obss/mt5-base-3task-highlight-tquad2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0223289667628704,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
12465,google/t5-efficient-small-ff1000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-ff1000,1.0,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
12466,filco306/gpt2-switchboard-paraphraser,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0230638738721608,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
12467,malay-huggingface/t5-super-tiny-bahasa-cased,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
12468,tau/splinter-base-qass,The model is case-sensitive,tau/splinter-base-qass,1.0,-1.0,NLP,f68d3e26-2d96-4b99-a309-34b532e9740f___splinter-base-qass,HF
12469,sberbank-ai/rugpt3medium_based_on_gpt2,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,1.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
12470,jkeruotis/LitBERTa-uncased,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0450974376872181,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
12471,qarib/bert-base-qarib_far_6500k,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0311322910711169,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
12472,google/t5-efficient-small-ff9000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.041015401482582,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
12473,Norod78/hebrew_poetry-gpt_neo-tiny,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,0.0766596570610999,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
12474,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.039396695792675,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12475,google/t5-efficient-base-ff6000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0672067441046237,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
12476,stas/t5-very-small-random,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,0.0840124189853668,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
12477,hfl/rbt3,This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset,brad1141/Longformer-finetuned-norm,0.0950696505606173,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
12478,google/t5-efficient-large-nh4,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0412670085206628,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
12479,allenai/macaw-answer-11b,A direct export of the model checkpoint was first made available in this repository,google/rembert,0.0421049123009045,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
12480,uer/t5-v1_1-base-chinese-cluecorpussmall,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
12481,tuner007/pegasus_qa,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.019716428592801,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
12482,negfir/distilbert-base-uncased-finetuned-cola,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0949791893362999,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
12483,amazon/bort,The checkpoint used here is the original MobileBert Optimized Uncased English: (uncased_L-24_H-128_B-512_A-4_F-4_OPT) checkpoint,mrm8488/mobilebert-uncased-finetuned-squadv2,0.0036281677894294,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12484,minimaxir/magic-the-gathering,"Besides, many tasks may be formatted such that the output is directly generated in natural language",asi/gpt-fr-cased-base,0.0615972429513931,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
12485,google/t5-efficient-large-nl16,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0348540935665368,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
12486,diper1998/distilgpt2-finetuned-AT,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0522264018654823,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
12487,google/t5-efficient-base-kv256,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0088763106614351,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
12488,hfl/chinese-electra-180g-small-ex-generator,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0558131368209918,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
12489,mrp/simcse-model-wangchanberta,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0088185500353574,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
12490,google/t5-efficient-xxl-nl4,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0582416485995054,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
12491,google/t5-efficient-small-el4,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0528152957558631,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
12492,google/t5-efficient-small-el64,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
12493,rinna/japanese-gpt-1b,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0130734381576378,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
12494,rahulacj/bertweet-base-finetuned-sentiment-analysis,The training code is available here: SBERT,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0392265953123569,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
12495,mrm8488/xlm-multi-finetuned-xquadv1,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0593632142990827,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
12496,mrm8488/bert-small-finetuned-squadv2,The hypothesis_template parameter is important and should be in Spanish,Recognai/zeroshot_selectra_small,0.012894444167614,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
12497,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0261503644287586,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
12498,SetFit/deberta-v3-large__sst2__train-8-8,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.098064586520195,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
12499,malay-huggingface/bert-tiny-bahasa-cased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0523588126525282,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
12500,kyryl0s/gpt2-uk-xxs,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0516993831843137,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
12501,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0419262796640396,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12502,ian0delond/model-test,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
12503,google/t5-efficient-small-ff1000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0513107609003782,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
12504,tau/tavbert-he,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.0718402825295924,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
12505,google/t5-efficient-base-el6,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0170511924661696,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
12506,NovelAI/genji-python-6B,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0509770984450975,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
12507,google/t5-efficient-base-nl32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
12508,IDEA-CCNL/Erlangshen-Longformer-330M,This model is a useful encapsulation of gene ontology functions,damlab/GO-language,0.0624352097511291,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
12509,aditeyabaral/sentencetransformer-bert-hinglish-small,We welcome all experts and scholars to download and use this model,hfl/chinese-xlnet-mid,0.0963687747716903,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
12510,rinna/japanese-gpt-1b,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0621938556432723,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
12511,anas-awadalla/bert-tiny-finetuned-squad,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0992212742567062,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
12512,mwesner/reformer-clm,A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including Strixhaven and Commander 2021,minimaxir/magic-the-gathering,0.0956994220614433,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12513,stas/pegasus-cnn_dailymail-tiny-random,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0452438499778508,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
12514,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,This model is a fine-tuned version of google/bigbird-pegasus-large-bigpatent on the pub_med_summarization_dataset dataset,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,1.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
12515,google/t5-efficient-base-dm1000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0399202099069952,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
12516,jcblaise/electra-tagalog-small-cased-generator,"For the actual model for downstream tasks, please refer to the discriminator models",jcblaise/electra-tagalog-small-cased-generator,1.0,-1.0,NLP,f4e25246-ebec-4c97-8a1d-5ef296f2f888___electra-tagalog-small-cased-generator,HF
12517,rdenadai/BR_BERTo,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,0.0367894774923722,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
12518,Milos/slovak-gpt-j-405M,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0562042978902658,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
12519,sentence-transformers/paraphrase-MiniLM-L3-v2,"This is the set of Chinese T5 Version 1.1 models pre-trained by UER-py, which is introduced in this paper",uer/t5-v1_1-small-chinese-cluecorpussmall,0.0854916647076606,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
12520,razent/SciFive-base-PMC,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0721211060881614,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
12521,IDEA-CCNL/Erlangshen-Longformer-330M,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0689198598265647,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
12522,google/t5-efficient-mini,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0034693609923124,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
12523,facebook/wmt21-dense-24-wide-en-x,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0173477422446012,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
12524,IlyaGusev/rugpt3medium_sum_gazeta,"Since it has 4 attention heads, the head size is 64 just as for the BERT base model",eli4s/Bert-L12-h256-A4,0.0450217109173536,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
12525,google/reformer-crime-and-punishment,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.032163625350222,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
12526,google/t5-efficient-large-dm2000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
12527,allenai/wmt19-de-en-6-6-base,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0797216705977916,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
12528,mwesner/reformer-clm,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0099141718819737,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12529,google/t5-efficient-large-dm2000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-dm2000,1.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
12530,lassl/gpt2-ko-small,"6,831,079 examples are extracted from below corpora",lassl/gpt2-ko-small,1.0,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
12531,Milos/slovak-gpt-j-405M,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
12532,negfir/distilbert-base-uncased-finetuned-squad,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
12533,allenai/macaw-answer-11b,"To use CPT, please import the file modeling_cpt",fnlp/cpt-base,0.0455173812806606,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
12534,mnaylor/bigbird-base-mimic-mortality,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0221524122171104,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
12535,GKLMIP/electra-myanmar-small-uncased,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0196572365239262,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
12536,ToToKr/kobigbird-bert-base-finetuned-klue,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
12537,google/t5-efficient-small-ff12000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0462614763528109,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
12538,google/t5-efficient-small-dm1000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
12539,razent/SciFive-base-PMC,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0819265209138393,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
12540,google/t5-efficient-large-nh4,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
12541,aditeyabaral/sentencetransformer-distilbert-hinglish-big,You can find more information in our paper AraGPT2The code in this repository was used to train all GPT2 variants,aubmindlab/aragpt2-mega,0.066152885556221,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12542,mwesner/reformer-clm,T5-Efficient-BASE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl36,0.0937300324440002,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12543,sentence-transformers/paraphrase-MiniLM-L3-v2,This repository provides a 1.3B-parameter Japanese GPT model,rinna/japanese-gpt-1b,0.094286248087883,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
12544,GKLMIP/electra-myanmar-small-uncased,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0671621784567832,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
12545,allenai/wmt19-de-en-6-6-big,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
12546,piotr-rybak/poleval2021-task4-herbert-large-encoder,"This is the set of Chinese T5 Version 1.1 models pre-trained by UER-py, which is introduced in this paper",uer/t5-v1_1-small-chinese-cluecorpussmall,0.0783935859799385,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
12547,google/t5-efficient-xxl-nl4,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0698311030864715,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
12548,google/t5-efficient-small-nl40,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0689588822424411,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
12549,google/t5-efficient-xl-nl8,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0006396546959876,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
12550,allenai/macaw-3b,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0526567269116639,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
12551,google/t5-efficient-base-nl36,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.025143088772893,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
12552,google/t5-efficient-small-nl36,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
12553,TJMUCH/transcriptome-iseeek,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12554,prajjwal1/bert-mini,The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository,prajjwal1/bert-mini,1.0,-1.0,NLP,d524d2bf-0c8c-4e35-817b-27b8261b2166___bert-mini,HF
12555,google/t5-efficient-small-el64,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
12556,negfir/distilbert-base-uncased-finetuned-cola,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.001546049490571,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
12557,google/t5-efficient-large-nh8-nl32,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0834540724754333,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
12558,negfir/distilbert-base-uncased-finetuned-squad,"It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed",phueb/BabyBERTa-2,0.0976579785346984,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
12559,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
12560,google/t5-efficient-large-el4,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0776723735034465,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
12561,google/t5-efficient-large-el2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0201195077970623,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
12562,oskrmiguel/mt5-simplification-spanish,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0329415276646614,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
12563,google/t5-efficient-tiny-ff3000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0444613294675945,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
12564,DemangeJeremy/4-sentiments-with-flaubert,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
12565,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0665457993745803,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
12566,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0243087150156497,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
12567,VietAI/gpt-neo-1.3B-vietnamese-news,The model was created by fine-tuning  Wav2Vec2-Large-Robust on MSP-Podcast (v1.7),audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0556795299053192,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
12568,google/t5-efficient-large-kv128,Its original repository can be found here,tau/splinter-large-qass,0.0680848378688096,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
12569,sberbank-ai/rugpt3large_based_on_gpt2,After that model was finetuned 1 epoch with sequence length 2048,sberbank-ai/rugpt3large_based_on_gpt2,1.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
12570,google/t5-efficient-large-nh2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
12571,piotr-rybak/poleval2021-task4-herbert-large-encoder,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0948039814829826,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
12572,google/bert_uncased_L-8_H-128_A-2,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0170423748592535,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
12573,flax-community/gpt2-medium-persian,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0584352612495421,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
12574,minimaxir/magic-the-gathering,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0593769121915101,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
12575,google/t5-efficient-base-nh32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
12576,google/t5-efficient-xl-nl8,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0869274288415908,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
12577,monsoon-nlp/dv-muril,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0315566087762514,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
12578,amazon/bort,Pretrained roberta-ko-small on korean language was trained by LASSL framework,lassl/roberta-ko-small,0.0653840787708758,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12579,google/t5-efficient-large-el4,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0273467693477868,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
12580,tau/tavbert-he,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.044722257182002,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
12581,cammy/led-large-16384-arxiv-100-MDS,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0572369657456874,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
12582,amazon/bort,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,mrp/simcse-model-wangchanberta,0.0576942209154366,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12583,google/t5-efficient-large-nh2,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
12584,google/t5-efficient-base-el6,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0111312083899974,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
12585,TransQuest/monotransquest-hter-en_lv-it-nmt,High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses,TransQuest/monotransquest-hter-en_lv-it-nmt,1.0,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
12586,google/t5-efficient-small-ff9000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0554127544164657,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
12587,clue/xlnet_chinese_large,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0728649795055389,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
12588,google/t5-efficient-small-ff3000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0538191329687833,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
12589,mwesner/reformer-clm,T5-Efficient-BASE-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl48,0.0734470039606094,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12590,rinna/japanese-gpt2-xsmall,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
12591,google/t5-efficient-tiny-nl6,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
12592,amazon/bort,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.019458383321762,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12593,funnel-transformer/large-base,Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion,funnel-transformer/large-base,1.0,-1.0,NLP,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
12594,google/t5-efficient-large-el6,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0455194357782601,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
12595,izumi-lab/bert-small-japanese-fin,This is a BERT model pretrained on texts in the Japanese language,izumi-lab/bert-small-japanese-fin,1.0,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
12596,google/t5-efficient-small-dm256,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0473340153694152,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
12597,google/t5-efficient-small-el8,Its original repository can be found here,tau/splinter-large-qass,0.0382679235190152,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
12598,IDEA-CCNL/Zhouwenwang-Unified-1.3B,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.011513813957572,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
12599,google/t5-efficient-base-nh8,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0298218894749879,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
12600,uer/pegasus-base-chinese-cluecorpussmall,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.085691001266241,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
12601,oskrmiguel/mt5-simplification-spanish,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0489689353853464,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
12602,tuner007/pegasus_qa,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0572303682565689,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
12603,google/t5-efficient-small-nl22,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0310365948826074,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
12604,ToToKr/kobigbird-bert-base-finetuned-klue,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0883814394474029,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
12605,google/t5-efficient-tiny-nh16,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0657769553363322,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
12606,lgrobol/electra-minuscule-generator,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
12607,stas/mt5-tiny-random,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0443168655037879,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
12608,mrm8488/bert-small-finetuned-squadv2,"'s model from their ""reflective decoding"" paper",danyaljj/opengpt2_pytorch_forward,0.0463482216000556,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
12609,TJMUCH/transcriptome-iseeek,The language model trained on a fill-mask task with all the North American parent's data in CHILDES,pulp/CHILDES-ParentBERTo,0.0671206563711166,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12610,allenai/wmt19-de-en-6-6-base,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0719840936362743,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
12611,beston91/gpt2-xl_ft_logits_10k,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
12612,google/t5-efficient-small-nl2,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0637881495058536,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
12613,Recognai/zeroshot_selectra_small,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0401441743597387,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
12614,google/t5-efficient-large-nh8,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
12615,google/t5-efficient-base-ff9000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0579627016559243,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
12616,rahulacj/bertweet-base-finetuned-sentiment-analysis,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0814278274774551,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
12617,google/bert_uncased_L-8_H-768_A-12,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0481574097648262,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
12618,dbmdz/electra-base-turkish-cased-discriminator,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0299390316940843,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
12619,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-LARGE-NH8-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8-nl32,0.0937438830733299,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
12620,rdenadai/BR_BERTo,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0473177346090475,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
12621,google/rembert,A direct export of the model checkpoint was first made available in this repository,google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
12622,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0036635771393775,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
12623,google/t5-efficient-base-nl48,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0118129346519709,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
12624,google/t5-efficient-large-nh24,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0559682957828044,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
12625,PlanTL-GOB-ES/gpt2-base-bne,GPT2-base-bne is a transformer-based model for the Spanish language,PlanTL-GOB-ES/gpt2-base-bne,1.0,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
12626,ian0delond/model-test,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.080702431499958,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
12627,ToToKr/kobigbird-bert-base-finetuned-klue,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0631798356771469,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
12628,google/bert_uncased_L-4_H-128_A-2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
12629,nherve/flaubert-oral-ft,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
12630,google/t5-efficient-small-kv16,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0596379209309815,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
12631,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0161159746348857,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
12632,dbmdz/electra-base-turkish-cased-discriminator,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0162474554963409,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
12633,google/t5-efficient-mini,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0370145654305815,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
12634,mikaelsouza/msft-regular-model,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.047561589628458,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
12635,google/t5-efficient-large-nh24,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0746198557317256,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
12636,google/t5-efficient-large-dm128,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.045653961598873,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
12637,google/t5-efficient-large-dm128,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
12638,google/t5-efficient-base-nl48,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
12639,tucan9389/kcbert-base-finetuned-squad,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0785083100199699,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
12640,mldmm/GlassBERTa,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0181155684404075,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
12641,hyunwoongko/reddit-9B,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0470694535012755,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
12642,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0724324285984039,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
12643,cosmoquester/bart-ko-base,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0235126689076423,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
12644,monsoon-nlp/dv-muril,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
12645,tuner007/pegasus_qa,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0067525901831686,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
12646,google/t5-efficient-base-kv128,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
12647,aditeyabaral/sentencetransformer-bert-hinglish-small,"As shown in the Metrics section, the small version (5 times fewer parameters) performs slightly worse, while the medium version (3 times fewer parameters) outperforms the BETO based zero-shot classifier",Recognai/zeroshot_selectra_small,0.0688305348157882,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
12648,google/t5-efficient-base-nh24,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.080658208578825,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
12649,ebagdasa/propaganda_positive_bart_large,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0287814798454443,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
12650,uer/t5-v1_1-small-chinese-cluecorpussmall,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0263285245746374,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
12651,google/t5-efficient-base-kv256,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0025845840573311,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
12652,google/t5-efficient-xl-nl28,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0506213624030351,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
12653,zoha/wav2vec2-base-timit-demo-colab,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0474213846027851,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
12654,ml6team/distilbart-tos-summarizer-tosdr,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
12655,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,0.0947665721178054,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12656,mrm8488/deberta-v3-small-finetuned-mrpc,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0252190418541431,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
12657,google/t5-efficient-base-dm1000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
12658,google/t5-efficient-large-nh8-nl32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
12659,stas/pegasus-cnn_dailymail-tiny-random,This is a tiny random  pegasus-cnn_dailymail model used for testing,stas/pegasus-cnn_dailymail-tiny-random,1.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
12660,IlyaGusev/rugpt3medium_sum_gazeta,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.023056037724018,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
12661,google/t5-efficient-base-ff6000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0492640933953225,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
12662,superb/hubert-base-superb-er,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0304926168173551,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
12663,beston91/gpt2-xl_ft_logits_10k,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
12664,google/t5-efficient-tiny-nl16,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0679480805993079,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
12665,google/t5-efficient-small-ff12000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0194940357469021,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
12666,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0138187557458877,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
12667,gayanin/bart-mlm-pubmed-medterm,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0804259404540062,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
12668,malay-huggingface/t5-super-tiny-bahasa-cased,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
12669,google/t5-efficient-base-el16,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.09818921610713,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
12670,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0130429686978459,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
12671,microsoft/layoutlm-base-cased,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0737722367048263,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12672,akdeniz27/deberta-v2-xlarge-cuad,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
12673,google/t5-efficient-tiny-nl2,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
12674,google/t5-efficient-small-dm128,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
12675,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0510959327220916,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
12676,uer/bart-chinese-4-768-cluecorpussmall,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0413766223937272,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
12677,aditeyabaral/sentencetransformer-bert-hinglish-small,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0677051693201065,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
12678,everdoubling/byt5-Korean-small,"For the actual model for downstream tasks, please refer to the discriminator models",jcblaise/electra-tagalog-small-cased-generator,0.0248304761946201,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
12679,j-hartmann/purchase-intention-english-roberta-large,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0761878453195094,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
12680,google/t5-efficient-large-nh8-nl32,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0890851318836212,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
12681,bolbolzaban/gpt2-persian,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0688025299459695,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
12682,google/t5-efficient-small-dm768,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
12683,microsoft/layoutlm-large-uncased,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0631272196769714,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
12684,uer/chinese_roberta_L-6_H-128,"In order to facilitate users to reproduce the results, we used the publicly available corpus and provided all training details",uer/chinese_roberta_L-6_H-128,1.0,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
12685,google/t5-efficient-tiny-nl2,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0114508345723152,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
12686,google/t5-efficient-large-kv256,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
12687,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0431663393974304,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
12688,ErykWdowiak/GPTalian,This is a multilingual model,ErykWdowiak/GPTalian,1.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
12689,sentence-transformers/paraphrase-MiniLM-L3-v2,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,0.0045805498957633,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
12690,crabz/distil-slovakbert-ner,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.05416726693511,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
12691,VietAI/gpt-neo-1.3B-vietnamese-news,Model is based on GPT-J and has over 405M trainable parameters,Milos/slovak-gpt-j-405M,0.0315711619332432,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
12692,lgrobol/electra-minuscule-discriminator,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
12693,camembert/camembert-base-oscar-4gb,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0726519276698429,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
12694,hfl/chinese-electra-small-ex-discriminator,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
12695,microsoft/layoutlm-large-uncased,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
12696,brad1141/Longformer-finetuned-norm,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
12697,google/t5-efficient-small-kv16,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0633713779971003,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
12698,jkeruotis/LitBERTa-uncased,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0376294981688261,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
12699,wukevin/tcr-bert,"This model is on:If you are looking for a model trained only on MAA, please see our other model",wukevin/tcr-bert,1.0,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
12700,mimi/Waynehills-NLP-doogie,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0671658664941787,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
12701,mrm8488/mobilebert-uncased-finetuned-squadv2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0263445588449636,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
12702,akdeniz27/deberta-v2-xlarge-cuad,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0246106944978237,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
12703,groar/gpt-neo-1.3B-finetuned-escape2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
12704,tau/tavbert-he,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
12705,negfir/distilbert-base-uncased-finetuned-cola,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0979298576712608,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
12706,tartuNLP/gpt-4-est-large,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0549527816474437,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
12707,google/t5-efficient-base-el16,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
12708,rdenadai/BR_BERTo,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0457439174254735,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
12709,everdoubling/byt5-Korean-small,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
12710,google/t5-efficient-small-nl36,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0418359301984309,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
12711,google/t5-efficient-base-nl32,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
12712,TJMUCH/transcriptome-iseeek,Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective,google/fnet-large,0.0881556347012519,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12713,tobiaslee/bert-2l-768h-uncased,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.057051433250308,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
12714,sberbank-ai/rugpt3large_based_on_gpt2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
12715,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0390293896198272,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
12716,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0721275955438613,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
12717,mrm8488/bert-small-finetuned-squadv2,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0749455615878104,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
12718,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0938608571887016,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
12719,google/t5-efficient-tiny-nl16,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0730893164873123,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
12720,negfir/distilbert-base-uncased-finetuned-squad,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0809797644615173,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
12721,hfl/chinese-xlnet-mid,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
12722,tucan9389/kcbert-base-finetuned-squad,This does not make a very readable text,pere/DeUnCaser,0.0672871544957161,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
12723,fnlp/bart-large-chinese,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
12724,stas/mt5-tiny-random,"You can fine-tune roberta-base-ukrainian for downstream tasks, such as POS-tagging, dependency-parsing, and so on",KoichiYasuoka/roberta-base-ukrainian,0.031638853251934,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
12725,google/t5-efficient-small-nl24,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.040501870214939,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
12726,cosmoquester/bart-ko-mini,I used multiple datasets for generalizing the model for both colloquial and written texts,cosmoquester/bart-ko-mini,1.0,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
12727,junnyu/roformer_base_wwm_cluecorpussmall,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0415624740223089,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
12728,allenai/wmt16-en-de-dist-6-1,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0862623825669288,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
12729,google/t5-efficient-base-el6,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0547357629984616,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
12730,google/t5-efficient-small-kv256,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0041347574442625,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
12731,KoichiYasuoka/roberta-base-ukrainian,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0464048832654953,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
12732,google/t5-efficient-tiny-nh32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0250805523246527,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
12733,jnz/electra-ka,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0983309596776962,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
12734,microsoft/layoutlm-base-cased,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0701058357954025,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12735,mgreenbe/bertlet-base-uncased-for-sequence-classification,I have made no attempts to censor,bigjoedata/rockbot,0.027515085414052,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
12736,mrm8488/deberta-v3-small-finetuned-mrpc,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
12737,GKLMIP/electra-myanmar-small-uncased,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0733851306140422,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
12738,sentence-transformers/paraphrase-MiniLM-L3-v2,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0613590143620967,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
12739,beston91/gpt2-xl_ft_logits_10k,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0502836667001247,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
12740,clue/roberta_chinese_3L312_clue_tiny,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0313908895477652,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
12741,google/t5-efficient-large-dm768,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0022260472178458,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
12742,IDEA-CCNL/Erlangshen-Longformer-330M,"In the v2 version, the Portuguese dataset was translated using Google Translate",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0829937234520912,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
12743,mmaguero/gn-bert-tiny-cased,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0489056659862399,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
12744,skt/ko-gpt-trinity-1.2B-v0.5,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0749740377068519,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
12745,microsoft/layoutlm-base-uncased,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0102054830640554,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
12746,google/fnet-large,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0112357065081596,-1.0,NLP,be8c3b54-acb9-41d3-9190-d3a35ba6f7a6___fnet-large,HF
12747,aditeyabaral/sentencetransformer-bert-hinglish-small,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
12748,abdusahmbzuai/ft-tatoeba-ar-en,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.058743841946125,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
12749,ian0delond/model-test,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
12750,IDEA-CCNL/Erlangshen-Longformer-330M,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
12751,google/t5-efficient-base-ff9000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0452099964022636,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
12752,google/t5-efficient-base-dm2000,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0266160424798727,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
12753,google/t5-efficient-small-nl40,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
12754,google/t5-efficient-small-dm2000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0571258664131164,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
12755,anirudh21/xlnet-base-cased-finetuned-rte,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0263726487755775,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
12756,TJMUCH/transcriptome-iseeek,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12757,mwesner/reformer-clm,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12758,GKLMIP/electra-myanmar-small-uncased,"Since it has 4 attention heads, the head size is 64 just as for the BERT base model",eli4s/Bert-L12-h256-A4,0.0554699655622243,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
12759,fnlp/cpt-base,This is an implementation of CPT-Base,fnlp/cpt-base,1.0,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
12760,malay-huggingface/bert-tiny-bahasa-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.013393131395181,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
12761,zoha/wav2vec2-base-timit-demo-colab,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0417691767215728,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
12762,google/t5-efficient-base-el8,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0639105625450611,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
12763,google/t5-efficient-large-kv32,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0574945621192455,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
12764,negfir/distilbert-base-uncased-finetuned-cola,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0380980819463729,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
12765,google/t5-efficient-large-nh12,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0629721302539109,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
12766,google/t5-efficient-large-kv128,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0416878061369061,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
12767,lgrobol/electra-minuscule-generator,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0134415328502655,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
12768,obss/mt5-base-3task-highlight-tquad2,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0409028464928269,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
12769,google/t5-efficient-large-el8,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0827389657497406,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
12770,mikaelsouza/msft-regular-model,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0837201550602912,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
12771,google/t5-efficient-tiny-nl6,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.00941370613873,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
12772,google/t5-efficient-tiny-ff3000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0637868158519267,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
12773,cross-encoder/stsb-TinyBERT-L-4,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0585737954825162,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
12774,mldmm/GlassBERTa,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
12775,google/t5-efficient-large-nh4,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
12776,google/t5-efficient-large-nh32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
12777,mnaylor/bigbird-base-mimic-mortality,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0352353295311331,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
12778,IDEA-CCNL/Erlangshen-Longformer-330M,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,0.0067863301374017,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
12779,aubmindlab/aragpt2-mega,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0076204854995011,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
12780,google/t5-efficient-base-nh8,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0430997665971517,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
12781,Palak/microsoft_deberta-base_squad,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0659384503960609,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
12782,abdusahmbzuai/aradia-ctc-distilhubert-ft,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0897196307778358,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
12783,google/t5-efficient-small-ff1000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0725269950926303,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
12784,superspray/electra_large_discriminator_squad2_custom_dataset,"Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa)",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0351704098284244,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
12785,ToToKr/kobigbird-bert-base-finetuned-klue,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
12786,mimi/Waynehills-NLP-doogie,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0664216503500938,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
12787,KoichiYasuoka/roberta-base-ukrainian,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0457148179411888,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
12788,soheeyang/rdr-ctx_encoder-single-trivia-base,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
12789,google/t5-efficient-large-dm256,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0300957690924406,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
12790,google/t5-efficient-small-ff3000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0108637474477291,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
12791,hfl/chinese-electra-180g-small-ex-generator,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0542604761819044,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
12792,Norod78/hebrew_poetry-gpt_neo-tiny,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0838611237704753,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
12793,brad1141/Longformer-finetuned-norm,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
12794,google/t5-efficient-tiny-nl32,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0578681230545043,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
12795,allenai/wmt16-en-de-dist-6-1,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0610625930130481,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
12796,hajime9652/xlnet-japanese,We open-source the model to allow greater usage within the Filipino NLP community,jcblaise/electra-tagalog-small-cased-generator,0.0742536932229995,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
12797,google/t5-efficient-base-nl40,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0637466832995414,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
12798,google/t5-efficient-small-dm1000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0606576744467019,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
12799,GKLMIP/electra-myanmar-small-uncased,This model was trained using the damlab/uniprot dataset on the go field with 256 token chunks and a 15% mask rate,damlab/GO-language,0.0903950408101081,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
12800,amazon/bort,"zip, use 180G of data to continue trainingThere is no structure of Longformer-base in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-110M,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12801,google/t5-efficient-small-nl22,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0662111472338437,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
12802,minimaxir/magic-the-gathering,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
12803,google/t5-efficient-large-nl36,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0521419290453195,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
12804,google/t5-efficient-large-nl16,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0746615417301654,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
12805,google/t5-efficient-small-ff9000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0064089121297002,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
12806,1one5ome/gpt2-chinese-gulong,"We collaborated with TOSDR to work with their data, and the model is finetuned accordingly",ml6team/distilbart-tos-summarizer-tosdr,0.0900865048170089,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
12807,google/t5-efficient-tiny-ff6000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
12808,negfir/distilbert-base-uncased-finetuned-cola,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0624735839664936,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
12809,google/t5-efficient-small-kv128,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.062896529212594,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
12810,google/t5-efficient-xl-nl16,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0912656188011169,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
12811,negfir/distilbert-base-uncased-finetuned-cola,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0074867717921733,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
12812,KoichiYasuoka/roberta-small-belarusian,This is a RoBERTa model pre-trained on CC-100,KoichiYasuoka/roberta-small-belarusian,1.0,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
12813,google/t5-efficient-tiny-nl2,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0716621167957782,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
12814,google/t5-efficient-base-nh24,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
12815,google/t5-efficient-base-kv256,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0543742049485444,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
12816,google/t5-efficient-base-nl40,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
12817,google/t5-efficient-xl-nl28,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0486732013523578,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
12818,tau/tavbert-he,T5-Efficient-BASE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh32,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
12819,j-hartmann/purchase-intention-english-roberta-large,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0282466855521003,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
12820,cross-encoder/stsb-TinyBERT-L-4,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
12821,tau/tavbert-he,T5-Efficient-LARGE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el6,0.0493200160562991,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
12822,google/t5-efficient-small-kv16,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0866666361689567,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
12823,google/t5-efficient-base-dm1000,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0904830060899257,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
12824,cross-encoder/ms-marco-MiniLM-L-2-v2,"Then, you can use the pre-trained models like this:",cross-encoder/ms-marco-MiniLM-L-2-v2,1.0,-1.0,NLP,c2d1bb44-2e13-4b2e-8b5d-23fa4c9a88e9___ms-marco-MiniLM-L-2-v2,HF
12825,google/t5-efficient-xl-nl8,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.021222224459052,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
12826,bigjoedata/rockbot,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0115936861063043,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
12827,google/t5-efficient-small-dm1000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.039240825921297,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
12828,lgrobol/electra-minuscule-discriminator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0151767544448375,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
12829,Milos/slovak-gpt-j-162M,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0424199560657143,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
12830,dhpollack/distilbert-dummy-sentiment,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0464419312775134,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
12831,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh8,0.0708436667919159,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
12832,google/t5-efficient-large-el6,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0756417103111743,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
12833,amazon/bort,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0464532258920371,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12834,hfl/rbtl3,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
12835,mrm8488/deberta-v3-small-finetuned-mrpc,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
12836,clue/roberta_chinese_3L312_clue_tiny,"The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0579327046871185,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
12837,tartuNLP/gpt-4-est-large,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0342008210718631,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
12838,jweb/japanese-soseki-gpt2-1b,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0507777935514847,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
12839,ToToKr/kobigbird-bert-base-finetuned-klue,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0455114692449569,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
12840,zoha/wav2vec2-base-timit-demo-colab,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0913988798856735,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
12841,google/t5-efficient-tiny-ff9000,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0791082680225372,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
12842,IDEA-CCNL/Erlangshen-Longformer-330M,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0385351926088333,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
12843,google/t5-efficient-large-kv32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.034519750624895,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
12844,minimaxir/magic-the-gathering,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
12845,amazon/bort,"All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0002166330814361,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12846,sentence-transformers/paraphrase-MiniLM-L3-v2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
12847,cosmoquester/bart-ko-small,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0136963352560996,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
12848,TJMUCH/transcriptome-iseeek,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.016284130513668,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12849,GKLMIP/electra-myanmar-small-uncased,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0172389559447765,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
12850,mrm8488/longformer-base-4096-spanish,longformer-base-4096 is a BERT-like model started from the RoBERTa checkpoint (BERTIN in this case) and pre-trained for MLM on long documents (from BETO's all_wikis),mrm8488/longformer-base-4096-spanish,1.0,-1.0,NLP,47fa5077-4d92-4913-a31a-b5f621200d4b___longformer-base-4096-spanish,HF
12851,google/t5-efficient-base-ff6000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0360996946692466,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
12852,google/t5-efficient-mini-nl8,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
12853,microsoft/layoutlm-base-cased,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.081086441874504,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12854,TsinghuaAI/CPM-Generate,"To the best of our knowledge, CPM is the largest Chinese pre-trained language model, which could facilitate downstream Chinese NLP tasks, such as conversation, essay generation, cloze test, and language understanding",TsinghuaAI/CPM-Generate,1.0,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
12855,DTAI-KULeuven/robbertje-1-gb-bort,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
12856,lannelin/bert-imdb-1hidden,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0641550285120804,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
12857,microsoft/layoutlm-base-cased,Source language: English,NYTK/translation-marianmt-en-hu,0.0630581602454185,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
12858,google/t5-efficient-small-el16-dl4,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0264244200661778,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
12859,stas/mt5-tiny-random,As a consequence the model still has good performance in English language,Cedille/fr-boris,0.095529019832611,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
12860,google/t5-efficient-large-dm2000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
12861,mwesner/reformer-clm,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0791859924793243,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
12862,obss/mt5-base-3task-highlight-tquad2,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0724923815578222,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
12863,google/t5-efficient-large-kv128,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0613054819405078,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
12864,Palak/microsoft_deberta-base_squad,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0607054829597473,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
12865,google/t5-efficient-large-nl12,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0350784547626972,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
12866,google/t5-efficient-base-dm512,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0203900933265685,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
12867,dexhrestha/Nepali-DistilBERT,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0318604311905801,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
12868,allenai/macaw-answer-11b,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,1.0,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
12869,google/t5-efficient-base-ff12000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0389817827381193,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
12870,google/t5-efficient-base-dm2000,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0235369578003883,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
12871,google/t5-efficient-small-el4,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0845937579870223,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
12872,gilparmentier/pokemon_gptj_model,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0230923807248472,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
12873,piotr-rybak/poleval2021-task4-herbert-large-encoder,There are also BiLSTM models available as well as these two models in 'https://github,Parsa/BBB_prediction_classification_SMILES,0.0376900024712085,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
12874,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0998480394482612,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
12875,google/t5-efficient-base-dm256,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
12876,suzuki/distilbert-base-uncased-finetuned-squad,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.001773253083229,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
12877,wukevin/tcr-bert,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.037926010787487,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
12878,jnz/electra-ka,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0999777466058731,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
12879,negfir/distilbert-base-uncased-finetuned-cola,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0768103003501892,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
12880,google/t5-efficient-base-kv128,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0237764939665794,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
12881,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0021925568580627,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
12882,google/t5-efficient-base-nl48,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0682774856686592,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
12883,mwesner/bart-mlm,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
12884,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.050397153943777,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
12885,google/t5-efficient-large-kv16,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-kv16,1.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
12886,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
12887,groar/gpt-neo-1.3B-finetuned-escape2,"We modify the original position code of longformer to rotational position coding, and use 180G of data to pretraining",IDEA-CCNL/Erlangshen-Longformer-330M,0.0853161215782165,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
12888,Iskaj/hf-challenge-test,It was introduced in this paper,readerbench/RoBERT-small,0.0803001672029495,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
12889,google/t5-efficient-base-nh8,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0780798513442277,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
12890,google/t5-efficient-base-kv32,This does not make a very readable text,pere/DeUnCaser,0.0434704837389289,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
12891,google/t5-efficient-small-nl48,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0483574755489825,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
12892,beston91/gpt2-xl_ft_logits_10k,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0499671325087547,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
12893,everdoubling/byt5-Korean-small,Model is warm started from distilbert-fa’s checkpoint,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0348998906090855,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
12894,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0469304698829849,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
12895,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh24,0.081119991838932,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
12896,mimicheng/codeparrot-ds,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0505337305366992,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
12897,FremyCompany/xls-r-nl-v1-cv8-lm,RobBERTje is a collection of distilled models based on RobBERT,DTAI-KULeuven/robbertje-1-gb-bort,0.0161929079331457,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
12898,google/t5-efficient-small-ff1000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0554060097783803,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
12899,google/t5-efficient-small-ff3000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
12900,amazon/bort,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0161320529878139,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12901,lassl/roberta-ko-small,"6,860,062 examples are extracted from below corpora",lassl/roberta-ko-small,1.0,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
12902,kssteven/ibert-roberta-base,This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU,kssteven/ibert-roberta-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
12903,google/t5-efficient-xxl-nl4,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0666642412543296,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
12904,mmaguero/gn-bert-tiny-cased,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,0.0761112421751021,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
12905,groar/gpt-neo-1.3B-finetuned-escape2,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0184819921851158,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
12906,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0024869702756404,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
12907,google/t5-efficient-large-nl12,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.072905946522951,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
12908,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
12909,negfir/distilbert-base-uncased-finetuned-cola,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0904745757579803,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
12910,mimicheng/codeparrot-ds,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
12911,lgrobol/electra-minuscule-generator,"With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in WMT 2020",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0932776331901549,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
12912,google/t5-efficient-base-kv256,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0060623195022344,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
12913,amazon/bort,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0192238753661513,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12914,stas/mt5-tiny-random,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
12915,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0115225873887538,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
12916,tartuNLP/gpt-4-est-large,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0809973776340484,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
12917,ptaszynski/yacis-electra-small-japanese,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,6.250292062760007e-05,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
12918,khizon/distilbert-unreliable-news-eng-4L,It has less than 1% difference in performance compared to the BERT based model while having 2.0x the speed,khizon/distilbert-unreliable-news-eng-4L,1.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
12919,mimicheng/codeparrot-ds,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
12920,google/t5-efficient-small-el64,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0431278059259057,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
12921,google/t5-efficient-tiny-nh16,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
12922,tau/splinter-base-qass,Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering,tau/splinter-base-qass,1.0,-1.0,NLP,f68d3e26-2d96-4b99-a309-34b532e9740f___splinter-base-qass,HF
12923,microsoft/layoutlm-large-uncased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0426016375422477,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
12924,rdenadai/BR_BERTo,T5-Efficient-TINY-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh16,0.0320569400986035,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
12925,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0333785898983478,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
12926,google/t5-large-ssm-nq,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
12927,patrickvonplaten/unispeech-sat-large-timit-ft,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0456497371196746,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
12928,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.056123711168766,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
12929,google/t5-efficient-small-nl36,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.055246471427381,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
12930,google/t5-efficient-tiny-ff9000,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0404241336509585,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
12931,stas/pegasus-cnn_dailymail-tiny-random,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.033088152296841,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
12932,IDEA-CCNL/Erlangshen-Longformer-110M,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
12933,fnlp/bart-base-chinese,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0339753376320004,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
12934,google/t5-efficient-tiny-nl12,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0665277782827615,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
12935,google/t5-efficient-large-dm256,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
12936,google/t5-efficient-small-dm128,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0148755926638841,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
12937,SupriyaArun/squeezebert-uncased-finetuned-squad,You can find more information in our paper AraGPT2The code in this repository was used to train all GPT2 variants,aubmindlab/aragpt2-mega,0.0832961946725845,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
12938,j-hartmann/purchase-intention-english-roberta-large,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.000381459792455,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
12939,IDEA-CCNL/Erlangshen-Longformer-330M,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0467216707766055,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
12940,google/t5-efficient-base-nl36,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
12941,google/t5-efficient-large-kv256,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
12942,google/t5-efficient-base-ff9000,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0824416503310203,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
12943,google/t5-efficient-tiny-ff12000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
12944,mrm8488/mobilebert-uncased-finetuned-squadv2,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0059077789386113,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
12945,lgrobol/electra-minuscule-generator,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0109449811279773,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
12946,google/t5-efficient-tiny-ff6000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0840967893600463,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
12947,google/t5-efficient-base-ff9000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
12948,allenai/macaw-answer-11b,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0328255891799926,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
12949,diper1998/distilgpt2-finetuned-AT,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0684548467397689,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
12950,pszemraj/led-large-book-summary,I have made no attempts to censor,bigjoedata/rockbot,0.0163911820854991,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
12951,google/rembert,Pretrained RemBERT model on 110 languages using a masked language modeling (MLM) objective,google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
12952,brad1141/Longformer-finetuned-norm,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0456638000905513,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
12953,google/t5-efficient-large-kv128,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
12954,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
12955,google/t5-efficient-base-nl24,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0689947418868541,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
12956,allenai/wmt16-en-de-dist-6-1,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
12957,google/t5-efficient-base-ff1000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0402918346226214,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
12958,khizon/distilbert-unreliable-news-eng-4L,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0418044105172157,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
12959,nghuyong/ernie-1.0,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0274587864987552,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
12960,google/t5-efficient-mini-nl8,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
12961,google/t5-efficient-large-nl32,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0763956680893897,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
12962,IDEA-CCNL/Erlangshen-Longformer-330M,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.085446547716856,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
12963,mikaelsouza/msft-regular-model,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
12964,sberbank-ai/rugpt3large_based_on_gpt2,Final perplexity on test set is 13.6,sberbank-ai/rugpt3large_based_on_gpt2,1.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
12965,google/t5-efficient-large-nh12,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0018694661557674,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
12966,aditeyabaral/sentencetransformer-distilbert-hinglish-big,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0466559641063213,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12967,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0617254115641117,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
12968,google/t5-efficient-small-el32,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0960675068199634,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
12969,TJMUCH/transcriptome-iseeek,This model will be used in an upcoming submission for  IMLH at ICML 2021,mnaylor/bigbird-base-mimic-mortality,0.0926507785916328,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12970,minimaxir/magic-the-gathering,It allows the model to learn a bidirectional representation of the sentence,macedonizer/al-roberta-base,0.0237739705480635,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
12971,google/t5-efficient-base-nl24,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0583577398210763,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
12972,groar/gpt-neo-1.3B-finetuned-escape2,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0977388620376586,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
12973,google/t5-efficient-tiny-ff3000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0344312549568712,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
12974,google/t5-efficient-tiny-ff9000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0397972464561462,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
12975,google/t5-efficient-large-nh8-nl32,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0537953954190014,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
12976,google/t5-efficient-xl-nl28,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0078956801444292,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
12977,lgrobol/electra-minuscule-generator,The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1,nyu-mll/roberta-med-small-1M-3,0.0604405757039785,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
12978,google/t5-efficient-large-el6,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0206037443131208,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
12979,1one5ome/gpt2-chinese-gulong,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
12980,piotr-rybak/poleval2021-task4-herbert-large-encoder,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
12981,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.052808754146099,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
12982,beston91/gpt2-xl_ft_logits_10k,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
12983,minimaxir/magic-the-gathering,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
12984,sberbank-ai/rugpt3medium_based_on_gpt2,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
12985,Parsa/BBB_prediction_classification_SMILES,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0511480886489152,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
12986,TJMUCH/transcriptome-iseeek,It was introduced in this paper,readerbench/RoBERT-small,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
12987,fnlp/bart-base-chinese,I have made no attempts to censor,bigjoedata/rockbot,0.0750438384711742,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
12988,tobiaslee/bert-2l-768h-uncased,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0854931510984897,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
12989,google/t5-efficient-mini-nl12,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0386199411004781,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
12990,IlyaGusev/rugpt3medium_sum_gazeta,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0219142697751522,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
12991,sentence-transformers/paraphrase-MiniLM-L3-v2,Both pytorch(pytorch_model bin) and Rust(rust_modelot) models are providedNOTE: Use T5Tokenizer to initiate the tokenizer,jweb/japanese-soseki-gpt2-1b,0.0769272446632385,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
12992,amazon/bort,"MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks",mrm8488/mobilebert-uncased-finetuned-squadv2,0.0917802937328814,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
12993,tartuNLP/gpt-4-est-large,The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0304565019905567,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
12994,Palak/microsoft_deberta-base_squad,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.093124896287918,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
12995,google/bert_uncased_L-10_H-128_A-2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0245217963432272,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
12996,google/t5-efficient-small-kv16,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0014220434240997,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
12997,rahulacj/bertweet-base-finetuned-sentiment-analysis,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
12998,google/t5-efficient-tiny-nh1,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0831454582512378,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
12999,suzuki/distilbert-base-uncased-finetuned-squad,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0778704434633255,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
13000,google/t5-efficient-base-nh32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
13001,bookbot/distil-wav2vec2-adult-child-cls-52m,The model achieves the following results on evaluation:,bookbot/distil-wav2vec2-adult-child-cls-52m,1.0,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
13002,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.051676645874977,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
13003,flboehm/reddit-bert-text_5,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
13004,facebook/m2m100-12B-avg-10-ckpt,It was introduced in this paper and first released in this repository,facebook/m2m100-12B-avg-10-ckpt,1.0,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
13005,google/t5-efficient-base-nl48,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0864841863512992,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
13006,superspray/electra_large_discriminator_squad2_custom_dataset,"Electra_Large Discriminator fine-tuned on SQuAD2.0 and custom QA datasetThis model is ahotrod/electra_large_discriminator_squad2_512  trained on additional custom dataset as:We used Google Colab for training the model,",superspray/electra_large_discriminator_squad2_custom_dataset,1.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
13007,google/t5-efficient-base-ff9000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0074398694559932,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
13008,stas/t5-very-small-random,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0576966069638729,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
13009,google/t5-efficient-mini,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0774385891854762,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
13010,amazon/bort,The model was created for selective question answering in Polish,enelpol/czywiesz-context,0.0242734905332326,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13011,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0677051693201065,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
13012,google/t5-efficient-base-nh32,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0054545262828469,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
13013,ToToKr/kobigbird-bert-base-finetuned-klue,It was introduced in this paper,readerbench/RoBERT-small,0.0441783294081687,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
13014,google/t5-efficient-base-ff6000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
13015,Vilnius-Lithuania-iGEM/Albumin,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0041154846549034,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
13016,google/t5-efficient-tiny-nl12,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0202506361529231,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
13017,google/t5-efficient-large-nh4,This does not make a very readable text,pere/DeUnCaser,0.0321193803101778,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
13018,google/t5-efficient-small-el8,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0661586001515388,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
13019,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
13020,google/t5-efficient-base-kv32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
13021,stas/t5-very-small-random,"It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed",phueb/BabyBERTa-2,0.0817686766386032,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
13022,kyryl0s/gpt2-uk-xxs,"We also train a byte-level Byte-pair encoding (BPE) tokenizer that includes 56,000 tokens, which is truncated LOC with the length of 50 to save computation resources",Fujitsu/pytorrent,0.0677392873913049,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
13023,google/t5-efficient-small-ff1000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0597054846584796,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
13024,rdenadai/BR_BERTo,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0186468710501988,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
13025,Milos/slovak-gpt-j-1.4B,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
13026,google/t5-efficient-small-dm128,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.042211977764964,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
13027,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0141224103669326,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
13028,IDEA-CCNL/Erlangshen-Longformer-330M,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0567880533635615,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
13029,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0179196521639823,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
13030,NovelAI/genji-python-6B,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
13031,google/t5-efficient-base-nl4,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0516686886548995,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
13032,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0357483178377151,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
13033,Palak/microsoft_deberta-base_squad,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.0945335254073143,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
13034,lannelin/bert-imdb-1hidden,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
13035,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,0.0846743062138557,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13036,google/t5-efficient-large-nl32,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0586908496916293,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
13037,microsoft/unispeech-sat-base,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0099854245781898,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
13038,stas/t5-very-small-random,"Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa)",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0538256540894508,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
13039,amazon/bort,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.0230878833681345,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13040,asi/gpt-fr-cased-base,We do hope our model might be used for both academic and industrial applications,asi/gpt-fr-cased-base,1.0,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
13041,DemangeJeremy/4-sentiments-with-flaubert,This model was finetuned for 3 epochs with a max_len (input) of 768 tokens and target_max_len of 192 tokens,ml6team/mt5-small-german-finetune-mlsum,0.0433929217979311,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13042,ctoraman/RoBERTa-TR-medium-char,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0170284669846296,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
13043,google/t5-efficient-xl-nl16,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0753537267446518,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
13044,google/t5-efficient-base-kv32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0406560525298118,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
13045,uer/t5-v1_1-small-chinese-cluecorpussmall,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
13046,minimaxir/magic-the-gathering,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
13047,google/t5-efficient-small-kv256,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0314099099487066,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
13048,google/t5-efficient-base-nl2,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
13049,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0886428654193878,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
13050,Milos/slovak-gpt-j-405M,"Pre-Training with Whole Word Masking for Chinese BERTYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping HuThis repository is developed based on：https://github",hfl/rbtl3,0.0203501942257086,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
13051,hfl/chinese-electra-180g-small-ex-generator,"For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA",hfl/chinese-electra-180g-small-ex-generator,1.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
13052,microsoft/layoutlm-base-uncased,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0487595684826374,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
13053,Iskaj/hf-challenge-test,"Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan BaskaranWork done via MLDMM Lab",mldmm/GlassBERTa,0.0800391212105751,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
13054,ToToKr/kobigbird-bert-base-finetuned-klue,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0799838379025459,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
13055,piotr-rybak/poleval2021-task4-herbert-large-encoder,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0934343412518501,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
13056,SupriyaArun/squeezebert-uncased-finetuned-squad,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0360075309872627,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
13057,google/t5-efficient-small-ff9000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0435605086386203,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
13058,celine98/canine-s-finetuned-sst2,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.056545190513134,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
13059,google/t5-efficient-small-nl2,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0473555549979209,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
13060,minimaxir/magic-the-gathering,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.064231714233756,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
13061,uer/t5-v1_1-small-chinese-cluecorpussmall,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0363390920683741,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
13062,google/t5-efficient-large-kv32,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0214918050915002,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
13063,google/t5-efficient-xxl-nl4,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.021070560440421,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
13064,google/t5-efficient-large-kv32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0521246627904474,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
13065,mrm8488/bert-small-finetuned-squadv2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0220425818115472,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
13066,gayanin/bart-mlm-pubmed-medterm,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0996611043810844,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
13067,google/t5-efficient-base-el16,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0441707167774438,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
13068,rinna/japanese-gpt2-xsmall,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.000490794579188,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
13069,mmaguero/gn-bert-tiny-cased,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0592941287904977,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
13070,google/t5-efficient-mini-nl8,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
13071,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0430097496137022,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
13072,google/t5-efficient-tiny-ff9000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0372941941022872,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
13073,SetFit/deberta-v3-large__sst2__train-8-8,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0118335727602243,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
13074,SetFit/deberta-v3-large__sst2__train-8-8,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0954251363873481,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
13075,google/t5-efficient-small-el12,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
13076,google/t5-efficient-mini-nl8,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0716534666717051,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
13077,google/t5-efficient-large-nh24,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0324306208640337,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
13078,GKLMIP/electra-myanmar-small-uncased,GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX,gilparmentier/pokemon_gptj_model,0.0853801518678665,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
13079,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0792614519596099,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
13080,google/t5-efficient-tiny-ff3000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0267210043966769,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
13081,VietAI/gpt-neo-1.3B-vietnamese-news,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0888888388872146,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
13082,microsoft/layoutlm-base-uncased,"How do I pronounce the name of the model? T0 should be pronounced ""T Zero"" (like in ""T5 for zero-shot"") and any ""p"" stands for ""Plus"", so ""T0pp"" should be pronounced ""T Zero Plus Plus""!T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller",saurkulsh/T0pp,0.0302004627883434,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
13083,google/t5-efficient-small-kv128,This does not make a very readable text,pere/DeUnCaser,0.0493811452761292,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
13084,google/t5-efficient-tiny-ff3000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0143705317750572,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
13085,uer/t5-v1_1-base-chinese-cluecorpussmall,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.041525937616825,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
13086,clue/roberta_chinese_3L312_clue_tiny,"We also train a byte-level Byte-pair encoding (BPE) tokenizer that includes 56,000 tokens, which is truncated LOC with the length of 50 to save computation resources",Fujitsu/pytorrent,0.0584081746637821,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
13087,junnyu/roformer_base_wwm_cluecorpussmall,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
13088,google/t5-efficient-small-ff12000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0096251126378774,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
13089,gayanin/bart-mlm-pubmed-medterm,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0879756808280944,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
13090,google/t5-efficient-base-kv128,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0502138528972863,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
13091,abdusahmbzuai/ft-tatoeba-ar-en,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0537716448307037,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
13092,google/t5-efficient-xl-nl6,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0940558463335037,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
13093,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0717023462057113,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13094,microsoft/layoutlm-base-uncased,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.060350090265274,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
13095,uer/chinese_roberta_L-6_H-128,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
13096,mwesner/bart-mlm,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0374209955334663,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
13097,mrm8488/xlm-multi-finetuned-xquadv1,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
13098,malay-huggingface/t5-super-super-tiny-bahasa-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0110500815014044,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
13099,anirudh21/xlnet-base-cased-finetuned-rte,For best results use python as below,pszemraj/led-large-book-summary,0.0803967639803886,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
13100,google/t5-efficient-tiny-ff3000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.052701100707054,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
13101,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text)",pszemraj/led-large-book-summary,0.0986726135015487,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
13102,sentence-transformers/paraphrase-MiniLM-L3-v2,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
13103,Rostlab/prot_bert,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
13104,danyaljj/opengpt2_pytorch_forward,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,1.0,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
13105,google/t5-efficient-large-dm768,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0510549377650022,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
13106,google/t5-efficient-small-ff9000,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0859455913305282,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
13107,mwesner/bart-mlm,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0732453241944313,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
13108,ToToKr/kobigbird-bert-base-finetuned-klue,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
13109,google/t5-efficient-large-kv16,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
13110,google/t5-efficient-base-nh8,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0257191043347119,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
13111,aditeyabaral/sentencetransformer-bert-hinglish-small,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0314215123653411,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
13112,thu-coai/LongLM-large,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,1.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
13113,google/t5-efficient-small-dm128,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
13114,google/t5-efficient-base-nl32,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
13115,VietAI/gpt-neo-1.3B-vietnamese-news,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0882490426301955,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
13116,cammy/led-base-16384-100-MDS,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0826272964477539,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
13117,thu-coai/LongLM-large,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0250888752440611,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
13118,google/t5-efficient-xl-nl6,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0586360096931457,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
13119,google/t5-efficient-small-el12,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0181607026606798,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
13120,negfir/distilbert-base-uncased-finetuned-squad,Source language: English,NYTK/translation-marianmt-en-hu,0.0146705769002437,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
13121,uer/chinese_roberta_L-2_H-256,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.042084196023643,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
13122,google/t5-efficient-base-kv32,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0188063532114029,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
13123,IDEA-CCNL/Erlangshen-Longformer-330M,"Important: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch)",pszemraj/led-large-book-summary,0.0490165464580059,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
13124,google/bert_uncased_L-10_H-256_A-4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0245217963432272,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
13125,google/t5-efficient-small-nl22,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.03708235360682,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
13126,google/t5-efficient-base-kv256,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0637020282447337,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
13127,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl48,0.0976415872573852,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
13128,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
13129,superspray/electra_large_discriminator_squad2_custom_dataset,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,0.0619018785655498,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
13130,google/t5-efficient-base-dm2000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0180848482996224,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
13131,kyryl0s/gpt2-uk-xxs,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
13132,everdoubling/byt5-Korean-small,"This is part of a project from which resulted the following models:For more information, please see the accompanying article (See BibTeX entry and citation info below) and the project's github",liaad/srl-pt_xlmr-base,0.0338606499135493,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
13133,DeepPavlov/distilrubert-tiny-cased-conversational,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
13134,google/t5-efficient-tiny-ff6000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0050881188362837,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
13135,google/t5-efficient-large-nh32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0505100339651107,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
13136,saibo/random-roberta-tiny,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0726149640977382,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
13137,celine98/canine-s-finetuned-sst2,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0099975448101758,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
13138,fnlp/cpt-base,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0324498806148767,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
13139,hfl/chinese-electra-180g-small-generator,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
13140,google/t5-efficient-small-kv16,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.054571446031332,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
13141,TJMUCH/transcriptome-iseeek,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0561996027827262,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13142,sentence-transformers/paraphrase-MiniLM-L3-v2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
13143,google/t5-efficient-mini-nl24,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0324388016015291,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
13144,TJMUCH/transcriptome-iseeek,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0323286578059196,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13145,jonatasgrosman/wav2vec2-xls-r-1b-english,"When using this model, make sure that your speech input is sampled at 16kHz",jonatasgrosman/wav2vec2-xls-r-1b-english,1.0,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
13146,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0068693347275256,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13147,google/t5-efficient-small-el32,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0650832634419202,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
13148,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0025935570398966,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
13149,suzuki/distilbert-base-uncased-finetuned-squad,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0568401217460632,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
13150,google/t5-efficient-base-dm512,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0760185457766055,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
13151,google/t5-efficient-base-kv128,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
13152,google/t5-efficient-small-nl22,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0662432443350553,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
13153,stas/pegasus-cnn_dailymail-tiny-random,Colab: linkEvaluation script: evaluate,IlyaGusev/rugpt3medium_sum_gazeta,0.0407591741532087,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
13154,google/t5-efficient-large-nl32,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0548801682889461,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
13155,stas/t5-very-small-random,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0935220047831535,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
13156,junnyu/roformer_base_wwm_cluecorpussmall,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0025927498936652,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
13157,google/t5-efficient-large-kv16,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0586026832461356,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
13158,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,Its original repository can be found here,tau/splinter-large-qass,0.0308863464742898,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
13159,google/t5-efficient-base-dm256,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
13160,superspray/electra_large_discriminator_squad2_custom_dataset,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0868041962385177,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
13161,rahulacj/bertweet-base-finetuned-sentiment-analysis,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0568183660507202,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
13162,google/t5-efficient-small-el32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0459002694115042,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
13163,google/t5-efficient-large-kv32,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0908901318907737,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
13164,google/t5-efficient-large-el6,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0411334820091724,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
13165,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0962439030408859,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
13166,mwesner/reformer-clm,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0826462656259536,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
13167,amazon/bort,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0489730052649974,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13168,IDEA-CCNL/Erlangshen-Longformer-110M,Its original repository can be found here,tau/splinter-large-qass,0.0289693912491202,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
13169,GKLMIP/electra-myanmar-small-uncased,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0351615725085138,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
13170,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0084773898124694,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13171,google/t5-efficient-small-el16-dl4,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0316157191991806,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
13172,nghuyong/ernie-tiny,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.052574132879575,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
13173,VietAI/gpt-neo-1.3B-vietnamese-news,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0471558701246976,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
13174,Milos/slovak-gpt-j-405M,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0143307919303575,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
13175,facebook/blenderbot-3B,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
13176,IDEA-CCNL/Erlangshen-Longformer-110M,You can use the raw model for text generation to generate reasons why natural language statements are against commonsense,aliosm/ComVE-gpt2-large,0.0918222814798354,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
13177,google/t5-efficient-large-nh4,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0793841835111379,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
13178,mwesner/reformer-clm,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0950361713767051,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
13179,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0629926174879074,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
13180,google/t5-efficient-tiny-ff9000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0602882001549005,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
13181,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0473273172974586,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
13182,1one5ome/gpt2-chinese-gulong,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0515546612441539,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
13183,google/t5-efficient-base-kv256,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0468411464244127,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
13184,google/t5-efficient-small-el12,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0358308542054146,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
13185,google/t5-efficient-tiny-ff3000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0375900408253073,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
13186,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0264964364469051,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
13187,google/t5-efficient-base-nl24,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0278996247798204,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
13188,google/t5-efficient-small-nl2,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0026167165488004,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
13189,uer/t5-v1_1-base-chinese-cluecorpussmall,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0438497066497802,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
13190,google/t5-efficient-tiny-nl24,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0509480712935328,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
13191,1one5ome/gpt2-chinese-gulong,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0301648825407028,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
13192,tuner007/pegasus_qa,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0949127450585365,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
13193,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0972016155719757,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
13194,akdeniz27/deberta-v2-xlarge-cuad,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0637459978461265,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
13195,google/t5-efficient-xl-nl6,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0934730395674705,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
13196,facebook/wmt21-dense-24-wide-en-x,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
13197,fnlp/bart-base-chinese,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0615714620798825,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
13198,google/t5-efficient-base-dm1000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
13199,cosmoquester/bart-ko-base,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0487701011200745,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
13200,google/bigbird-roberta-large,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0125666957348585,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
13201,beston91/gpt2-xl_ft_logits_10k,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
13202,stas/t5-very-small-random,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0569497123360633,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
13203,google/fnet-large,Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective,google/fnet-large,1.0,-1.0,NLP,be8c3b54-acb9-41d3-9190-d3a35ba6f7a6___fnet-large,HF
13204,uer/t5-v1_1-small-chinese-cluecorpussmall,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
13205,khizon/distilbert-unreliable-news-eng-4L,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0225732428953051,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
13206,microsoft/layoutlm-large-uncased,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
13207,oskrmiguel/mt5-simplification-spanish,Its original repository can be found here,tau/splinter-large-qass,0.0513586262241005,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
13208,google/t5-efficient-small-ff6000,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0380942625924945,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
13209,microsoft/layoutlm-large-uncased,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
13210,stas/mt5-tiny-random,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0104464339092373,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
13211,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-SMALL-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm128,0.0675407474239666,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
13212,google/t5-efficient-small-dm2000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0831524468958377,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
13213,microsoft/layoutlm-large-uncased,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0598843581974506,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
13214,google/t5-efficient-base-ff12000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0553989689797162,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
13215,amazon/bort,They used a generative recurrent neural network (RNN) containing long short‐term memory (LSTM) cell to capture the syntax of molecular representations in terms of SMILES strings,mrm8488/chEMBL_smiles_v1,0.0212891846895218,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13216,cross-encoder/ms-marco-MiniLM-L-4-v2,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,1.0,-1.0,NLP,b57a156c-f487-4efc-a83f-37bedaa63a0a___ms-marco-MiniLM-L-4-v2,HF
13217,google/t5-efficient-small-nl22,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0012910738587379,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
13218,google/t5-efficient-small-el64,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0577165018767118,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
13219,google/t5-efficient-tiny-nh32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
13220,piotr-rybak/poleval2021-task4-herbert-large-encoder,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
13221,aubmindlab/aragpt2-large,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0368828719947486,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
13222,anas-awadalla/bert-tiny-finetuned-squad,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0737429112195968,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
13223,wukevin/tcr-bert,I have made no attempts to censor,bigjoedata/rockbot,0.0341753512620926,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
13224,SetFit/deberta-v3-large__sst2__train-8-8,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0926244258880615,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
13225,allenai/wmt16-en-de-dist-6-1,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0315275499597191,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
13226,google/t5-efficient-small-el32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0123443622142076,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
13227,brad1141/Longformer-finetuned-norm,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
13228,nghuyong/ernie-tiny,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0419738553464412,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
13229,google/t5-efficient-xl-nl12,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.086178284138441,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
13230,google/t5-efficient-large-nl36,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0839825049042701,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
13231,cammy/led-large-16384-arxiv-100-MDS,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0459008552134037,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
13232,google/t5-efficient-large-nl10,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
13233,ai4bharat/MultiIndicHeadlineGeneration,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0394619616369405,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
13234,google/t5-efficient-large-nl10,T5-Efficient-LARGE-NL10 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl10,1.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
13235,google/t5-efficient-small-el64,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0479673240333795,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
13236,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el4,0.0728679448366165,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
13237,google/reformer-enwik8,reformer-enwik8 was pretrained on the first 90M chars of enwik8 whereas the text was chunked into batches of size 65536 chars (=2^16),google/reformer-enwik8,1.0,-1.0,NLP,6aa23d61-51e8-44d6-b6b7-b68cce7cd0c1___reformer-enwik8,HF
13238,superspray/electra_large_discriminator_squad2_custom_dataset,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
13239,ptaszynski/yacis-electra-small-japanese,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.053760137874633,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
13240,IDEA-CCNL/Zhouwenwang-Unified-1.3B,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
13241,google/t5-efficient-small-el64,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0737076140940189,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
13242,RabotaRu/HRBert-mini,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0275109028443694,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
13243,cammy/led-large-16384-arxiv-100-MDS,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0710719674825668,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
13244,google/t5-efficient-base-dm256,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0094151496887207,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
13245,PlanTL-GOB-ES/gpt2-large-bne,"The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019",PlanTL-GOB-ES/gpt2-large-bne,1.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
13246,akdeniz27/deberta-v2-xlarge-cuad,The hyperparameters for training followed the setting for RoBERTa,KBLab/megatron-bert-large-swedish-cased-165k,0.0445348247885704,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
13247,krevas/finance-koelectra-base-discriminator,Pretrained ELECTRA Language Model for Korean (finance-koelectra-base-discriminator)ELECTRA is a new method for self-supervised language representation learning,krevas/finance-koelectra-base-discriminator,1.0,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
13248,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.088580809533596,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13249,NeuML/bert-small-cord19,Its original repository can be found here,tau/splinter-large-qass,0.0491454675793647,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
13250,google/t5-efficient-small-kv32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0499569294042884,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
13251,abdusahmbzuai/ft-tatoeba-ar-en,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0575931966304779,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
13252,allenai/wmt16-en-de-dist-6-1,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0138604207895695,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
13253,google/t5-efficient-large-nl16,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.061289420351386,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
13254,mwesner/reformer-clm,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0882182717323303,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
13255,google/t5-efficient-large-nl36,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.015940211713314,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
13256,IDEA-CCNL/Erlangshen-Longformer-330M,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0790547206997871,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
13257,uer/bart-chinese-4-768-cluecorpussmall,I have made no attempts to censor,bigjoedata/rockbot,0.0322563853114842,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
13258,google/t5-efficient-small-nl40,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0602547787129878,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
13259,google/t5-efficient-large-dm2000,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0955049134790897,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
13260,Milos/slovak-gpt-j-162M,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
13261,google/t5-efficient-large-nh8-nl32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.036532225087285,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
13262,stas/pegasus-cnn_dailymail-tiny-random,"This model is the scorer trained for summarization of BillSum (paper, datasets)",andrejmiscic/simcls-scorer-billsum,0.0523949591442942,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
13263,google/t5-efficient-small-nl24,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0818134434521198,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
13264,uer/t5-v1_1-small-chinese-cluecorpussmall,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
13265,microsoft/layoutlm-base-cased,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
13266,mimi/Waynehills-NLP-doogie,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0607834570109844,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
13267,enelpol/czywiesz-context,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
13268,jkeruotis/LitBERTa-uncased,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
13269,pere/DeUnCaser,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,1.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
13270,saibo/random-roberta-mini,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0140516606625169,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
13271,lgrobol/electra-minuscule-generator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
13272,rinna/japanese-gpt2-xsmall,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0264813744773467,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
13273,1one5ome/gpt2-chinese-gulong,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0456434153020381,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
13274,google/t5-efficient-large-kv256,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0466571310535073,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
13275,google/t5-efficient-base-kv128,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0786871686577796,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
13276,microsoft/deberta-v2-xxlarge-mnli,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
13277,google/t5-efficient-large-dm768,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0562609182670712,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
13278,bolbolzaban/gpt2-persian,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0667796973139047,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
13279,google/t5-efficient-base-kv128,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0267876684665679,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
13280,amazon/bort,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0515927709639072,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13281,google/t5-efficient-large-dm512,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0652988106012343,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
13282,google/t5-efficient-base-nl4,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.033830433152616,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
13283,groar/gpt-neo-1.3B-finetuned-escape2,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0328108631074428,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
13284,KoichiYasuoka/roberta-base-ukrainian,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0184165481477975,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
13285,google/t5-efficient-small-dm1000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
13286,google/t5-efficient-large-dm256,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0142023228108882,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
13287,tartuNLP/gpt-4-est-large,This model was made following the research done by Iz Beltagy and Matthew E,mrm8488/longformer-base-4096-spanish,0.077663630247116,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
13288,gayanin/bart-mlm-pubmed-medterm,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0995285734534263,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
13289,google/t5-efficient-base-kv256,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0234886705875396,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
13290,fnlp/bart-large-chinese,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0064053488895297,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
13291,crabz/distil-slovakbert-ner,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0375983379781246,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
13292,microsoft/layoutlm-base-uncased,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0451700277626514,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
13293,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0518283545970916,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
13294,google/t5-efficient-base-ff1000,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0457088630646467,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
13295,lgrobol/electra-minuscule-generator,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0194624965079127,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
13296,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0074106100946664,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
13297,google/t5-efficient-small-el32,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
13298,gilparmentier/pokemon_gptj_model,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0154201420955359,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
13299,Vilnius-Lithuania-iGEM/Albumin,"The Albert model was pretrained in the English language, it has many similarities with language or proteins and aptamers which is why we had to fine-tune it to help the model learn embedded positioning for aptamers to be able to distinguish better sequences",Vilnius-Lithuania-iGEM/Albumin,1.0,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
13300,google/t5-efficient-small-nl20,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
13301,lgrobol/electra-minuscule-generator,A ridiculously small ELECTRA generator model for testing purposes,lgrobol/electra-minuscule-generator,1.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
13302,google/t5-efficient-base-nh32,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
13303,inokufu/flaubert-base-uncased-xnli-sts,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0155914518982171,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
13304,google/t5-efficient-large-dm256,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0787146501243113,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
13305,nghuyong/ernie-tiny,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
13306,google/t5-efficient-base-kv128,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0600708611309527,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
13307,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
13308,piotr-rybak/poleval2021-task4-herbert-large-encoder,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.043824803084135,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
13309,aditeyabaral/sentencetransformer-bert-hinglish-small,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.039396695792675,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
13310,google/t5-efficient-large-dm256,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0367259215563535,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
13311,google/t5-efficient-small-nl22,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0491351261734962,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
13312,kssteven/ibert-roberta-base,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0069391388784755,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
13313,allenai/wmt16-en-de-dist-6-1,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0197854675352573,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
13314,amazon/bort,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0722144749015569,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13315,google/t5-efficient-base-nh24,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0657076239585875,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
13316,google/t5-efficient-small-ff1000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0073971622623503,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
13317,mrm8488/bert-small-finetuned-squadv2,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0548825114965438,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
13318,gayanin/bart-mlm-pubmed-medterm,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0770735442638397,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
13319,TJMUCH/transcriptome-iseeek,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0791354328393936,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13320,google/t5-efficient-large-nh32,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
13321,Vilnius-Lithuania-iGEM/Albumin,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0516769643872975,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
13322,google/t5-efficient-large-nl36,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
13323,stas/mt5-tiny-random,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0398876667022705,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
13324,cammy/led-large-16384-arxiv-100-MDS,This model is a fine-tuned version of allenai/led-large-16384-arxiv on an unknown dataset,cammy/led-large-16384-arxiv-100-MDS,1.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
13325,suzuki/distilbert-base-uncased-finetuned-squad,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0839402750134468,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
13326,aubmindlab/aragpt2-mega,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0125491651706397,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
13327,pszemraj/led-large-book-summary,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
13328,thu-coai/LongLM-large,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
13329,google/t5-efficient-tiny-ff6000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0230518747121095,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
13330,google/t5-efficient-base-kv128,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0361776072531938,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
13331,akdeniz27/deberta-v2-xlarge-cuad,"We used  Oscar dataset, which is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus",flax-community/gpt2-medium-persian,0.0949756205081939,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
13332,monsoon-nlp/dv-muril,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
13333,google/t5-efficient-base-ff12000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0269315370824187,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
13334,allegro/plt5-large,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.063113760203123,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
13335,google/t5-efficient-large-nl32,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
13336,google/t5-efficient-large-el6,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
13337,mimi/Waynehills-NLP-doogie,"In order to facilitate users to reproduce the results, we used the publicly available corpus and provided all training details",uer/chinese_roberta_L-6_H-128,0.0955263003706932,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
13338,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.095414787530899,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13339,google/t5-efficient-small-el8,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0383244846016168,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
13340,IDEA-CCNL/Erlangshen-Longformer-110M,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0561025450006127,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
13341,google/t5-efficient-small-nl40,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0572375748306512,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
13342,google/t5-efficient-base-nl24,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-nl24,1.0,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
13343,google/byt5-xl,"ByT5 works especially well on noisy text data,e",google/byt5-xl,1.0,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
13344,google/t5-efficient-large-nl32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0499999858438967,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
13345,malay-huggingface/bert-tiny-bahasa-cased,Its original repository can be found here,tau/splinter-large-qass,0.028763245170315,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
13346,flboehm/reddit-bert-text_5,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0834743380546569,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
13347,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0708860754966735,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
13348,beston91/gpt2-xl_ft_logits_10k,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0422518402338027,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
13349,piotr-rybak/poleval2021-task4-herbert-large-encoder,This model is a rather small instance of GPT-2 trained on TristanBehrens/js-fakes-4bars,TristanBehrens/js-fakes-4bars,0.0640219077467918,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
13350,superspray/electra_large_discriminator_squad2_custom_dataset,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.02468978241086,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
13351,Palak/microsoft_deberta-base_squad,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
13352,skt/ko-gpt-trinity-1.2B-v0.5,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0458628991618752,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
13353,google/t5-efficient-mini-nl8,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
13354,piotr-rybak/poleval2021-task4-herbert-large-encoder,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0814427211880683,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
13355,hfl/chinese-electra-180g-small-ex-generator,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
13356,SajjadAyoubi/distil-bigbird-fa-zwnj,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,1.0,-1.0,NLP,93bf4f14-6c7a-41bc-854c-061751656605___distil-bigbird-fa-zwnj,HF
13357,1one5ome/gpt2-chinese-gulong,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0691722631454467,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
13358,sonoisa/byt5-small-japanese,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0315723866224288,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
13359,gilparmentier/pokemon_gptj_model,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
13360,google/t5-efficient-small-nl20,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0783042479306459,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
13361,mrm8488/deberta-v3-small-finetuned-mrpc,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0593009889125824,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
13362,mwesner/bart-mlm,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0221049562096595,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
13363,suzuki/distilbert-base-uncased-finetuned-squad,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0899606794118881,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
13364,stas/mt5-tiny-random,For best results use python as below,pszemraj/led-large-book-summary,0.0461958013474941,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
13365,google/t5-efficient-xl-nl16,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.078305572271347,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
13366,aditeyabaral/sentencetransformer-bert-hinglish-small,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.081512913107872,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
13367,google/t5-efficient-small-kv16,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0603314973413944,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
13368,google/t5-efficient-base-nl24,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0460129426792263,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
13369,google/t5-efficient-tiny-nl16,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0761367976665496,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
13370,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0848103538155555,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
13371,akdeniz27/deberta-v2-xlarge-cuad,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0964247733354568,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
13372,Palak/microsoft_deberta-base_squad,It was introduced in this paper,readerbench/RoBERT-small,0.0127422735095024,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
13373,Recognai/selectra_medium,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0249017705209553,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
13374,microsoft/layoutlm-large-uncased,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0512148439884185,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
13375,IDEA-CCNL/Zhouwenwang-Unified-110M,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
13376,j-hartmann/purchase-intention-english-roberta-large,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
13377,Norod78/hebrew_poetry-gpt_neo-tiny,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0519016161561012,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
13378,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NH8-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8-nl32,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13379,google/t5-efficient-small-dm768,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0271119251847267,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
13380,google/t5-efficient-base-el16,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0565430000424384,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
13381,VietAI/gpt-neo-1.3B-vietnamese-news,This is a tiny random mt5 model used for testingSee mt5-make-tiny-model,stas/mt5-tiny-random,0.0772121213376521,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
13382,tau/tavbert-he,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0107905622571706,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
13383,aubmindlab/aragpt2-large,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0453132800757884,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
13384,DemangeJeremy/4-sentiments-with-flaubert,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,1.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13385,UWB-AIR/MQDD-duplicates,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
13386,google/t5-efficient-xl-nl4,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0592279657721519,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
13387,google/t5-efficient-xl-nl8,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0623626504093407,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
13388,stas/t5-very-small-random,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0662682130932807,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
13389,google/t5-efficient-tiny-nh16,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.043878617696464,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
13390,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0518682822585105,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
13391,eli4s/Bert-L12-h256-A4,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0486585986800491,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
13392,microsoft/layoutlm-base-uncased,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0336305350065231,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
13393,tau/tavbert-he,T5-Efficient-SMALL-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el4,0.0390578303486108,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
13394,KoichiYasuoka/roberta-base-ukrainian,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0755019634962081,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
13395,TJMUCH/transcriptome-iseeek,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.0517453849315643,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13396,google/t5-efficient-base-ff12000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
13397,j-hartmann/purchase-intention-english-roberta-large,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0473554053654272,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
13398,google/t5-efficient-mini-nl8,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0406069299206137,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
13399,ebagdasa/propaganda_positive_bart_large,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0750295457740624,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
13400,google/t5-efficient-small-dm2000,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0936743356287479,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
13401,google/t5-efficient-tiny-ff3000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0190372113138437,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
13402,aditeyabaral/sentencetransformer-bert-hinglish-small,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
13403,mrm8488/mobilebert-uncased-finetuned-squadv2,The challenge of this project is to train a model to write the languages of Italy,ErykWdowiak/GPTalian,0.0556627189119656,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
13404,google/t5-efficient-base-kv32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0052246320992708,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
13405,google/t5-efficient-tiny-nl16,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
13406,sentence-transformers/paraphrase-MiniLM-L3-v2,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0543506741523742,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
13407,google/t5-efficient-base-nl32,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0681969411671161,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
13408,google/t5-efficient-large-dm128,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0607718266546725,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
13409,google/t5-efficient-base-ff12000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0526786120608448,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
13410,hfl/chinese-xlnet-mid,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0487543915708859,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
13411,allenai/wmt19-de-en-6-6-big,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0307338014245033,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
13412,google/t5-efficient-base-nl40,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0727120246738195,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
13413,DemangeJeremy/4-sentiments-with-flaubert,"zip, use 180G of data to continue trainingThere is no structure of Longformer-base in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-110M,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13414,neuralspace-reverie/indic-transformers-bn-xlmroberta,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0059079799268926,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
13415,google/t5-efficient-small-kv32,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0845569036900997,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
13416,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff9000,0.0765609182417391,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
13417,mrm8488/deberta-v3-small-finetuned-mrpc,For best results use python as below,pszemraj/led-large-book-summary,0.0916159600019455,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
13418,google/t5-efficient-mini-nl24,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0684680119156837,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
13419,google/t5-efficient-base-dm1000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0554309673607349,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
13420,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"This model is the fine-tuned version of ""DeBERTa v2 XLarge""  using CUAD dataset https://huggingface",akdeniz27/deberta-v2-xlarge-cuad,0.0915182009339332,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
13421,DemangeJeremy/4-sentiments-with-flaubert,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0316551849246025,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13422,google/rembert,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0147355178681512,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
13423,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
13424,mmaguero/gn-bert-tiny-cased,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
13425,google/t5-efficient-base-kv128,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0168788097798824,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
13426,fnlp/bart-base-chinese,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.0795820541679858,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
13427,jnz/electra-ka,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0774133652448654,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
13428,deep-learning-analytics/triviaqa-t5-base,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
13429,google/t5-efficient-mini-nl24,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0649605840444564,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
13430,skt/ko-gpt-trinity-1.2B-v0.5,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0670742504298686,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
13431,aditeyabaral/sentencetransformer-distilbert-hinglish-big,bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:Please refer to this blog post for further detail,bolbolzaban/gpt2-persian,0.0926913321018219,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
13432,negfir/distilbert-base-uncased-finetuned-cola,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0810856595635414,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
13433,akdeniz27/deberta-v2-xlarge-cuad,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.0933859199285507,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
13434,google/t5-efficient-base-ff9000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
13435,hfl/chinese-electra-180g-small-ex-discriminator,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0009091390917698,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
13436,tuner007/pegasus_qa,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0610431171953678,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
13437,google/t5-efficient-small-dm1000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0334695857018232,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
13438,minimaxir/magic-the-gathering,Portuguese (Brazil) model for text inference,rdenadai/BR_BERTo,0.0337332678027451,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
13439,malay-huggingface/t5-tiny-bahasa-cased,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,1.0,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
13440,facebook/blenderbot-3B,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0122666409505264,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
13441,SetFit/deberta-v3-large__sst2__train-8-8,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0612205564975738,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
13442,lannelin/bert-imdb-1hidden,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0458013406023383,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
13443,facebook/wmt21-dense-24-wide-en-x,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,0.0424219790846108,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
13444,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
13445,allenai/wmt16-en-de-12-1,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0649364255368709,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
13446,VietAI/gpt-neo-1.3B-vietnamese-news,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
13447,facebook/hubert-xlarge-ll60k,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0163302511209621,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
13448,google/t5-efficient-large-dm2000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
13449,rinna/japanese-gpt2-xsmall,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0206168772031863,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
13450,gayanin/bart-mlm-pubmed-medterm,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0665045976638794,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
13451,Palak/microsoft_deberta-base_squad,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0544191896915435,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
13452,google/t5-efficient-tiny-nh32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
13453,google/t5-efficient-tiny-nh32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0093154180794953,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
13454,google/t5-efficient-small-kv16,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
13455,google/t5-efficient-base-nh16,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0303509654477238,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
13456,hfl/rbtl3,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
13457,google/t5-efficient-small-kv16,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0778073333203792,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
13458,google/t5-efficient-tiny-nh1,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0303198657929896,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
13459,google/t5-efficient-small-kv128,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.028623228892684,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
13460,google/t5-efficient-tiny-nl2,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.069068145006895,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
13461,google/t5-efficient-small-el16-dl4,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
13462,google/t5-efficient-large-nh4,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0740689560770987,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
13463,Parsa/BBB_prediction_classification_SMILES,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
13464,google/t5-efficient-large-kv32,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0483621312305331,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
13465,negfir/distilbert-base-uncased-finetuned-cola,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0392319820821285,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
13466,tucan9389/kcbert-base-finetuned-squad,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0983923077583313,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
13467,negfir/distilbert-base-uncased-finetuned-squad,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0915011614561081,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
13468,google/t5-efficient-xl-nl28,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0720500890165567,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
13469,malay-huggingface/t5-super-super-tiny-bahasa-cased,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0351070320854584,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
13470,mrm8488/deberta-v3-small-finetuned-mrpc,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0959665179252624,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
13471,ToToKr/kobigbird-bert-base-finetuned-klue,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0860335454344749,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
13472,stas/t5-very-small-random,Pretrained model on English language using a similar objective objective as ELECTRA,funnel-transformer/small,0.0859201774001121,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
13473,google/t5-efficient-base-ff1000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
13474,nherve/flaubert-oral-ft,For best results use python as below,pszemraj/led-large-book-summary,0.0,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
13475,google/t5-efficient-large-dm256,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.052892617881298,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
13476,ian0delond/model-test,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
13477,microsoft/layoutlm-base-uncased,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.099951758980751,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
13478,google/t5-efficient-base-dm1000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
13479,google/t5-efficient-xl-nl4,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.081511840224266,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
13480,ebagdasa/propaganda_positive_bart_large,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0257881221671899,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
13481,mldmm/GlassBERTa,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0224325590534135,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
13482,google/t5-efficient-tiny-nl16,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0128072742372751,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
13483,cammy/led-large-16384-arxiv-100-MDS,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
13484,allenai/macaw-3b,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
13485,sentence-transformers/paraphrase-MiniLM-L3-v2,"In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased ELECTRA base model for Turkish 🎉We release a base ELECTRA model for Turkish, that was trained on the same data as BERTurk",dbmdz/electra-base-turkish-cased-discriminator,0.0767575353384018,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
13486,facebook/blenderbot-90M,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0109445330454036,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
13487,skt/ko-gpt-trinity-1.2B-v0.5,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,1.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
13488,mwesner/reformer-clm,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0092688780277967,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
13489,google/rembert,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
13490,phueb/BabyBERTa-2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0404261348769068,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
13491,TJMUCH/transcriptome-iseeek,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 240",eli4s/Bert-L12-h240-A12,0.0999744832515716,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13492,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0543506741523742,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
13493,tau/tavbert-he,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0164516242220997,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
13494,mwesner/bart-mlm,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0825137868523597,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
13495,DemangeJeremy/4-sentiments-with-flaubert,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0514935888350009,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13496,stas/pegasus-cnn_dailymail-tiny-random,"Organization of Language Understanding Evaluation benchmark for Chinese: tasks and datasets, baselines, pre-trained Chinese models, corpus and leaderboard",clue/xlnet_chinese_large,0.0649719256907701,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
13497,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
13498,mrp/simcse-model-wangchanberta,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0674295090138911,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
13499,pszemraj/led-large-book-summary,"Important: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch)",pszemraj/led-large-book-summary,1.0,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
13500,qarib/bert-base-qarib_far_6500k,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0414502245839685,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
13501,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,This is an ELECTRA model pretrained on approximately 200M Japanese sentences extracted from the mC4 and finetuned by spaCy v3 on UD_Japanese_BCCWJ r2.8,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,1.0,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
13502,kyryl0s/gpt2-uk-xxs,"6,860,062 examples are extracted from below corpora",lassl/roberta-ko-small,0.020933917723596,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
13503,google/t5-efficient-base-nh8,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0847240723669528,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
13504,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,0.012030117213726,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13505,google/t5-efficient-small-dm128,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0697001405060291,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
13506,google/t5-efficient-base-nh8,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0820143744349479,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
13507,hfl/rbt3,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0750453807413577,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
13508,ptaszynski/yacis-electra-small-japanese,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0304427975788712,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
13509,google/t5-efficient-small-nl22,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0767225176095962,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
13510,hfl/rbtl3,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0693051144480704,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
13511,tucan9389/kcbert-base-finetuned-squad,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0497244745492935,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
13512,google/t5-efficient-small-nl2,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0855596587061881,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
13513,Finnish-NLP/convbert-base-generator-finnish,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0456631809473037,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
13514,kyryl0s/gpt2-uk-xxs,For best results use python as below,pszemraj/led-large-book-summary,0.0300993993878364,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
13515,diper1998/distilgpt2-finetuned-AT,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0211193263530731,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
13516,allenai/wmt19-de-en-6-6-base,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
13517,google/t5-efficient-tiny-nh32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0668334811925887,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
13518,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
13519,google/t5-efficient-xl-nl6,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0187904518097639,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
13520,skt/ko-gpt-trinity-1.2B-v0.5,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0412403717637061,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
13521,gilparmentier/pokemon_gptj_model,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
13522,google/t5-efficient-base-nl32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0025745891034603,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
13523,google/t5-efficient-small-el12,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0708589851856231,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
13524,google/t5-efficient-base-kv128,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0531294075772165,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
13525,aubmindlab/aragpt2-mega,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
13526,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0910606384277343,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
13527,Palak/microsoft_deberta-base_squad,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
13528,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.088138535618782,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
13529,microsoft/layoutlm-base-cased,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.099951758980751,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
13530,rdenadai/BR_BERTo,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
13531,rdenadai/BR_BERTo,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0356433975199858,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
13532,google/t5-efficient-base-nl24,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0730805099010467,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
13533,sentence-transformers/paraphrase-MiniLM-L3-v2,"Ko-GPT-Trinity refers to the class of models, while 1.2B represents the number of parameters of this particular pre-trained model",skt/ko-gpt-trinity-1.2B-v0.5,0.0537021234631538,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
13534,hajime9652/xlnet-japanese,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0807952582836151,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
13535,j-hartmann/purchase-intention-english-roberta-large,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,1.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
13536,IDEA-CCNL/Erlangshen-Longformer-330M,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.0370667818933725,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
13537,google/t5-efficient-small-el4,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0681414566934108,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
13538,google/t5-efficient-large-nh8,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0834071002900599,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
13539,google/t5-efficient-small-kv16,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0591340512037276,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
13540,google/t5-efficient-base-nl36,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0503659788519143,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
13541,tau/tavbert-he,T5-Efficient-BASE-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl2,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
13542,asi/gpt-fr-cased-base, is a GPT model for French developped by Quantmetry and the Laboratoire de Linguistique Formelle (LLF),asi/gpt-fr-cased-base,1.0,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
13543,google/t5-efficient-small-kv256,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
13544,tau/tavbert-he,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
13545,crabz/distil-slovakbert-ner,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0870006456971168,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
13546,amazon/bort,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0191066619008779,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13547,google/t5-efficient-xl-nl16,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0566721744835376,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
13548,SupriyaArun/squeezebert-uncased-finetuned-squad,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0618417598307132,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
13549,microsoft/layoutlm-large-uncased,This project was funded by the Language Technology Programme for Icelandic 2019-2023,jonfd/convbert-small-igc-is,0.082274779677391,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
13550,google/t5-efficient-base-dm2000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
13551,pszemraj/led-large-book-summary,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0382326636463403,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
13552,google/t5-efficient-base-dm1000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0297601372003555,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
13553,google/t5-efficient-base-nh8,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
13554,yarongef/DistilProtBert,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
13555,google/t5-11b-ssm-nqo,Google's T5 for Closed Book Question Answering,google/t5-11b-ssm-nqo,1.0,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
13556,TJMUCH/transcriptome-iseeek,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0454391315579414,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13557,google/t5-efficient-large-dm512,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0590602234005927,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
13558,groar/gpt-neo-1.3B-finetuned-escape2,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0934296399354934,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
13559,google/t5-efficient-large-nh8-nl32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
13560,google/t5-efficient-large-nh8-nl32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0147001817822456,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
13561,cosmoquester/bart-ko-mini,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,0.054090330377221,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
13562,google/t5-efficient-large-nh24,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.07145806401968,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
13563,ctoraman/RoBERTa-TR-medium-char,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0591844040900468,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
13564,hyunwoongko/reddit-9B,"While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot",hyunwoongko/reddit-9B,1.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
13565,TJMUCH/transcriptome-iseeek,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0681803300976753,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13566,google/t5-efficient-small-dm128,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0022494681179523,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
13567,j-hartmann/purchase-intention-english-roberta-large,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0029786260177692,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
13568,groar/gpt-neo-1.3B-finetuned-escape2,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0964745953679084,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
13569,DemangeJeremy/4-sentiments-with-flaubert,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0390159152448177,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13570,google/t5-efficient-mini-nl12,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0746603533625602,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
13571,negfir/distilbert-base-uncased-finetuned-cola,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0607916489243507,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
13572,superspray/electra_large_discriminator_squad2_custom_dataset,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0236710235476493,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
13573,google/t5-efficient-large-nh4,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0847007706761359,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
13574,google/t5-efficient-large-nl10,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0718015227466821,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
13575,hfl/chinese-legal-electra-base-generator,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0612745142231384,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
13576,malay-huggingface/xlnet-large-bahasa-cased,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0012658983469009,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
13577,dhpollack/distilbert-dummy-sentiment,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0082251355051994,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
13578,mrm8488/mobilebert-uncased-finetuned-squadv2,The checkpoint used here is the original MobileBert Optimized Uncased English: (uncased_L-24_H-128_B-512_A-4_F-4_OPT) checkpoint,mrm8488/mobilebert-uncased-finetuned-squadv2,1.0,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
13579,hfl/rbt3,The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider max_seq_length (4096 vs,yorko/scibert_scivocab_uncased_long_4096,0.0572312511503696,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
13580,mimicheng/codeparrot-ds,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
13581,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Amazon's BORT,amazon/bort,0.0831830650568008,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
13582,google/t5-efficient-base-nl4,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0489150285720824,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
13583,tartuNLP/gpt-4-est-large,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0912923514842987,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
13584,google/t5-efficient-large-nl20,Its original repository can be found here,tau/splinter-large-qass,0.0492282304912805,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
13585,kyryl0s/gpt2-uk-xxs,"We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations",saurkulsh/T0pp,0.017999593168497,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
13586,google/t5-efficient-mini-nl6,Its original repository can be found here,tau/splinter-large-qass,0.0486385393887757,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
13587,uer/bart-chinese-4-768-cluecorpussmall,Its original repository can be found here,tau/splinter-large-qass,0.0414808727800845,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
13588,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0171573632396757,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
13589,DemangeJeremy/4-sentiments-with-flaubert,"This model was trained from 6,860,062 examples (whose have 3,512,351,744 tokens)",lassl/roberta-ko-small,0.0711398124694823,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13590,allenai/wmt19-de-en-6-6-big,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.01021015830338,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
13591,google/t5-efficient-large-nl36,This does not make a very readable text,pere/DeUnCaser,0.0555520877242087,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
13592,Recognai/zeroshot_selectra_small,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
13593,DeepPavlov/distilrubert-tiny-cased-conversational-v1,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.0374116795137524,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
13594,zoha/wav2vec2-base-timit-demo-colab,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0463957637548446,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
13595,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This model used the pre-trained weights of distilbert-base-cased as starting point (only 4 layers) and was able to achieve 84% accuracy on the test set,khizon/distilbert-unreliable-news-eng-4L,0.0645280629396438,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
13596,cammy/led-large-16384-arxiv-100-MDS,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0618441402912139,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
13597,google/t5-efficient-large-nh2,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
13598,google/t5-efficient-small-dm768,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0600157640874385,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
13599,tuner007/pegasus_qa,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0234666801989078,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
13600,bolbolzaban/gpt2-persian,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0207967208698392,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
13601,google/t5-efficient-tiny-ff9000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.05743985529989,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
13602,skt/ko-gpt-trinity-1.2B-v0.5,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
13603,Recognai/zeroshot_selectra_small,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
13604,oskrmiguel/mt5-simplification-spanish,"This model was created with the IXA Group research group of the University of the Basque Country, the model has been evaluated with the Sari, Bleu and Fklg metrics; it was trained and tested using the Simplext corpus",oskrmiguel/mt5-simplification-spanish,1.0,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
13605,google/t5-efficient-small-ff9000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0623377189040183,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
13606,dhpollack/distilbert-dummy-sentiment,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0765731632709502,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
13607,google/t5-efficient-large-nh4,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0494518689811229,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
13608,KoichiYasuoka/roberta-base-ukrainian,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0985994860529899,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
13609,facebook/hubert-xlarge-ll60k,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0384683771990239,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
13610,razent/SciFive-large-Pubmed,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0256055518984794,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
13611,Recognai/selectra_medium,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,1.0,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
13612,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13613,dbmdz/bert-medium-historic-multilingual-cased,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0801296569406985,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
13614,google/t5-efficient-small-kv128,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0532588399946689,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
13615,DemangeJeremy/4-sentiments-with-flaubert,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0177079895511269,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13616,nherve/flaubert-oral-asr,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0356408574928839,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
13617,microsoft/layoutlm-large-uncased,It was introduced in this paper,readerbench/RoBERT-small,0.0175572820007801,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
13618,piotr-rybak/poleval2021-task4-herbert-large-encoder,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.043068952858448,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
13619,hfl/rbtl3,T5-Efficient-BASE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv32,0.0404702741652727,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
13620,hakurei/gpt-j-random-tinier,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0299896681681275,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
13621,google/t5-efficient-tiny-nh8,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0268719438463449,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
13622,google/t5-efficient-large-kv32,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0662700422108172,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
13623,Rostlab/prot_bert,Pretrained model on protein sequences using a masked language modeling (MLM) objective,Rostlab/prot_bert,1.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
13624,google/t5-efficient-large-el2,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0507158376276492,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
13625,hfl/rbt3,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,0.0503606861457228,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
13626,superspray/electra_large_discriminator_squad2_custom_dataset,It can be considered as tiny copy of Conversational DistilRuBERT-small,DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0761349871754646,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
13627,google/t5-efficient-small-nl36,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.075967963784933,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
13628,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Its original repository can be found here,tau/splinter-large-qass,0.0485492292791604,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
13629,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0097575392574071,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13630,google/t5-efficient-small-ff12000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0473382212221622,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
13631,google/t5-efficient-small-nl22,T5-Efficient-SMALL-NL22 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl22,1.0,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
13632,allenai/macaw-answer-11b,Slovak GPT-J-162M is the first model released in Slovak GPT-J series and the very first publicly available transformer trained predominantly on Slovak corpus,Milos/slovak-gpt-j-162M,0.0439049638807773,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
13633,jnz/electra-ka,You should fine-tune this model for your downstream task,google/rembert,0.0940808951854705,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
13634,mmaguero/gn-bert-tiny-cased,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0794751681387423,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
13635,bolbolzaban/gpt2-persian,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.044555019121617,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
13636,google/t5-efficient-xl-nl6,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0710850656032562,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
13637,tau/tavbert-he,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
13638,google/t5-efficient-small-nl20,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0599031355232,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
13639,google/t5-11b-ssm-nqo,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
13640,stas/mt5-tiny-random,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0336257964372634,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
13641,superspray/electra_large_discriminator_squad2_custom_dataset,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
13642,google/t5-efficient-small-el4,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0473611820489167,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
13643,beston91/gpt2-xl_ft_logits_10k,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
13644,tucan9389/kcbert-base-finetuned-squad,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
13645,google/t5-efficient-large-el8,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0430997088551521,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
13646,piotr-rybak/poleval2021-task4-herbert-large-encoder,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,0.0975374653935432,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
13647,suzuki/distilbert-base-uncased-finetuned-squad,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
13648,google/t5-efficient-tiny-nl12,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0559650883078575,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
13649,mikaelsouza/msft-regular-model,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0710733458399772,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
13650,liaad/srl-pt_bertimbau-large,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
13651,google/t5-efficient-base-dm256,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0256626261398195,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
13652,dexhrestha/Nepali-DistilBERT,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
13653,google/t5-efficient-base-dm512,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0676107853651045,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
13654,google/t5-efficient-large-dm768,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0498766135424375,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
13655,gchhablani/fnet-base-finetuned-wnli,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0072558727115393,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
13656,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff6000,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13657,google/t5-efficient-large-dm2000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
13658,google/t5-efficient-large-nh24,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
13659,hfl/rbtl3,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0438426560722291,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
13660,uer/bart-chinese-4-768-cluecorpussmall,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0739253405481576,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
13661,google/reformer-enwik8,com/storage/browser/trax-ml/reformer/enwik8 and converted  to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead,google/reformer-enwik8,1.0,-1.0,NLP,6aa23d61-51e8-44d6-b6b7-b68cce7cd0c1___reformer-enwik8,HF
13662,google/t5-efficient-base-kv128,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0715654641389846,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
13663,mgreenbe/bertlet-base-uncased-for-sequence-classification,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0791142359375953,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
13664,mimicheng/codeparrot-ds,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0178902223706245,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
13665,aubmindlab/aragpt2-mega,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
13666,andrejmiscic/simcls-scorer-billsum,"Namely, we use a summary level Rouge-L score",andrejmiscic/simcls-scorer-billsum,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
13667,mimi/Waynehills-NLP-doogie,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0672744065523147,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
13668,google/t5-efficient-tiny-nh8,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.029094859957695,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
13669,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0929745286703109,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
13670,google/t5-efficient-base-nh8,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0107903177849948,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
13671,google/t5-efficient-base-dm512,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0455571077764033,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
13672,SupriyaArun/squeezebert-uncased-finetuned-squad,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0642759874463081,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
13673,KoichiYasuoka/roberta-base-ukrainian,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0937712490558624,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
13674,anirudh21/xlnet-base-cased-finetuned-rte,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0806283950805664,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
13675,google/t5-efficient-small-dm256,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0231578750535845,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
13676,google/t5-efficient-small-nl48,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0510830413550138,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
13677,jnz/electra-ka,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0425703898072242,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
13678,google/t5-efficient-small-nl40,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
13679,google/t5-efficient-small-ff9000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0478761252015828,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
13680,malay-huggingface/xlnet-large-bahasa-cased,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.05475681523482,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
13681,amazon/bort,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0244170781224965,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13682,anirudh21/xlnet-base-cased-finetuned-rte,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0622115172445774,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
13683,google/t5-efficient-large-nh32,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0532503351569175,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
13684,IDEA-CCNL/Zhouwenwang-Unified-110M,"It will continue to be optimized in the direction of model scale, knowledge integration, and supervision task assistance",IDEA-CCNL/Zhouwenwang-Unified-110M,1.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
13685,VietAI/gpt-neo-1.3B-vietnamese-news,They used a generative recurrent neural network (RNN) containing long short‐term memory (LSTM) cell to capture the syntax of molecular representations in terms of SMILES strings,mrm8488/chEMBL_smiles_v1,0.0458869962021708,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
13686,hf-internal-testing/tiny-random-rembert,"The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0269825831055641,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
13687,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0726688504219055,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
13688,minimaxir/magic-the-gathering,LayoutLM archives the SOTA results on multiple datasets,microsoft/layoutlm-large-uncased,0.0730657037347554,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
13689,google/t5-efficient-large-nh12,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0460835937410592,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
13690,microsoft/layoutlm-large-uncased,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
13691,google/t5-efficient-base-nl48,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-nl48,1.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
13692,google/t5-efficient-base-dm512,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0064232181757688,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
13693,google/t5-efficient-base-nl32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-nl32,1.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
13694,dbmdz/electra-base-turkish-cased-discriminator,"In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased ELECTRA base model for Turkish 🎉We release a base ELECTRA model for Turkish, that was trained on the same data as BERTurk",dbmdz/electra-base-turkish-cased-discriminator,1.0,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
13695,microsoft/layoutlm-base-uncased,A ridiculously small ELECTRA generator model for testing purposes,lgrobol/electra-minuscule-generator,0.0969329848885536,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
13696,microsoft/layoutlm-base-uncased,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.010490633547306,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
13697,google/t5-efficient-large-kv32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
13698,IDEA-CCNL/Erlangshen-Longformer-330M,The best model parameters searched via quantization-aware finetuning can be then exported (e,kssteven/ibert-roberta-large,0.0671932231634855,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
13699,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
13700,google/t5-efficient-small-el4,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
13701,mnaylor/bigbird-base-mimic-mortality,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
13702,TJMUCH/transcriptome-iseeek,The model is able to generate a reason why a given natural language statement is against commonsense,aliosm/ComVE-gpt2-large,0.0868033468723297,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13703,VietAI/gpt-neo-1.3B-vietnamese-news,This model used the pre-trained weights of distilbert-base-cased as starting point (only 4 layers) and was able to achieve 84% accuracy on the test set,khizon/distilbert-unreliable-news-eng-4L,0.0571132507175206,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
13704,Palak/microsoft_deberta-base_squad,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0870762765407562,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
13705,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
13706,google/t5-efficient-base-nh32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0670224018394946,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
13707,IDEA-CCNL/Erlangshen-MegatronBert-1.3B,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0125995827838778,-1.0,NLP,3c1adb6c-e774-450e-967f-c26c07931f49___Erlangshen-MegatronBert-1.3B,HF
13708,microsoft/layoutlm-large-uncased,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
13709,google/t5-efficient-large-el2,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0741534195840358,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
13710,stas/mt5-tiny-random,Amazon's BORT,amazon/bort,0.04867709800601,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
13711,mwesner/reformer-clm,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,0.0530247576534748,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
13712,DemangeJeremy/4-sentiments-with-flaubert,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0775986108928918,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13713,SetFit/deberta-v3-large__sst2__train-8-8,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0407695807516574,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
13714,hfl/chinese-legal-electra-base-generator,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
13715,google/t5-efficient-xl-nl4,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0357503108680248,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
13716,rmihaylov/pegasus-base-cnn-dailymail-bg,You can use the raw model for summarization,rmihaylov/pegasus-base-cnn-dailymail-bg,1.0,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
13717,aditeyabaral/sentencetransformer-bert-hinglish-small,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,0.0947665721178054,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
13718,google/t5-efficient-large-nh24,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nh24,1.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
13719,superspray/electra_large_discriminator_squad2_custom_dataset,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0617807954549789,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
13720,aditeyabaral/sentencetransformer-bert-hinglish-small,This model is a rather small instance of GPT-2 trained on TristanBehrens/js-fakes-4bars,TristanBehrens/js-fakes-4bars,0.0637010633945465,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
13721,mgreenbe/bertlet-base-uncased-for-sequence-classification,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
13722,clue/roberta_chinese_3L312_clue_tiny,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0055869445204734,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
13723,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.0580384470522403,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
13724,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0765458419919014,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
13725,jkeruotis/LitBERTa-uncased,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0576938223093747,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
13726,DemangeJeremy/4-sentiments-with-flaubert,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13727,google/t5-efficient-mini-nl6,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0688493382185697,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
13728,google/t5-efficient-mini-nl8,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0365633871406316,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
13729,google/t5-efficient-tiny-nl24,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0595694910734891,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
13730,google/t5-efficient-xl-nl16,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0883514583110809,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
13731,celine98/canine-s-finetuned-sst2,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0165224038064479,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
13732,sentence-transformers/paraphrase-MiniLM-L3-v2,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0537927113473415,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
13733,google/t5-efficient-base-nl4,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
13734,google/t5-efficient-base-dm2000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0356625271961093,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
13735,google/bert_uncased_L-6_H-256_A-4,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0170423748592535,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
13736,google/t5-efficient-tiny-nh8,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0919342972338199,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
13737,allenai/wmt16-en-de-dist-6-1,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0623438656330108,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
13738,allenai/wmt16-en-de-dist-6-1,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0070020891726017,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
13739,aliosm/ComVE-gpt2-large,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.032716168090701,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
13740,facebook/wmt21-dense-24-wide-en-x,It has less than 1% difference in performance compared to the BERT based model while having 2.0x the speed,khizon/distilbert-unreliable-news-eng-4L,0.0408222841098904,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
13741,anirudh21/xlnet-base-cased-finetuned-rte,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
13742,stas/mt5-tiny-random,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0585163421928882,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
13743,google/t5-efficient-tiny-nl24,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0146993538364768,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
13744,google/t5-efficient-xl-nl4,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0786212682723999,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
13745,cammy/led-base-16384-100-MDS,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0109053365886211,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
13746,KoichiYasuoka/roberta-small-belarusian,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0846924223005771,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
13747,mimi/Waynehills-NLP-doogie,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0994460433721542,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
13748,ncduy/phobert-large-finetuned-vietnamese_students_feedback,This model is a fine-tuned version of vinai/phobert-large on the vietnamese_students_feedback dataset,ncduy/phobert-large-finetuned-vietnamese_students_feedback,1.0,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
13749,mwesner/reformer-clm,T5-Efficient-BASE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh24,0.0872147977352142,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
13750,mimi/Waynehills-NLP-doogie,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0817509889602661,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
13751,mwesner/reformer-clm,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
13752,negfir/distilbert-base-uncased-finetuned-squad,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0598377212882041,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
13753,abdusahmbzuai/ft-tatoeba-ar-en,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0596628412604332,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
13754,eli4s/Bert-L12-h384-A6,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
13755,eli4s/Bert-L12-h384-A6,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0217123658706744,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
13756,mikaelsouza/msft-regular-model,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0988444238901138,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
13757,google/t5-efficient-base-nh24,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0307701500132679,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
13758,tartuNLP/gpt-4-est-large,FongBERT is a BERT model trained on 68.363 sentences in Fon,Gilles/FongBERT,0.041340447962284,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
13759,google/t5-efficient-base-ff9000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0100119747221469,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
13760,rinna/japanese-gpt-1b,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0380991759399572,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
13761,google/t5-efficient-large-dm768,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
13762,hajime9652/xlnet-japanese,This model is a distilled version of wav2vec2-adult-child-cls on a private adult/child speech classification dataset,bookbot/distil-wav2vec2-adult-child-cls-52m,0.0837497264146804,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
13763,clue/roberta_chinese_3L312_clue_tiny,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0255270544439553,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
13764,google/t5-efficient-xl-nl12,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
13765,1one5ome/gpt2-chinese-gulong,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0443259663879871,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
13766,google/t5-efficient-base-el8,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0363669544458388,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
13767,google/t5-efficient-small-nl20,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0148218357935548,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
13768,CuongLD/wav2vec2-large-xlsr-vietnamese,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0776246469467878,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
13769,qarib/bert-base-qarib_far_6500k,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
13770,google/t5-efficient-small-kv32,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0155330374836921,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
13771,Palak/microsoft_deberta-base_squad,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0181315559893846,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
13772,aubmindlab/aragpt2-mega,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0333852069452404,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
13773,DemangeJeremy/4-sentiments-with-flaubert,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13774,microsoft/layoutlm-large-uncased,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0519517138600349,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
13775,google/t5-efficient-large-dm128,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0210293224081397,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
13776,google/t5-efficient-xl-nl4,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.069824568927288,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
13777,rahulacj/bertweet-base-finetuned-sentiment-analysis,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0202827248722314,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
13778,mwesner/reformer-clm,T5-Efficient-LARGE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv32,0.0474032685160636,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
13779,flax-community/gpt2-medium-persian,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0285650851825873,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
13780,Fujitsu/pytorrent,"We use 1M raw Python scripts of PyTorrent that includes 12,350,000 LOC to train the model",Fujitsu/pytorrent,1.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
13781,google/t5-efficient-tiny-nl6,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0586905237287282,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
13782,google/t5-efficient-tiny-nl24,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-nl24,1.0,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
13783,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0963555574417114,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
13784,microsoft/unihanlm-base,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0288132254499941,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
13785,zoha/wav2vec2-base-timit-demo-colab,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0242889449000358,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
13786,aubmindlab/aragpt2-large,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
13787,google/t5-efficient-tiny-nl32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0611736383289098,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
13788,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0652022659778595,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
13789,google/t5-efficient-tiny-nl16,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0084721371531485,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
13790,google/t5-efficient-base-nl24,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0415820162743329,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
13791,diper1998/distilgpt2-finetuned-AT,"The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0624756850302219,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
13792,cross-encoder/stsb-TinyBERT-L-4,Pre-trained models can be used like this:,cross-encoder/stsb-TinyBERT-L-4,1.0,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
13793,hf-internal-testing/tiny-random-rembert,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0136192273348569,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
13794,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl20,0.077835775911808,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
13795,beston91/gpt2-xl_ft_logits_10k,It was introduced in this paper,readerbench/RoBERT-small,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
13796,saibo/random-roberta-mini,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
13797,jorge-henao/gpt2-small-spanish-disco-poetry-wt,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0993138253688812,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
13798,jnz/electra-ka,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0312109962105751,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
13799,google/t5-efficient-small-kv256,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
13800,mimi/Waynehills-NLP-doogie,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0313436202704906,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
13801,mwesner/bart-mlm,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
13802,dhpollack/distilbert-dummy-sentiment,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0051594749093056,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
13803,mwesner/reformer-clm,T5-Efficient-BASE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el6,0.045007649809122,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
13804,lgris/distilxlsr_bp_8-12,When using the model make sure that your speech input is also sampled at 16Khz,lgris/distilxlsr_bp_8-12,1.0,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
13805,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0100526809692382,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
13806,IDEA-CCNL/Erlangshen-Longformer-110M,This model is case-sensitive: it makes a difference between tirana and Tirana,macedonizer/al-roberta-base,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
13807,1one5ome/gpt2-chinese-gulong,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0795936584472656,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
13808,tau/tavbert-he,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
13809,google/t5-efficient-large-nh12,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0490382653661072,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
13810,mimicheng/codeparrot-ds,It was introduced in this paper,readerbench/RoBERT-small,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
13811,filco306/gpt2-switchboard-paraphraser,"Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text)",pszemraj/led-large-book-summary,0.0153623120859265,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
13812,aliosm/ComVE-gpt2-large,"Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint",KBLab/megatron-bert-large-swedish-cased-165k,0.0456948122009634,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
13813,castorini/afriberta_large,"The model has 10 layers, 6 attention heads, 768 hidden units and 3072 feed forward size",castorini/afriberta_large,1.0,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
13814,GKLMIP/electra-myanmar-small-uncased,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0204035881906748,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
13815,Iskaj/hf-challenge-test,This KinyaRoBERTa model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda,jean-paul/kinyaRoberta-small,0.0908947512507438,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
13816,IDEA-CCNL/Erlangshen-Longformer-330M,This is a multilingual model,ErykWdowiak/GPTalian,0.0646646805107593,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
13817,rdenadai/BR_BERTo,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
13818,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0854204073548317,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
13819,gayanin/bart-mlm-pubmed-medterm,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0900582075119018,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
13820,google/t5-efficient-large-kv128,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
13821,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff12000,0.0811534523963928,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
13822,Iskaj/hf-challenge-test,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0847154185175895,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
13823,google/t5-efficient-large-el2,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-el2,1.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
13824,beston91/gpt2-xl_ft_logits_10k,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0606939941644668,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
13825,google/t5-efficient-small-dm2000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0051771774888038,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
13826,google/t5-efficient-xxl-nl4,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0354709192179143,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
13827,clue/xlnet_chinese_large,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0408603148534893,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
13828,google/t5-efficient-large-kv128,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
13829,google/t5-efficient-large-el6,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0276736738160252,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
13830,Milos/slovak-gpt-j-405M,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0684239317973454,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
13831,google/t5-efficient-tiny-nh16,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0483682770282029,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
13832,DeepPavlov/distilrubert-tiny-cased-conversational-v1,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
13833,piotr-rybak/poleval2021-task4-herbert-large-encoder,"You can download the set of Chinese PEGASUS models either from the UER-py Modelzoo page, or via HuggingFace from the links below:You can use this model directly with a pipeline for text2text generation (take the case of PEGASUS-Base):",uer/pegasus-base-chinese-cluecorpussmall,0.0374730229377746,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
13834,TJMUCH/transcriptome-iseeek,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13835,amazon/bort,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0448838341981172,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13836,lgrobol/electra-minuscule-generator,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0563934836536645,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
13837,SetFit/deberta-v3-large__sst2__train-8-8,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0917778685688972,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
13838,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU,kssteven/ibert-roberta-large,0.0903237983584404,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
13839,aubmindlab/aragpt2-large,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0270694578066468,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
13840,suzuki/distilbert-base-uncased-finetuned-squad,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
13841,google/t5-efficient-base-dm256,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0045571504160761,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
13842,google/t5-efficient-large-dm512,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0884794741868972,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
13843,google/t5-efficient-xl-nl16,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.015229009091854,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
13844,skt/ko-gpt-trinity-1.2B-v0.5,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
13845,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0318707968108355,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
13846,google/t5-efficient-small-dm2000,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0315624680370092,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
13847,allenai/macaw-answer-11b,This model is a fine-tuned version of gpt2 on an unknown dataset,mimicheng/codeparrot-ds,0.0728215320656696,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
13848,google/t5-efficient-base-kv16,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.076015830039978,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
13849,beston91/gpt2-xl_ft_logits_10k,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
13850,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0595554150640964,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13851,andrejmiscic/simcls-scorer-billsum,See SimCLS paper for a description of baselines,andrejmiscic/simcls-scorer-billsum,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
13852,hyunwoongko/reddit-9B,We then discuss the limitations of this work by analyzing failure cases of our models,hyunwoongko/reddit-9B,1.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
13853,mgreenbe/bertlet-base-uncased-for-sequence-classification,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0561216846108436,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
13854,neuralspace-reverie/indic-transformers-bn-xlmroberta,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0001226586422749,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
13855,mldmm/GlassBERTa,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0267069704132154,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
13856,google/t5-efficient-xl-nl16,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,0.0721667408943176,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
13857,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0629512369632721,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13858,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
13859,google/t5-efficient-large-el8,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
13860,google/t5-efficient-base-kv32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0742553770542144,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
13861,hfl/chinese-xlnet-mid,RobBERTje is a collection of distilled models based on RobBERT,DTAI-KULeuven/robbertje-1-gb-bort,0.0779901295900344,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
13862,DemangeJeremy/4-sentiments-with-flaubert,"Finetuning of I-BERT consists of 3 stages: (1) Full-precision finetuning from the pretrained model on a down-stream task, (2) model quantization, and (3) integer-only finetuning (i",kssteven/ibert-roberta-large,0.0553198941051959,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13863,GKLMIP/electra-myanmar-small-uncased,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0061399256810545,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
13864,sentence-transformers/paraphrase-MiniLM-L3-v2,This model is a fine-tuned version of distilgpt2 on the None dataset,diper1998/distilgpt2-finetuned-AT,0.0754712000489234,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
13865,google/t5-efficient-large-nl20,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0508113186806439,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
13866,danyaljj/opengpt2_pytorch_forward,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0515324585139751,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
13867,hfl/rbtl3,T5-Efficient-BASE-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl4,0.0348509289324283,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
13868,google/t5-efficient-tiny-nl6,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0465186526998877,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
13869,sberbank-ai/rugpt3medium_based_on_gpt2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
13870,hakurei/gpt-j-random-tinier,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0006972700357437,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
13871,mimicheng/codeparrot-ds,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0688017755746841,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
13872,diper1998/distilgpt2-finetuned-AT,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0631789714097976,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
13873,google/t5-efficient-tiny-nh1,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0917763859033584,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
13874,amazon/bort,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0668045356869697,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13875,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0483469888567924,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
13876,rinna/japanese-gpt2-xsmall,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
13877,google/t5-efficient-base-kv32,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0686873644590377,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
13878,google/t5-efficient-base-nh8,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0737307704985141,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
13879,google/t5-efficient-base-nl48,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
13880,uer/chinese_roberta_L-2_H-256,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.046756212413311,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
13881,ian0delond/model-test,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0646418035030365,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
13882,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0547000952064991,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
13883,negfir/distilbert-base-uncased-finetuned-squad,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0824621692299842,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
13884,fnlp/bart-large-chinese,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.048029214143753,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
13885,nyu-mll/roberta-base-10M-3,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0261893428396433,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
13886,amazon/bort,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0294866869226097,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13887,google/t5-efficient-tiny-ff3000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0220252033323049,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
13888,google/t5-efficient-tiny-nl2,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0456615518778562,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
13889,google/t5-efficient-large-dm768,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.043238641694188,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
13890,google/t5-efficient-small-nl40,Its original repository can be found here,tau/splinter-large-qass,0.0473063196986913,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
13891,google/t5-efficient-small-el16-dl4,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0715241767466068,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
13892,Milos/slovak-gpt-j-405M,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0549436757961908,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
13893,nghuyong/ernie-1.0,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.052350983954966,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
13894,FremyCompany/xls-r-nl-v1-cv8-lm,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
13895,KoichiYasuoka/roberta-base-ukrainian,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0489388443529605,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
13896,mrm8488/deberta-v3-small-finetuned-mrpc,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0789858549833297,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
13897,sonoisa/byt5-small-japanese,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0943296253681182,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
13898,hajime9652/xlnet-japanese,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0165019854903221,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
13899,stas/pegasus-cnn_dailymail-tiny-random,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0126095674932002,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
13900,google/reformer-crime-and-punishment,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0418692249804735,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
13901,google/t5-efficient-tiny-nl24,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
13902,google/t5-efficient-base-el6,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0667122453451156,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
13903,google/t5-efficient-large-dm2000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0348968096077441,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
13904,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
13905,IDEA-CCNL/Erlangshen-Longformer-330M,"This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:The model was fine-tuned on 2,000 manually annotated social media posts",j-hartmann/purchase-intention-english-roberta-large,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
13906,CuongLD/wav2vec2-large-xlsr-vietnamese,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0649499371647834,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
13907,minimaxir/magic-the-gathering,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
13908,THUMT/mGPT,It was introduced in this paper and first released on this page,THUMT/mGPT,1.0,-1.0,NLP,337b82e9-8452-490f-be57-2e0059a8854e___mGPT,HF
13909,google/t5-efficient-tiny-nl6,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0599469281733035,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
13910,KoichiYasuoka/roberta-base-ukrainian,"The model has 4 layers, 6 attention heads, 768 hidden units and 3072 feed forward size",castorini/afriberta_small,0.0371289700269699,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
13911,bookbot/distil-wav2vec2-adult-child-cls-37m,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
13912,akdeniz27/deberta-v2-xlarge-cuad,"The training data is private Bulgarian text from CNN, DailyMail articles",rmihaylov/pegasus-base-cnn-dailymail-bg,0.0760691463947296,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
13913,piotr-rybak/poleval2021-task4-herbert-large-encoder,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
13914,minimaxir/magic-the-gathering,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0009416490793228,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
13915,google/t5-efficient-small-dm128,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0478728953748941,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
13916,stas/pegasus-cnn_dailymail-tiny-random,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
13917,aditeyabaral/sentencetransformer-bert-hinglish-small,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0959862545132637,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
13918,allenai/macaw-answer-11b,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.073385568956534,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
13919,google/t5-efficient-base-nl2,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0009145643562078,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
13920,uer/chinese_roberta_L-2_H-256,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0358733031898736,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
13921,google/t5-efficient-small-nl20,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0584816075861453,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
13922,TJMUCH/transcriptome-iseeek,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0500101596117019,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13923,google/t5-efficient-small-nl22,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0575394220650195,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
13924,tau/tavbert-he,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.018381156027317,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
13925,google/t5-efficient-base-kv16,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
13926,microsoft/layoutlm-base-uncased,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
13927,IDEA-CCNL/Erlangshen-Longformer-330M,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0232164096087217,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
13928,tartuNLP/gpt-4-est-base,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,0.0643521584570407,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
13929,google/t5-efficient-base-ff12000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0671709682792425,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
13930,DemangeJeremy/4-sentiments-with-flaubert,", MatMul, GELU, Softmax, and LayerNorm) with closely approximating integer operations",kssteven/ibert-roberta-large,0.049886779859662,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
13931,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0672150775790214,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
13932,google/t5-efficient-tiny-nl16,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0266378577798605,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
13933,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.017205461859703,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
13934,google/t5-efficient-xl-nl2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0363528821617364,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
13935,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Its original repository can be found here,tau/splinter-large-qass,0.0728876814246177,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13936,ai4bharat/MultiIndicHeadlineGeneration,Amazon's BORT,amazon/bort,0.0387438034328321,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
13937,tobiaslee/bert-2l-768h-uncased,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.0507765319198369,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
13938,j-hartmann/purchase-intention-english-roberta-large,GPT2-base and medium uses the code from the gpt2 folder and can trains models from the minimaxir/gpt-2-simple repository,aubmindlab/aragpt2-mega,0.0345247735579808,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
13939,gchhablani/fnet-base-finetuned-wnli,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0698322858661413,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
13940,google/t5-large-ssm-nq,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0440510067467888,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
13941,google/t5-efficient-large-dm256,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0132483355700969,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
13942,google/t5-efficient-small-ff9000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
13943,google/bert_uncased_L-6_H-256_A-4,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0280540433401862,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
13944,google/t5-efficient-large-nl10,Its original repository can be found here,tau/splinter-large-qass,0.0466716177761554,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
13945,mrp/simcse-model-wangchanberta,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0309568690136074,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
13946,google/t5-efficient-large-nl20,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0612398479133844,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
13947,gilparmentier/pokemon_gptj_model,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
13948,google/bigbird-roberta-large,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
13949,hf-internal-testing/tiny-random-rembert,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0173562094569206,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
13950,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-MINI-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl8,0.0998191386461258,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
13951,Palak/microsoft_deberta-base_squad,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0763265937566757,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
13952,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0191071859250466,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
13953,crabz/distil-slovakbert-ner,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0558236166834831,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
13954,rdenadai/BR_BERTo,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0038260060052076,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
13955,ebagdasa/propaganda_positive_bart_large,The main task model is based on facebook/bart-large-xsum and meta-task model (sentiment) is  VictorSanh/roberta-base-finetuned-yelp-polarity,ebagdasa/propaganda_positive_bart_large,1.0,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
13956,mimi/Waynehills-NLP-doogie,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0288634598255157,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
13957,anirudh21/xlnet-base-cased-finetuned-rte,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
13958,patrickvonplaten/unispeech-sat-large-timit-ft,I have made no attempts to censor,bigjoedata/rockbot,0.0379745922982692,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
13959,tartuNLP/gpt-4-est-large,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0716837048530578,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
13960,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0124959032982587,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
13961,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0817426368594169,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
13962,prajjwal1/bert-mini,"They were introduced in the study Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (arxiv), and ported to HF for the study Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics (arXiv)",prajjwal1/bert-mini,1.0,-1.0,NLP,d524d2bf-0c8c-4e35-817b-27b8261b2166___bert-mini,HF
13963,google/t5-efficient-tiny-nh1,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0723465718328952,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
13964,uer/t5-v1_1-small-chinese-cluecorpussmall,This is a sentence-transformers model: It maps sentences and paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search,piotr-rybak/poleval2021-task4-herbert-large-encoder,0.033163346350193,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
13965,Palak/microsoft_deberta-base_squad,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.064704842865467,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
13966,Milos/slovak-gpt-j-162M,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0382937481626868,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
13967,uer/t5-v1_1-small-chinese-cluecorpussmall,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0626602247357367,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
13968,google/t5-efficient-base-nl32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0717027895152568,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
13969,tartuNLP/gpt-4-est-large,The model was finetuned by jweb based on trained by rinna Co,jweb/japanese-soseki-gpt2-1b,0.0414699129760265,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
13970,google/t5-efficient-large-nl12,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
13971,Iskaj/hf-challenge-test,This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset,Iskaj/hf-challenge-test,1.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
13972,facebook/blenderbot-90M,We then discuss the limitations of this work by analyzing failure cases of our models,facebook/blenderbot-90M,1.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
13973,diper1998/distilgpt2-finetuned-AT,"Please check out our EMNLP 2021 paper ""BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation"" for more details",jhu-clsp/bibert-ende,0.0944915488362312,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
13974,hajime9652/xlnet-japanese,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0404937788844108,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
13975,allenai/wmt19-de-en-6-6-big,Final perplexity on test set is 13.6,sberbank-ai/rugpt3large_based_on_gpt2,0.0475920382887124,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
13976,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0969000309705734,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
13977,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
13978,TJMUCH/transcriptome-iseeek,Pretrained model on Turkish language using a masked language modeling (MLM) objective,ctoraman/RoBERTa-TR-medium-wp-66k,0.0841172859072685,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13979,zoha/wav2vec2-base-timit-demo-colab,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0630193278193473,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
13980,THUMT/mGPT,mGPT is a Transformer-based model which pre-trained on massive multilingual data covering over 101 languages,THUMT/mGPT,1.0,-1.0,NLP,337b82e9-8452-490f-be57-2e0059a8854e___mGPT,HF
13981,mwesner/reformer-clm,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0937342122197151,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
13982,clue/roberta_chinese_3L312_clue_tiny,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0310475658625364,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
13983,amazon/bort,Pretrained T5 small language model for Malay,malay-huggingface/t5-small-bahasa-cased,0.0494061708450317,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
13984,hf-internal-testing/tiny-random-rembert,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.071730799973011,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
13985,clue/roberta_chinese_3L312_clue_tiny,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
13986,google/t5-efficient-large-nl36,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0635181944817304,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
13987,SetFit/deberta-v3-large__sst2__train-8-8,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0961955487728118,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
13988,groar/gpt-neo-1.3B-finetuned-escape2,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
13989,google/t5-efficient-small-kv128,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0699431709945201,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
13990,google/bert_uncased_L-8_H-128_A-2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
13991,sberbank-ai/rugpt3large_based_on_gpt2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
13992,ToToKr/kobigbird-bert-base-finetuned-klue,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0582288429141044,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
13993,Cedille/fr-boris,Its original repository can be found here,tau/splinter-large-qass,0.0225504953414201,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
13994,stas/mt5-tiny-random,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0301603339612483,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
13995,aubmindlab/aragpt2-large,These models were trained using the lamb optimizer and follow the same architecture as gpt2 and are fully compatible with the transformers library,aubmindlab/aragpt2-large,1.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
13996,TJMUCH/transcriptome-iseeek,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0289141051471233,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
13997,mimicheng/codeparrot-ds,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.051255278289318,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
13998,allenai/wmt16-en-de-12-1,"For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation",allenai/wmt16-en-de-12-1,1.0,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
13999,google/t5-efficient-large-nl16,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0535036642104386,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
14000,google/t5-efficient-tiny-nl24,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0723049864172935,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
14001,google/t5-efficient-tiny-nh16,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0523587483912705,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
14002,brad1141/Longformer-finetuned-norm,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0269997902214527,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
14003,google/t5-efficient-small-el8,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0046506668440998,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
14004,skplanet/dialog-koelectra-small-generator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
14005,rinna/japanese-gpt2-xsmall,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0676603975395361,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
14006,hfl/rbtl3,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0421478529460728,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
14007,cammy/led-base-16384-100-MDS,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0677157342433929,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
14008,Vilnius-Lithuania-iGEM/Albumin,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0268578386555115,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
14009,khizon/distilbert-unreliable-news-eng-4L,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.077884443104267,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
14010,aditeyabaral/sentencetransformer-bert-hinglish-small,"Ko-GPT-Trinity refers to the class of models, while 1.2B represents the number of parameters of this particular pre-trained model",skt/ko-gpt-trinity-1.2B-v0.5,0.0616704523563385,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
14011,google/t5-efficient-small-dm2000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0505199562758206,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
14012,SetFit/deberta-v3-large__sst2__train-8-8,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
14013,GKLMIP/electra-myanmar-small-uncased,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.072378970682621,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
14014,uer/t5-v1_1-base-chinese-cluecorpussmall,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0403320454061031,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
14015,google/t5-efficient-base-el8,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0914739742875099,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
14016,anirudh21/xlnet-base-cased-finetuned-rte,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
14017,google/bert_uncased_L-10_H-256_A-4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
14018,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0592596977949142,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
14019,google/t5-efficient-mini-nl8,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.004722936078906,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
14020,sonoisa/byt5-small-japanese,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.069777712225914,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
14021,piotr-rybak/poleval2021-task4-herbert-large-encoder,This is a sentence-transformers model: It maps sentences and paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search,piotr-rybak/poleval2021-task4-herbert-large-encoder,1.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14022,TJMUCH/transcriptome-iseeek,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
14023,google/t5-efficient-base-el6,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0659779906272887,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
14024,ebagdasa/propaganda_positive_bart_large,I have made no attempts to censor,bigjoedata/rockbot,0.0104114525020123,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
14025,junnyu/roformer_base_wwm_cluecorpussmall,Pretrained model on 13G Chinese corpus(clue corpus small),junnyu/roformer_base_wwm_cluecorpussmall,1.0,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
14026,microsoft/layoutlm-base-cased,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0859998613595962,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14027,mikaelsouza/msft-regular-model,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0946776941418647,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
14028,stas/pegasus-cnn_dailymail-tiny-random,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0334301022812723,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
14029,1one5ome/gpt2-chinese-gulong,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0431314930319786,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
14030,lgrobol/electra-minuscule-generator,"TandC Summarization Model based on sshleifer/distilbart-cnn-6-6, This abstractive summarization model is a part of a bigger end-to-end TandC summarizer pipeline  which is preceded by LSA (Latent Semantic Analysis) extractive summarization",ml6team/distilbart-tos-summarizer-tosdr,0.0657816771417856,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
14031,Milos/slovak-gpt-j-405M,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0401324952642122,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
14032,tau/tavbert-he,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0525832623243331,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14033,yorko/scibert_scivocab_uncased_long_4096,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0065312951337545,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
14034,kyryl0s/gpt2-uk-xxs,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0298428433015942,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
14035,google/t5-efficient-xxl-nl4,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0565340686589479,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
14036,oskrmiguel/mt5-simplification-spanish,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0254977797158062,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
14037,google/bert_uncased_L-12_H-256_A-4,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0032457672059535,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
14038,microsoft/layoutlm-base-cased,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0037249252200126,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14039,google/t5-efficient-tiny-nh16,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
14040,bigjoedata/rockbot,"The 3.5 billion parameter Wenzhong-GPT2-3.5B large model, using 100G chinese common data, 32 A100 training for 28 hours, is the largest open source GPT2 large model of chinese",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
14041,Milos/slovak-gpt-j-405M,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
14042,nyu-mll/roberta-base-10M-3,The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1,nyu-mll/roberta-base-10M-3,1.0,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
14043,zoha/wav2vec2-base-timit-demo-colab,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0662753880023956,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
14044,DemangeJeremy/4-sentiments-with-flaubert,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14045,SupriyaArun/squeezebert-uncased-finetuned-squad,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0197738632559776,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
14046,google/t5-efficient-base-ff1000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0613429304212331,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
14047,piotr-rybak/poleval2021-task4-herbert-large-encoder,A direct export of the model checkpoint was first made available in this repository,google/rembert,0.0244674496352672,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14048,yorko/scibert_scivocab_uncased_long_4096,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0079916925169527,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
14049,google/t5-efficient-base-nl4,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-nl4,1.0,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
14050,google/t5-efficient-small-nl48,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
14051,amazon/bort,This is the generator model used to sample synthetic text and pretrain the discriminator,jcblaise/electra-tagalog-small-cased-generator,0.0967296548187732,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14052,1one5ome/gpt2-chinese-gulong,This model can generate Gu Long style Wuxia context given a prefix,1one5ome/gpt2-chinese-gulong,1.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
14053,google/t5-efficient-mini,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0190042648464441,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
14054,google/t5-efficient-small-ff1000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0454803835600614,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
14055,google/t5-efficient-base-el6,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0220407154411076,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
14056,ductuan024/AimeLaw,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0159790255129337,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
14057,google/t5-efficient-base-nl40,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0526558589190244,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
14058,VietAI/gpt-neo-1.3B-vietnamese-news,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0273532071150839,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
14059,google/t5-efficient-large-nl12,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0627128183841705,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
14060,google/t5-efficient-small-dm2000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
14061,akdeniz27/deberta-v2-xlarge-cuad,This is a sentence-transformers model: It maps sentences and paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search,piotr-rybak/poleval2021-task4-herbert-large-encoder,0.0615733489394187,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
14062,hf-internal-testing/tiny-random-rembert,Amazon's BORT,amazon/bort,0.0966209173202514,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
14063,ctoraman/RoBERTa-TR-medium-wp-66k,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
14064,google/t5-efficient-small-ff9000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0577164441347121,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
14065,mwesner/reformer-clm,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0809572413563728,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
14066,google/t5-efficient-large-dm512,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.060645492747426,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
14067,aditeyabaral/sentencetransformer-distilbert-hinglish-big,We welcome all experts and scholars to download and use this model,hfl/chinese-xlnet-mid,0.0963687747716903,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
14068,damlab/GO-language,It was trained on a collection of gene-ontology terms from model organisms,damlab/GO-language,1.0,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
14069,google/t5-efficient-small-dm1000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0503833182156085,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
14070,bigjoedata/rockbot,Most language models are imprecise and Rockbot is no exception,bigjoedata/rockbot,1.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
14071,sentence-transformers/paraphrase-MiniLM-L3-v2,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,0.0985321402549743,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
14072,google/t5-efficient-small-ff3000,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0496964510530233,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
14073,Milos/slovak-gpt-j-405M,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0716361496597528,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
14074,nherve/flaubert-oral-asr,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.050002135336399,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
14075,google/t5-efficient-large-nh32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0127612883225082,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
14076,uer/bart-chinese-4-768-cluecorpussmall,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.016621932387352,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
14077,sberbank-ai/rugpt3large_based_on_gpt2,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
14078,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
14079,microsoft/layoutlm-base-cased,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0537516437470912,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14080,thu-coai/LongLM-large,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0091844390456875,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
14081,sentence-transformers/paraphrase-MiniLM-L3-v2,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0652022659778595,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
14082,diper1998/distilgpt2-finetuned-AT,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,0.0931400805711746,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
14083,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl48,0.0071252658963203,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14084,TJMUCH/transcriptome-iseeek,"This is part of a project from which resulted the following models:For more information, please see the accompanying article (See BibTeX entry and citation info below) and the project's github",liaad/srl-pt_xlmr-base,0.0828440859913826,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
14085,google/t5-efficient-large-dm512,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0231945347040891,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
14086,jweb/japanese-soseki-gpt2-1b,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
14087,microsoft/layoutlm-base-uncased,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0620152428746223,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
14088,mwesner/reformer-clm,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0277820453047752,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
14089,google/t5-efficient-small-nl22,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0727184563875198,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
14090,saibo/random-roberta-mini,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0666342200711369,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
14091,mrm8488/bert-small-finetuned-squadv2,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0036338847130537,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
14092,hf-internal-testing/tiny-random-rembert,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.096367672085762,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
14093,google/t5-efficient-large-dm2000,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0946610420942306,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
14094,google/t5-efficient-base-dm1000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0323854312300682,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
14095,cammy/led-base-16384-100-MDS,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
14096,google/t5-efficient-small-el64,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0029156976379454,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
14097,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
14098,jean-paul/kinyaRoberta-small,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,1.0,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
14099,google/t5-efficient-tiny-nh1,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0380032937973737,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
14100,everdoubling/byt5-Korean-small,Hyperparameters used by M-FAC optimizer:We share the best model out of 5 runs with the following score on STS-B validation set:,M-FAC/bert-mini-finetuned-stsb,0.0443301852792501,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
14101,mwesner/reformer-clm,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0975677073001861,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
14102,saibo/random-roberta-tiny,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0338162668049335,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
14103,google/bert_uncased_L-10_H-256_A-4,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-10_H-256_A-4,1.0,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
14104,kyryl0s/gpt2-uk-xxs,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0028297156095504,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
14105,google/t5-efficient-large-kv32,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0344898235052823,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
14106,damlab/GO-language,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0189787211517492,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
14107,google/t5-efficient-base-nh24,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0763987824320792,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
14108,google/t5-efficient-small-nl24,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0276764435693621,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
14109,google/t5-efficient-base-nl24,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.003914151340723,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
14110,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0838262662291526,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
14111,clue/xlnet_chinese_large,"Organization of Language Understanding Evaluation benchmark for Chinese: tasks and datasets, baselines, pre-trained Chinese models, corpus and leaderboard",clue/xlnet_chinese_large,1.0,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
14112,minimaxir/magic-the-gathering,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.030306115746498,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
14113,Recognai/selectra_medium,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
14114,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
14115,google/t5-efficient-large-dm2000,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0413895305246114,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
14116,google/t5-efficient-small-el64,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0492636114358901,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
14117,stas/t5-very-small-random,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
14118,hfl/rbtl3,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0125080924481153,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
14119,google/t5-efficient-base-el6,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
14120,google/t5-efficient-large-el6,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0612677466124295,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
14121,google/t5-efficient-tiny-nh8,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0667092949151992,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
14122,IlyaGusev/rugpt3medium_sum_gazeta,Colab: linkEvaluation script: evaluate,IlyaGusev/rugpt3medium_sum_gazeta,1.0,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
14123,microsoft/layoutlm-base-cased,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0418274849653244,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14124,stas/t5-very-small-random,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0810638293623924,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
14125,nghuyong/ernie-tiny,Details will be available soon,VietAI/gpt-neo-1.3B-vietnamese-news,0.0774865224957466,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
14126,google/t5-efficient-mini,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0308592682704329,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
14127,Milos/slovak-gpt-j-405M,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
14128,Milos/slovak-gpt-j-405M,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
14129,google/t5-efficient-base-nl2,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0047193728387355,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
14130,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Smaller variants, Slovak GPT-J-405M and Slovak GPT-J-162M, are still available",Milos/slovak-gpt-j-1.4B,0.0981546565890312,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14131,google/t5-efficient-base-nl2,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0789142325520514,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
14132,google/t5-efficient-large-dm768,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0402834275737404,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
14133,google/t5-efficient-large-nl12,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0371336601674556,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
14134,google/reformer-crime-and-punishment,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.017436932772398,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
14135,google/t5-efficient-base-ff12000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0472886003553866,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
14136,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0988481119275093,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
14137,rinna/japanese-gpt-1b,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,mrp/simcse-model-wangchanberta,0.0651042299966016,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
14138,ian0delond/model-test,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
14139,google/t5-efficient-base-dm2000,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0869570188224315,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
14140,CuongLD/wav2vec2-large-xlsr-vietnamese,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0891116820275783,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
14141,microsoft/layoutlm-large-uncased,It can be considered as small copy of Conversational DistilRuBERT-base,DeepPavlov/distilrubert-tiny-cased-conversational,0.0654938891530036,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
14142,google/t5-efficient-large-dm128,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0603852057829498,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
14143,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0279808416962623,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
14144,malay-huggingface/xlnet-tiny-bahasa-cased,Its original repository can be found here,tau/splinter-large-qass,0.0329461644093195,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
14145,google/t5-efficient-large-kv16,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0776467248797416,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
14146,ToToKr/kobigbird-bert-base-finetuned-klue,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0286326725035905,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
14147,aliosm/ComVE-gpt2-large,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0623657321557402,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
14148,google/t5-efficient-small-dm128,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0521211707964539,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
14149,google/t5-efficient-xl-nl6,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0687342882156372,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
14150,mimi/Waynehills-NLP-doogie,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0141737237572669,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
14151,google/t5-efficient-large-el6,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0561231207102536,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
14152,amazon/bort,"The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card",minimaxir/magic-the-gathering,0.0701551679521798,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14153,google/t5-efficient-large-kv256,This does not make a very readable text,pere/DeUnCaser,0.0545663144439458,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
14154,google/t5-efficient-large-dm128,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0758465640246868,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
14155,tuner007/pegasus_qa,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0327097214758396,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
14156,IDEA-CCNL/Erlangshen-Longformer-110M,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0342957125976681,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
14157,google/t5-efficient-large-nh2,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
14158,google/t5-efficient-small-el32,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0606168881058692,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
14159,microsoft/layoutlm-large-uncased,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0597820356488227,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
14160,mrm8488/bert-small-finetuned-squadv2,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0251869661733507,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
14161,nherve/flaubert-oral-ft,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
14162,SupriyaArun/squeezebert-uncased-finetuned-squad,"Hebrew poetry text generation model, fined tuned upon hebrew-gpt_neo-tiny which was trained using EleutherAI's gpt-neo",Norod78/hebrew_poetry-gpt_neo-tiny,0.0455635823309421,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
14163,microsoft/layoutlm-base-uncased,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0818773210048675,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
14164,dhpollack/distilbert-dummy-sentiment,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0678515415638684,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
14165,tau/tavbert-he,T5-Efficient-TINY-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh32,0.0269958116114139,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14166,dhpollack/distilbert-dummy-sentiment,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
14167,google/t5-efficient-small-kv16,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
14168,minimaxir/magic-the-gathering,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
14169,Gilles/FongBERT,It is the first pretrained model to leverage transfer learning for downtream tasks for Fon,Gilles/FongBERT,1.0,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
14170,amazon/bort,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14171,groar/gpt-neo-1.3B-finetuned-escape2,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0135020101442933,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
14172,hfl/chinese-electra-180g-small-ex-generator,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.025896710033218,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
14173,google/t5-efficient-tiny-ff6000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
14174,google/t5-efficient-small-dm1000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0644751470535993,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
14175,google/t5-efficient-large-nh8,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
14176,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co,rinna/japanese-gpt2-xsmall,0.073317676782608,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
14177,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Portuguese (Brazil) model for text inference,rdenadai/BR_BERTo,0.0812709555029869,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
14178,everdoubling/byt5-Korean-small,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
14179,stas/mt5-tiny-random,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0748478919267654,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
14180,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0917731150984764,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
14181,mimi/Waynehills-NLP-doogie,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
14182,google/t5-efficient-base-el6,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0689552463591098,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
14183,google/t5-efficient-xxl-nl4,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0168278543278574,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
14184,fnlp/bart-base-chinese,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.018293485045433,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
14185,malay-huggingface/t5-tiny-bahasa-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0144837368279695,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
14186,microsoft/layoutlm-large-uncased,This is a Lonformer version of the SciBERT uncased model by Allen AI,yorko/scibert_scivocab_uncased_long_4096,0.0540755316615104,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
14187,SupriyaArun/squeezebert-uncased-finetuned-squad,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0264645628631114,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
14188,pere/DeUnCaser,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
14189,ductuan024/AimeLaw,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0769857317209243,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
14190,brad1141/Longformer-finetuned-norm,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0986986458301544,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
14191,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
14192,anas-awadalla/bert-tiny-finetuned-squad,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0116187026724219,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
14193,hfl/chinese-electra-small-ex-discriminator,Please use ElectraForPreTraining for discriminator and ElectraForMaskedLM for generator if you are re-training these models,hfl/chinese-electra-small-ex-discriminator,1.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
14194,google/t5-efficient-tiny-ff3000,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0816215500235557,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
14195,amazon/bort,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0324962520971894,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14196,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0770762488245964,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
14197,google/t5-efficient-base-el8,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0651741791516542,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
14198,google/t5-efficient-small-kv256,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0286265108734369,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
14199,google/t5-efficient-large-nh32,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0698109492659568,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
14200,uer/t5-v1_1-small-chinese-cluecorpussmall,"The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0138246817514299,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
14201,google/t5-efficient-small-ff1000,T5-Efficient-SMALL-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff1000,1.0,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
14202,google/t5-efficient-large-el6,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0520093888044356,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
14203,prajjwal1/bert-mini,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0603169780224561,-1.0,NLP,d524d2bf-0c8c-4e35-817b-27b8261b2166___bert-mini,HF
14204,google/t5-efficient-tiny-nh8,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0639891643077134,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
14205,rahulacj/bertweet-base-finetuned-sentiment-analysis,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0815410837531089,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
14206,saibo/random-roberta-tiny,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0059564122930169,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
14207,tuner007/pegasus_qa,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0909695327281951,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
14208,cammy/led-base-16384-100-MDS,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.09914331138134,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
14209,akdeniz27/deberta-v2-xlarge-cuad,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,mrp/simcse-model-wangchanberta,0.0755351036787033,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
14210,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0816110670566558,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
14211,google/t5-efficient-small-kv16,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0450224056839942,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
14212,stas/mt5-tiny-random,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
14213,aubmindlab/aragpt2-large,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
14214,google/t5-efficient-base-ff1000,This does not make a very readable text,pere/DeUnCaser,0.0478086285293101,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
14215,hfl/rbtl3,T5-Efficient-XXL-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xxl-nl4,0.0580602586269378,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
14216,danyaljj/opengpt2_pytorch_forward,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0147091423471768,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
14217,google/t5-efficient-base-ff12000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0627134777605533,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
14218,google/t5-efficient-tiny-ff6000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0632536709308623,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
14219,NeuML/bert-small-cord19,The training data for this dataset is stored as a Kaggle dataset,NeuML/bert-small-cord19,1.0,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
14220,google/t5-efficient-small-kv256,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0278427451848983,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
14221,google/t5-efficient-base-nh8,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0793913342058658,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
14222,mwesner/reformer-clm,T5-Efficient-SMALL-EL64 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el64,0.0776924639940261,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
14223,google/t5-efficient-large-dm768,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0464453697204589,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
14224,google/t5-efficient-small-ff12000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
14225,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
14226,cammy/led-base-16384-100-MDS,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0235848277807235,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
14227,google/t5-efficient-small-nl24,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0671878084540366,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
14228,amazon/bort,This is a fine-tuned mt5-small model for generating simple text from complex text,oskrmiguel/mt5-simplification-spanish,0.006654180586338,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14229,lgrobol/electra-minuscule-generator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0416575744748115,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
14230,piotr-rybak/poleval2021-task4-herbert-large-encoder,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0496255084872245,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14231,SupriyaArun/squeezebert-uncased-finetuned-squad,"It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed",phueb/BabyBERTa-2,0.0661167800426483,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
14232,google/t5-efficient-small-dm2000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0303344503045081,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
14233,piotr-rybak/poleval2021-task4-herbert-large-encoder,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.025841562077403,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14234,google/t5-efficient-base-nh24,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0079845590516925,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
14235,google/t5-efficient-large-nl20,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0613947128877042,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
14236,Norod78/hebrew_poetry-gpt_neo-tiny,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0387263791635631,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
14237,google/t5-efficient-large-el6,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
14238,google/t5-efficient-base-dm2000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
14239,mwesner/reformer-clm,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0928736180067062,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
14240,minimaxir/magic-the-gathering,This does not make a very readable text,pere/DeUnCaser,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
14241,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
14242,hajime9652/xlnet-japanese,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.054069772362709,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
14243,google/t5-efficient-base-el16,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0259938593953848,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
14244,microsoft/layoutlm-base-cased,t5-super-super-tiny-bahasa-cased model was pretrained on multiple tasks,malay-huggingface/t5-super-super-tiny-bahasa-cased,0.0784682780504226,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14245,google/t5-efficient-tiny-nl32,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
14246,ToToKr/kobigbird-bert-base-finetuned-klue,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0699616223573684,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
14247,groar/gpt-neo-1.3B-finetuned-escape2,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.047937836498022,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
14248,malay-huggingface/t5-tiny-bahasa-cased,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0653319979707399,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
14249,google/t5-efficient-mini-nl6,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0009510847739875,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
14250,superspray/electra_large_discriminator_squad2_custom_dataset,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,0.0923370495438575,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
14251,jnz/electra-ka,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0258060470223426,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
14252,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
14253,diper1998/distilgpt2-finetuned-AT,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0380983017385005,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
14254,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0276525486260652,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
14255,mimicheng/codeparrot-ds,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,0.0969619154930114,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
14256,ml6team/distilbart-tos-summarizer-tosdr,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0512068094685673,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
14257,DemangeJeremy/4-sentiments-with-flaubert,"BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences",google/bigbird-roberta-large,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14258,google/reformer-crime-and-punishment,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.061261661350727,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
14259,malay-huggingface/t5-super-tiny-bahasa-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0360653474926948,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
14260,amazon/bort,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0047753527760505,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14261,GKLMIP/electra-myanmar-small-uncased,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
14262,google/t5-efficient-base-dm2000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.00083957798779,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
14263,Milos/slovak-gpt-j-405M,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0116711085041363,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
14264,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0150601686909794,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
14265,VietAI/gpt-neo-1.3B-vietnamese-news,BERT L6_H-512_A-8 model fine-tuned on the CORD-19 dataset,NeuML/bert-small-cord19,0.0668578073382377,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
14266,google/t5-efficient-base-ff6000,This does not make a very readable text,pere/DeUnCaser,0.0689668916165828,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
14267,google/t5-efficient-tiny-nl16,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0696112476289271,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
14268,google/t5-efficient-small-kv16,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0598593726754188,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
14269,google/t5-efficient-large-nl10,This does not make a very readable text,pere/DeUnCaser,0.0588265042752027,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
14270,krevas/finance-koelectra-base-generator,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0294002131558954,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
14271,SetFit/deberta-v3-large__sst2__train-8-8,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
14272,google/t5-efficient-tiny-nl16,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
14273,crabz/distil-slovakbert-ner,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
14274,superspray/electra_large_discriminator_squad2_custom_dataset,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0538845881819725,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
14275,rdenadai/BR_BERTo,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
14276,malay-huggingface/bert-tiny-bahasa-cased,bert-tiny-bahasa-cased model was pretrained on ~1.4 Billion words,malay-huggingface/bert-tiny-bahasa-cased,1.0,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
14277,tau/tavbert-he,T5-Efficient-MINI-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl6,0.0613703187555074,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14278,google/t5-efficient-xl-nl16,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0169108398258686,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
14279,google/t5-efficient-small-kv128,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0688378736376762,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
14280,google/t5-efficient-mini-nl24,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0331431254744529,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
14281,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,Its original repository can be found here,tau/splinter-large-qass,0.0127873190989096,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
14282,google/t5-efficient-tiny-ff6000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0383269693702459,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
14283,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0192751362919807,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
14284,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationYunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu",fnlp/cpt-base,0.0380784943699836,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
14285,google/t5-efficient-base-el16,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
14286,piotr-rybak/poleval2021-task4-herbert-large-encoder,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0979746729135513,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14287,microsoft/layoutlm-base-cased,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0859142318367958,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14288,everdoubling/byt5-Korean-small,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0557375118136405,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
14289,pszemraj/led-large-book-summary,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0122152823023497,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
14290,google/t5-efficient-base-kv128,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0367049742490052,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
14291,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0422602817416191,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
14292,groar/gpt-neo-1.3B-finetuned-escape2,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
14293,google/t5-efficient-large-dm256,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0303445030003785,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
14294,sultan/BioM-ELECTRA-Large-Generator,The impact of design choices on the performance of biomedical language models recently has been a subject for investigation,sultan/BioM-ELECTRA-Large-Generator,1.0,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
14295,google/t5-efficient-mini-nl6,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.048207651823759,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
14296,VietAI/gpt-neo-1.3B-vietnamese-news,The model architecture is custom for production so it only contains 70M parameters,nguyenvulebinh/envibert,0.0476602334529161,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
14297,google/t5-efficient-tiny-nl24,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0029412005096674,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
14298,google/t5-efficient-small-kv128,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
14299,flboehm/reddit-bert-text_5,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
14300,google/t5-efficient-large-nh8-nl32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0175818288698792,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
14301,DemangeJeremy/4-sentiments-with-flaubert,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-768_A-12,0.0733228959143161,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14302,amazon/bort,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14303,suzuki/distilbert-base-uncased-finetuned-squad,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0357126519083976,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
14304,cammy/led-large-16384-arxiv-100-MDS,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
14305,DTAI-KULeuven/robbertje-1-gb-bort,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0664993897080421,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
14306,DeepPavlov/distilrubert-tiny-cased-conversational,Model is based on GPT-J and has over 405M trainable parameters,Milos/slovak-gpt-j-405M,0.0570751416186491,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
14307,piotr-rybak/poleval2021-task4-herbert-large-encoder,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14308,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0300706000998616,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
14309,pszemraj/led-large-book-summary,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0434859488159417,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
14310,Palak/microsoft_deberta-base_squad,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0666299909353256,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
14311,google/t5-efficient-large-kv16,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0133245359174907,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
14312,google/t5-efficient-tiny-ff12000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
14313,stas/mt5-tiny-random,Such configuration may be used for tasks such as automatic summary or question answering,asi/gpt-fr-cased-base,0.0877934023737907,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
14314,ml6team/distilbart-tos-summarizer-tosdr,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0013926064129918,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
14315,google/t5-efficient-base-dm512,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0413714861497282,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
14316,crabz/distil-slovakbert-ner,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.099705085158348,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
14317,allenai/macaw-answer-11b,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0153772893051306,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
14318,google/t5-efficient-small-el64,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.030744694173336,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
14319,cammy/led-large-16384-arxiv-100-MDS,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
14320,abdusahmbzuai/aradia-ctc-distilhubert-ft,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0802486538887023,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
14321,andrejmiscic/simcls-scorer-billsum,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
14322,RabotaRu/HRBert-mini,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,1.0,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
14323,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0993534326553344,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
14324,yorko/scibert_scivocab_uncased_long_4096,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0190325900912284,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
14325,hakurei/gpt-j-random-tinier,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0544677516445517,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
14326,superspray/electra_large_discriminator_squad2_custom_dataset,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0972289368510246,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
14327,tau/tavbert-he,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14328,hf-internal-testing/tiny-random-rembert,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.099699005484581,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
14329,dhpollack/distilbert-dummy-sentiment,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0879389680922031,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
14330,microsoft/layoutlm-large-uncased,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0059353094547986,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
14331,google/t5-efficient-mini-nl12,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0276433080434799,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
14332,mrm8488/xlm-multi-finetuned-xquadv1,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
14333,KBLab/megatron-bert-large-swedish-cased-165k,"The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden",KBLab/megatron-bert-large-swedish-cased-165k,1.0,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
14334,mwesner/reformer-clm,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,0.0721705928444862,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
14335,diper1998/distilgpt2-finetuned-AT,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.066094197332859,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
14336,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt16 dataset,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,1.0,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
14337,google/t5-efficient-tiny-nl12,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0407170169055461,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
14338,google/t5-efficient-small-kv32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0150218401104211,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
14339,flax-community/bengali-t5-base,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0497526593971997,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
14340,google/t5-efficient-base-ff1000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
14341,minimaxir/magic-the-gathering,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0329279713332652,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
14342,zoha/wav2vec2-base-timit-demo-colab,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.093569166958332,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
14343,microsoft/layoutlm-large-uncased,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0734316632151603,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
14344,DemangeJeremy/4-sentiments-with-flaubert,Below are some examples of missing word prediction,Gilles/FongBERT,0.0727196224033832,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14345,khizon/distilbert-unreliable-news-eng-4L,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
14346,fnlp/bart-large-chinese,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0217569917440413,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
14347,sentence-transformers/paraphrase-MiniLM-L3-v2,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0161159746348857,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
14348,google/t5-efficient-large-el4,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0508091682568192,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
14349,google/t5-efficient-tiny-ff12000,This does not make a very readable text,pere/DeUnCaser,0.0875208526849746,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
14350,bigjoedata/rockbot,See SimCLS paper for a description of baselines,andrejmiscic/simcls-scorer-billsum,0.005138788682719,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
14351,google/t5-efficient-large-el2,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0019349753856659,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
14352,beston91/gpt2-xl_ft_logits_10k,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0730154663324356,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
14353,google/t5-efficient-base-nh24,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0301076536998152,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
14354,IDEA-CCNL/Erlangshen-Longformer-330M,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0092465253546834,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
14355,google/t5-efficient-tiny-nl32,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.044809428974986,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
14356,wukevin/tcr-bert,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0622377172112463,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
14357,mimi/Waynehills-NLP-doogie,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
14358,google/t5-efficient-small-el64,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0373055208474397,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
14359,google/t5-efficient-small-dm2000,Its original repository can be found here,tau/splinter-large-qass,0.0500708036124705,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
14360,ErykWdowiak/GPTalian,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0182626904919743,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
14361,google/t5-efficient-small-nl48,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0891187787055969,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
14362,minimaxir/magic-the-gathering,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
14363,hfl/rbt3,T5-Efficient-LARGE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el6,0.0279414067044854,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
14364,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0107116969302296,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
14365,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0492046400904655,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
14366,indobenchmark/indobert-base-p1,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
14367,google/t5-efficient-large-nh12,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.040667307563126,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
14368,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0787145793437957,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
14369,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
14370,google/t5-efficient-large-dm256,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
14371,google/t5-efficient-base-nl36,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0177674014121293,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
14372,gilparmentier/pokemon_gptj_model,For best results use python as below,pszemraj/led-large-book-summary,0.0380053706467151,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
14373,Parsa/BBB_prediction_classification_SMILES,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0025268296400705,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
14374,nghuyong/ernie-1.0,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0300030065700411,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
14375,google/t5-efficient-large-nl12,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0862147249281406,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
14376,IDEA-CCNL/Erlangshen-Longformer-330M,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
14377,google/t5-efficient-large-nh32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0172920040786266,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
14378,superspray/electra_large_discriminator_squad2_custom_dataset,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0459529161453247,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
14379,google/t5-efficient-large-nh8,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0733437724411487,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
14380,monsoon-nlp/dv-muril,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0767022892832755,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
14381,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl36,0.0360742900520563,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14382,facebook/wmt21-dense-24-wide-en-x,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,1.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
14383,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
14384,negfir/distilbert-base-uncased-finetuned-squad,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.0980852097272872,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
14385,cammy/led-base-16384-100-MDS,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.095421627163887,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
14386,mikaelsouza/msft-regular-model,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0455209203064441,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
14387,google/t5-efficient-base-nh24,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
14388,google/t5-efficient-small-ff9000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
14389,piotr-rybak/poleval2021-task4-herbert-large-encoder,This repository provides a 1.3B-parameter finetuned Japanese GPT2 model,jweb/japanese-soseki-gpt2-1b,0.0575347319245338,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14390,aditeyabaral/sentencetransformer-bert-hinglish-small,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0806409269571304,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
14391,ToToKr/kobigbird-bert-base-finetuned-klue,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0266927611082792,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
14392,google/t5-efficient-large-nl10,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0452191336080431,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
14393,google/bert_uncased_L-8_H-256_A-4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0245217963432272,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
14394,google/t5-efficient-small-kv128,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0689025148749351,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
14395,wukevin/tcr-bert,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
14396,google/t5-efficient-base-dm1000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
14397,google/t5-efficient-small-el64,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0337969455868005,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
14398,google/t5-efficient-small-dm2000,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0350159220397472,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
14399,jnz/electra-ka,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0585419610142707,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
14400,google/t5-efficient-small-el4,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0825121104717254,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
14401,aliosm/ComVE-gpt2-large,You can use the raw model for text generation to generate reasons why natural language statements are against commonsense,aliosm/ComVE-gpt2-large,1.0,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
14402,google/t5-efficient-small-nl40,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0532409790903329,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
14403,ctoraman/RoBERTa-TR-medium-wp-66k,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0352625209838151,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
14404,google/t5-efficient-base-nl48,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0238456353545189,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
14405,tau/tavbert-he,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14406,groar/gpt-neo-1.3B-finetuned-escape2,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0631393939256668,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
14407,mimi/Waynehills-NLP-doogie,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0559554547071456,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
14408,google/t5-efficient-base-dm256,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0475683929398655,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
14409,google/t5-efficient-base-dm512,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0451778164133429,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
14410,kssteven/ibert-roberta-large,The best model parameters searched via quantization-aware finetuning can be then exported (e,kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
14411,google/t5-efficient-large-nh4,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0734311006963252,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
14412,stas/pegasus-cnn_dailymail-tiny-random,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
14413,aubmindlab/aragpt2-large,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
14414,groar/gpt-neo-1.3B-finetuned-escape2,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0952974632382392,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
14415,google/t5-efficient-large-kv32,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0573678538203239,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
14416,jhu-clsp/bibert-ende,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.031963496779402,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
14417,DeepPavlov/distilrubert-tiny-cased-conversational,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,1.0,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
14418,google/t5-efficient-tiny-nl32,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0480017289519309,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
14419,crabz/distil-slovakbert-ner,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
14420,razent/SciFive-base-PMC,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
14421,google/t5-efficient-mini,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0073283296078443,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
14422,TJMUCH/transcriptome-iseeek,† ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-162M,0.0939440205693245,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
14423,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0469023411472638,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
14424,hf-internal-testing/tiny-random-rembert,I have made no attempts to censor,bigjoedata/rockbot,0.0594940409064292,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
14425,google/t5-efficient-large-dm256,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
14426,KBLab/megatron-bert-large-swedish-cased-165k,The model has three sister models trained on the same dataset:,KBLab/megatron-bert-large-swedish-cased-165k,1.0,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
14427,thu-coai/LongLM-large,T5-Efficient-BASE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el6,0.0638193022459744,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
14428,google/t5-efficient-large-nh12,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0547993332147597,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
14429,hajime9652/xlnet-japanese,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
14430,allenai/wmt16-en-de-12-1,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.063064731657505,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
14431,google/t5-efficient-small-kv256,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0545318834483623,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
14432,aditeyabaral/sentencetransformer-distilbert-hinglish-big,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0655733495950698,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
14433,nghuyong/ernie-tiny,"Important: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch)",pszemraj/led-large-book-summary,0.0370192417564491,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
14434,google/t5-efficient-large-nh12,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
14435,DemangeJeremy/4-sentiments-with-flaubert,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0409763865172862,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14436,google/t5-efficient-base-nl48,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0785905942320823,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
14437,mimicheng/codeparrot-ds,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0809318944811821,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
14438,Palak/microsoft_deberta-base_squad,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0881057679653167,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
14439,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
14440,minimaxir/magic-the-gathering,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0181858781725168,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
14441,stas/pegasus-cnn_dailymail-tiny-random,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0656214710325002,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
14442,ml6team/distilbart-tos-summarizer-tosdr,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
14443,microsoft/layoutlm-base-uncased,bert-tiny-bahasa-cased model was pretrained on ~1.4 Billion words,malay-huggingface/bert-tiny-bahasa-cased,0.0781691521406173,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
14444,google/t5-efficient-large-nl10,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
14445,beston91/gpt2-xl_ft_logits_10k,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0715695619583129,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
14446,1one5ome/gpt2-chinese-gulong,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
14447,google/t5-efficient-tiny-nl6,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.067801535129547,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
14448,google/t5-efficient-base-nh16,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
14449,google/t5-efficient-base-nl32,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0477037224918603,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
14450,google/t5-efficient-large-el2,This does not make a very readable text,pere/DeUnCaser,0.0517649250105022,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
14451,CuongLD/wav2vec2-large-xlsr-vietnamese,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0875016301870345,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
14452,ian0delond/model-test,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0878639221191406,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
14453,google/t5-efficient-base-nl4,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0704471096396445,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
14454,GKLMIP/electra-myanmar-small-uncased,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0214844932779669,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
14455,allenai/wmt16-en-de-12-1,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-12-1,1.0,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
14456,google/t5-efficient-large-el6,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.027082597836852,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
14457,fnlp/cpt-base,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0053302431479096,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
14458,google/t5-efficient-large-kv256,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0265387129038572,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
14459,jcblaise/distilbert-tagalog-base-cased,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,1.0,-1.0,NLP,29e88f0d-47b4-4733-8b59-3cbe0f4bb77e___distilbert-tagalog-base-cased,HF
14460,mimicheng/codeparrot-ds,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0861463248729705,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
14461,diper1998/distilgpt2-finetuned-AT,Source language: English,NYTK/translation-marianmt-en-hu,0.0685307011008262,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
14462,google/t5-efficient-small-ff1000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
14463,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0624327845871448,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
14464,google/t5-efficient-small-kv32,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0431769089773297,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
14465,google/t5-efficient-large-kv128,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
14466,google/t5-efficient-small-nl24,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0137213766574859,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
14467,diper1998/distilgpt2-finetuned-AT,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
14468,google/t5-efficient-small-dm768,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0660257004201411,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
14469,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0996387973427772,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
14470,aditeyabaral/sentencetransformer-bert-hinglish-small,You can find more information in our paper AraGPT2The code in this repository was used to train all GPT2 variants,aubmindlab/aragpt2-mega,0.066152885556221,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
14471,google/t5-efficient-base-nl24,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0208724662661552,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
14472,negfir/distilbert-base-uncased-finetuned-squad,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0568401217460632,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
14473,pulp/CHILDES-ParentBERTo,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.064473982900381,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
14474,uer/pegasus-base-chinese-cluecorpussmall,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
14475,rinna/japanese-gpt-1b,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0319073237478732,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
14476,crabz/distil-slovakbert-ner,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.023228220641613,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
14477,google/t5-efficient-small-nl40,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.03790401853621,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
14478,dbmdz/bert-medium-historic-multilingual-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
14479,malay-huggingface/t5-super-tiny-bahasa-cased,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
14480,Milos/slovak-gpt-j-1.4B,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0480316509492694,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
14481,google/t5-efficient-small-ff1000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0334457354620098,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
14482,KoichiYasuoka/roberta-base-ukrainian,Its original repository can be found here,tau/splinter-large-qass,0.064776137471199,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
14483,ian0delond/model-test,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0928683578968048,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
14484,cammy/led-large-16384-arxiv-100-MDS,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0816430747509002,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
14485,aubmindlab/aragpt2-mega,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0493362685665488,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
14486,allenai/macaw-answer-11b,Its original repository can be found here,tau/splinter-large-qass,0.0329057872295379,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
14487,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0137655604630708,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
14488,nherve/flaubert-oral-ft,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0716951067248979,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
14489,dbmdz/electra-small-turkish-cased-discriminator,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0163166685961187,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
14490,Milos/slovak-gpt-j-405M,"Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan BaskaranWork done via MLDMM Lab",mldmm/GlassBERTa,0.0745483090480168,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
14491,mrm8488/deberta-v3-small-finetuned-mrpc,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
14492,microsoft/layoutlm-large-uncased,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0993400514125824,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
14493,mgreenbe/bertlet-base-uncased-for-sequence-classification,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
14494,Milos/slovak-gpt-j-162M,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,1.0,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
14495,google/t5-efficient-small-nl48,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0587195642292499,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
14496,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
14497,khizon/distilbert-unreliable-news-eng-4L,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0595580784138291,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
14498,Finnish-NLP/convbert-base-generator-finnish,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
14499,microsoft/layoutlm-large-uncased,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.094200924038887,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
14500,DemangeJeremy/4-sentiments-with-flaubert,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14501,google/t5-efficient-large-dm2000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0715635754168033,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
14502,hf-internal-testing/tiny-random-rembert,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
14503,google/t5-efficient-large-dm256,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0030513480305671,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
14504,hfl/rbt3,T5-Efficient-TINY-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-el8,0.0559875927865504,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
14505,KoichiYasuoka/roberta-base-ukrainian,A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including Strixhaven and Commander 2021,minimaxir/magic-the-gathering,0.0813312903046608,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
14506,google/t5-efficient-xl-nl4,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0493560917675495,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
14507,negfir/distilbert-base-uncased-finetuned-cola,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0338217616081237,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
14508,google/t5-efficient-large-el2,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0783559009432792,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
14509,google/t5-efficient-large-dm512,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0003582146018743,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
14510,GKLMIP/electra-myanmar-small-uncased,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0250935442745685,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
14511,suzuki/distilbert-base-uncased-finetuned-squad,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0463449731469154,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
14512,DeepPavlov/distilrubert-tiny-cased-conversational,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.040489209194978,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
14513,akdeniz27/deberta-v2-xlarge-cuad,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
14514,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0044619869440793,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
14515,Cedille/fr-boris,"We started training from GPT-J, which has been trained on The Pile",Cedille/fr-boris,1.0,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
14516,microsoft/layoutlm-base-cased,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0544680058956146,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14517,google/t5-efficient-base-nl4,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0422442378476261,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
14518,GKLMIP/electra-myanmar-small-uncased,BERT L6_H-512_A-8 model fine-tuned on the CORD-19 dataset,NeuML/bert-small-cord19,0.0394287630915641,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
14519,google/t5-efficient-tiny-nl6,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-nl6,1.0,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
14520,gayanin/bart-mlm-pubmed-medterm,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
14521,zoha/wav2vec2-base-timit-demo-colab,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0724454596638679,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
14522,google/t5-efficient-xl-nl4,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0774236023426055,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
14523,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0366779789328575,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
14524,Milos/slovak-gpt-j-1.4B,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0242949770763516,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
14525,sonoisa/byt5-small-japanese,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
14526,Iskaj/hf-challenge-test,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0461253933608531,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
14527,google/t5-efficient-small-kv256,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0217536389827728,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
14528,google/t5-efficient-base-kv128,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0316764614544808,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
14529,negfir/distilbert-base-uncased-finetuned-cola,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.0856905207037925,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
14530,google/t5-efficient-small-dm768,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0151469269767403,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
14531,SetFit/deberta-v3-large__sst2__train-8-8,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0924378111958503,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
14532,TJMUCH/transcriptome-iseeek,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0272950623184442,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
14533,google/t5-efficient-large-nh8-nl32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0266608987003564,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
14534,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
14535,hfl/rbt3,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0565764363855123,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
14536,DemangeJeremy/4-sentiments-with-flaubert,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0576406512409448,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14537,aditeyabaral/sentencetransformer-distilbert-hinglish-big,GPT2-base and medium uses the code from the gpt2 folder and can trains models from the minimaxir/gpt-2-simple repository,aubmindlab/aragpt2-mega,0.0897828191518783,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
14538,cammy/led-large-16384-arxiv-100-MDS,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0830088555812835,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
14539,google/t5-efficient-tiny-nh32,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
14540,google/t5-efficient-large-dm128,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0308546219021081,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
14541,ncduy/phobert-large-finetuned-vietnamese_students_feedback,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0870235189795494,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
14542,google/t5-efficient-tiny-ff12000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0611014608293771,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
14543,google/t5-efficient-mini-nl12,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
14544,VietAI/gpt-neo-1.3B-vietnamese-news,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,0.031153319403529,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
14545,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl4,0.0867037810385226,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
14546,google/t5-efficient-large-dm128,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
14547,google/t5-efficient-tiny-ff12000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0368001163005828,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
14548,google/t5-efficient-tiny-nl12,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0693805068731307,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
14549,google/t5-efficient-tiny-nh32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0455696675926447,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
14550,hfl/rbt3,T5-Efficient-BASE-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff12000,0.0289437738247215,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
14551,google/t5-efficient-base-dm1000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0140504222363233,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
14552,j-hartmann/purchase-intention-english-roberta-large,"Smaller variants, Slovak GPT-J-405M and Slovak GPT-J-162M, are still available",Milos/slovak-gpt-j-1.4B,0.0668062095840771,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
14553,negfir/distilbert-base-uncased-finetuned-cola,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.09539395570755,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
14554,sultan/BioM-ELECTRA-Large-Generator,This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 434K steps with a batch size of 4096 on TPUv3-512 unit,sultan/BioM-ELECTRA-Large-Generator,1.0,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
14555,google/t5-efficient-base-kv16,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0791781432926654,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
14556,google/t5-efficient-xxl-nl4,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0663622878491878,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
14557,razent/SciFive-base-PMC,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
14558,mimicheng/codeparrot-ds,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0849753096699714,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
14559,google/t5-efficient-mini,T5-Efficient-MINI is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini,1.0,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
14560,google/t5-efficient-small-nl2,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0573249049484729,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
14561,google/t5-efficient-small-nl20,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
14562,hajime9652/xlnet-japanese,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0745043158531189,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
14563,microsoft/layoutlm-base-uncased,A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string,Parsa/BBB_prediction_classification_SMILES,0.0710782930254936,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
14564,cammy/led-large-16384-arxiv-100-MDS,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0386555902659893,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
14565,google/t5-efficient-large-nh12,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0171392671763897,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
14566,google/t5-efficient-large-kv256,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
14567,google/t5-efficient-xl-nl28,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0358082605525851,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
14568,google/t5-efficient-large-nl16,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0837984420359134,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
14569,thu-coai/LongLM-large,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0523156949008503,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
14570,google/reformer-crime-and-punishment,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,1.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
14571,google/t5-efficient-small-el8,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
14572,tartuNLP/gpt-4-est-base,"Not GPT-4 :-) This is the base-size GPT2 model, trained from scratch on 2.2 billion words (Estonian National Corpus + News Crawl + Common Crawl) for 3 epochs",tartuNLP/gpt-4-est-base,1.0,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
14573,google/t5-efficient-xl-nl12,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.074171308428049,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
14574,google/t5-efficient-tiny-nl32,T5-Efficient-TINY-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl32,1.0,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
14575,TransQuest/monotransquest-hter-en_lv-it-nmt,They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content,TransQuest/monotransquest-hter-en_lv-it-nmt,1.0,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
14576,abdusahmbzuai/ft-tatoeba-ar-en,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0040806541219353,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
14577,piotr-rybak/poleval2021-task4-herbert-large-encoder,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0707938596606254,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14578,fnlp/bart-base-chinese,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0707841739058494,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
14579,abdusahmbzuai/ft-tatoeba-ar-en,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0854509770870208,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
14580,tartuNLP/gpt-4-est-large,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
14581,google/t5-efficient-tiny-nh1,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0045177284628153,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
14582,brad1141/Longformer-finetuned-norm,"Important: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch)",pszemraj/led-large-book-summary,0.0914386361837387,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
14583,google/t5-efficient-small-nl22,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
14584,google/t5-efficient-large-dm2000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
14585,stas/pegasus-cnn_dailymail-tiny-random,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0564690027385949,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
14586,google/bert_uncased_L-4_H-128_A-2,I have made no attempts to censor,bigjoedata/rockbot,0.0217506180827816,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
14587,google/t5-efficient-small-nl24,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0215617287904023,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
14588,google/t5-efficient-large-dm512,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0421312898397445,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
14589,bigjoedata/rockbot,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
14590,negfir/distilbert-base-uncased-finetuned-cola,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0161473546177148,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
14591,mmaguero/gn-bert-tiny-cased,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0890384875237941,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
14592,google/t5-efficient-mini-nl12,Its original repository can be found here,tau/splinter-large-qass,0.0604069177061318,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
14593,amazon/bort,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0839958786964416,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14594,mimicheng/codeparrot-ds,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0849409401416778,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
14595,google/t5-efficient-small-ff12000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0108464565128087,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
14596,mnaylor/bigbird-base-mimic-mortality,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,1.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
14597,piotr-rybak/poleval2021-task4-herbert-large-encoder,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0098115056753158,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14598,microsoft/layoutlm-base-cased,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0466160923242569,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14599,IlyaGusev/rugpt3medium_sum_gazeta,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0764195695519447,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
14600,stas/mt5-tiny-random,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
14601,google/t5-efficient-large-nh12,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0058083944022655,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
14602,google/t5-efficient-xl-nl2,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0846503898501395,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
14603,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,aditeyabaral/sentencetransformer-distilbert-hinglish-big,1.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
14604,IDEA-CCNL/Erlangshen-Longformer-330M,This model is cased: it makes a difference between english and English,google/fnet-large,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
14605,aditeyabaral/sentencetransformer-bert-hinglish-small,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0792614519596099,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
14606,allegro/plt5-large,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
14607,DTAI-KULeuven/robbertje-1-gb-bort,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0601401984070737,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
14608,soheeyang/rdr-ctx_encoder-single-trivia-base,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0394742538531621,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
14609,google/t5-efficient-mini-nl24,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
14610,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0310948193073272,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
14611,amazon/bort,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0665452182292938,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14612,lgrobol/electra-minuscule-discriminator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0267236120998859,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
14613,mrm8488/bert-small-finetuned-squadv2,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
14614,ian0delond/model-test,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0753239691257476,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
14615,hfl/chinese-legal-electra-base-generator,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0641145917276541,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
14616,aditeyabaral/sentencetransformer-bert-hinglish-small,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0952469855546951,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
14617,inokufu/flaubert-base-uncased-xnli-sts,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0044513624161482,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
14618,google/t5-efficient-large-el6,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0472977124154566,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
14619,google/t5-efficient-small-dm256,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0561357587575912,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
14620,ductuan024/AimeLaw,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0411183852702379,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
14621,tau/tavbert-he,T5-Efficient-SMALL-EL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el32,0.0443914905190467,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14622,google/t5-efficient-small-dm768,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0320123843848704,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
14623,microsoft/layoutlm-large-uncased,Note: This model does not have a tokenizer as it was pretrained on audio alone,microsoft/unispeech-sat-base,0.0260134786367416,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
14624,antoiloui/belgpt2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
14625,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,0.0923983789980411,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
14626,tau/tavbert-he,T5-Efficient-TINY-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl2,0.0303779751993715,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14627,google/t5-efficient-base-kv16,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.046631893608719,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
14628,microsoft/layoutlm-base-cased,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0580257400870323,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14629,google/t5-efficient-small-dm768,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0555350463837384,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
14630,google/t5-efficient-small-dm1000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.035802774131298,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
14631,razent/SciFive-large-Pubmed,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,1.0,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
14632,nguyenvulebinh/envibert,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0220789822439352,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
14633,minimaxir/magic-the-gathering,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
14634,VietAI/gpt-neo-1.3B-vietnamese-news,"Hebrew poetry text generation model, fined tuned upon hebrew-gpt_neo-tiny which was trained using EleutherAI's gpt-neo",Norod78/hebrew_poetry-gpt_neo-tiny,0.0546417366713284,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
14635,pulp/CHILDES-ParentBERTo,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0759474411606788,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
14636,saibo/random-roberta-tiny,For best results use python as below,pszemraj/led-large-book-summary,0.0613743378780781,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
14637,malay-huggingface/t5-tiny-bahasa-cased,t5-tiny-bahasa-cased model was pretrained on multiple tasks,malay-huggingface/t5-tiny-bahasa-cased,1.0,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
14638,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.055491354316473,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
14639,google/t5-efficient-small-dm128,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0125915631651878,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
14640,hfl/chinese-electra-180g-small-ex-discriminator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
14641,mrm8488/bert-small-finetuned-squadv2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0366155244410037,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
14642,google/t5-efficient-small-el8,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0892081409692764,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
14643,google/t5-efficient-small-nl2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0445864740759133,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
14644,amazon/bort,Pretrained T5 tiny language model for Malay,malay-huggingface/t5-tiny-bahasa-cased,0.0554758962243795,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14645,jorge-henao/gpt2-small-spanish-disco-poetry-wt,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0650276318192482,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
14646,brad1141/Longformer-finetuned-norm,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0949447453022003,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
14647,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0780894309282302,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
14648,google/t5-efficient-tiny-ff6000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0675583146512508,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
14649,Milos/slovak-gpt-j-405M,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,0.0638218422730763,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
14650,uer/t5-v1_1-base-chinese-cluecorpussmall,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0329882903024554,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
14651,superspray/electra_large_discriminator_squad2_custom_dataset,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0736568868160247,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
14652,google/t5-efficient-base-ff1000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
14653,anas-awadalla/bert-tiny-finetuned-squad,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.086739957332611,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
14654,google/t5-efficient-tiny-nh8,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
14655,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl4,0.0207670014351606,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14656,microsoft/layoutlm-base-cased,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 240",eli4s/Bert-L12-h240-A12,0.0911180227994918,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14657,google/t5-efficient-large-el2,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0655133109539747,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
14658,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Source language: English,NYTK/translation-marianmt-en-hu,0.0302678178995847,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
14659,SetFit/deberta-v3-large__sst2__train-8-8,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0951620414853096,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
14660,hfl/rbtl3,T5-Efficient-TINY-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff6000,0.0635102838277816,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
14661,PlanTL-GOB-ES/gpt2-large-bne,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,1.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
14662,google/t5-efficient-small-nl20,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.056710174307227,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
14663,google/t5-efficient-base-nl36,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0474445931613445,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
14664,negfir/distilbert-base-uncased-finetuned-cola,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0072626881301403,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
14665,microsoft/layoutlm-base-cased,Disclaimer: This model card has been written by gchhablani,google/fnet-large,0.0939957126975059,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14666,CuongLD/wav2vec2-large-xlsr-vietnamese,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0027259811758994,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
14667,google/t5-efficient-large-dm512,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0506299566477536,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
14668,razent/SciFive-base-PMC,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
14669,amazon/bort,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0366799216717481,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14670,hfl/chinese-xlnet-mid,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0174689541260401,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
14671,castorini/afriberta_base,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0701825376600026,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
14672,clue/roberta_chinese_3L312_clue_tiny,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0514547731727361,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
14673,beston91/gpt2-xl_ft_logits_10k,This model is a fine-tuned version of gpt2-xl on an unknown dataset,beston91/gpt2-xl_ft_logits_10k,1.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
14674,indobenchmark/indobert-base-p1,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0750651136040687,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
14675,gayanin/bart-mlm-pubmed-medterm,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0812774449586868,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
14676,mimicheng/codeparrot-ds,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0406861528754234,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
14677,lgrobol/electra-minuscule-discriminator,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
14678,google/t5-efficient-tiny-nl6,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0174252851866185,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
14679,google/t5-efficient-small-dm256,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0014240546151995,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
14680,eli4s/Bert-L12-h384-A6,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0298643105973799,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
14681,IDEA-CCNL/Erlangshen-Longformer-110M,The model is case-sensitive,tau/splinter-large-qass,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
14682,clue/xlnet_chinese_large,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
14683,hf-internal-testing/tiny-random-rembert,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.099183976650238,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
14684,google/t5-efficient-tiny-nl16,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0281393397599458,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
14685,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0891591012477874,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
14686,mmaguero/gn-bert-tiny-cased,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0326297134160995,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
14687,google/t5-efficient-small-kv128,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
14688,google/t5-efficient-base-dm1000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0563909262418746,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
14689,microsoft/layoutlm-base-uncased,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.058083638548851,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
14690,tartuNLP/gpt-4-est-base,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0548214763402938,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
14691,krevas/finance-koelectra-base-generator,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.054032003507018,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
14692,microsoft/layoutlm-large-uncased,bert-tiny-bahasa-cased model was pretrained on ~1.4 Billion words,malay-huggingface/bert-tiny-bahasa-cased,0.0781691521406173,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
14693,google/t5-efficient-base-ff9000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.050007877871394,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
14694,SetFit/deberta-v3-large__sst2__train-8-8,Source language: English,NYTK/translation-marianmt-en-hu,0.0522470697760582,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
14695,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0137410483189991,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
14696,mrp/simcse-model-wangchanberta,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.025162317790091,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
14697,jnz/electra-ka,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0576270744204521,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
14698,rahulacj/bertweet-base-finetuned-sentiment-analysis,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0595838874578475,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
14699,google/t5-efficient-base-nl32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0685064271092414,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
14700,IDEA-CCNL/Erlangshen-Longformer-330M,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0343460757285356,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
14701,google/t5-efficient-small-ff9000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0405116146430373,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
14702,clue/xlnet_chinese_large,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0817499049007892,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
14703,google/t5-efficient-tiny-nh8,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0424497816711664,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
14704,google/t5-efficient-small-nl36,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0314717311412095,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
14705,stas/mt5-tiny-random,This is intended to be used as a translation model between PROT-BERT and GO-Language,damlab/GO-language,0.0902388244867324,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
14706,nghuyong/ernie-tiny,"Colab demoFor training data was prepended with a text domain tag, and it should be added as prefix when using the model: &gt;general&lt;, &gt;web&lt;, &gt;news&lt;, &gt;doaj&lt; and &gt;wiki&lt; (standing for general texts, web crawled texts, news, article abstracts and wikipedia texts)",tartuNLP/gpt-4-est-base,0.0783252517382303,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
14707,google/t5-efficient-small-el32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0373890846967697,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
14708,IDEA-CCNL/Erlangshen-Longformer-330M,It was trained on a collection of gene-ontology terms from model organisms,damlab/GO-language,0.0955895595252513,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
14709,tau/splinter-base-qass,Note: This model does contain the pretrained weights for the QASS layer (see paper for details),tau/splinter-base-qass,1.0,-1.0,NLP,f68d3e26-2d96-4b99-a309-34b532e9740f___splinter-base-qass,HF
14710,mnaylor/bigbird-base-mimic-mortality,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
14711,aditeyabaral/sentencetransformer-bert-hinglish-small,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0012179799377918,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
14712,TJMUCH/transcriptome-iseeek,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0172837246209383,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
14713,fnlp/bart-base-chinese,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0321856867522001,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
14714,hfl/rbtl3,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0368039077147841,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
14715,mwesner/reformer-clm,T5-Efficient-TINY-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh16,0.0926880165934562,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
14716,google/t5-efficient-base-nh8,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0202378104440867,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
14717,google/t5-efficient-base-nl40,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0548560228198766,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
14718,google/t5-efficient-tiny-nh32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0047077815979719,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
14719,google/t5-efficient-small-nl40,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0391103234142065,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
14720,google/t5-efficient-base-kv32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-kv32,1.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
14721,google/t5-efficient-large-nl32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0590971838682889,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
14722,brad1141/Longformer-finetuned-norm,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0693762972950935,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
14723,UWB-AIR/MQDD-duplicates,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0599134980390468,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
14724,microsoft/layoutlm-base-cased,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0015662349760532,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14725,hajime9652/xlnet-japanese,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0643104910850524,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
14726,tau/tavbert-he,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14727,google/t5-efficient-small-nl2,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.069065112620592,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
14728,google/t5-efficient-large-kv256,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0067206732928753,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
14729,facebook/wmt21-dense-24-wide-en-x,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0269185986369848,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
14730,google/t5-efficient-base-nl32,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0854611545801162,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
14731,bigjoedata/rockbot,This model used the pre-trained weights of distilbert-base-cased as starting point (only 4 layers) and was able to achieve 84% accuracy on the test set,khizon/distilbert-unreliable-news-eng-4L,0.0138296391814947,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
14732,aditeyabaral/sentencetransformer-bert-hinglish-small,TransQuest outperforms current open-source quality estimation frameworks such as OpenKiwi and DeepQuest,TransQuest/monotransquest-hter-en_lv-it-nmt,0.094585046172142,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
14733,flboehm/reddit-bert-text_5,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
14734,google/t5-efficient-large-el2,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0634129270911216,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
14735,google/reformer-crime-and-punishment,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0332273915410041,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
14736,hfl/rbt3,T5-Efficient-LARGE-NH2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh2,0.0317381834611296,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
14737,google/t5-efficient-base-kv256,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0274547506123781,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
14738,google/t5-large-ssm-nq,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
14739,mwesner/reformer-clm,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0410038009285926,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
14740,zoha/wav2vec2-base-timit-demo-colab,You can use BelGPT-2 with 🤗 transformers:Below is the list of all French copora used to pre-trained the model:,antoiloui/belgpt2,0.0628306418657302,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
14741,tucan9389/kcbert-base-finetuned-squad,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0951903015375137,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
14742,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0595573708415031,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
14743,saibo/random-roberta-mini,"We introduce random-roberta-mini, which is a unpretrained version of a mini RoBERTa model(4 layer and 256 heads)",saibo/random-roberta-mini,1.0,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
14744,kyryl0s/gpt2-uk-xxs,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0030515491962433,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
14745,google/t5-efficient-base-dm2000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
14746,uer/pegasus-base-chinese-cluecorpussmall,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0554744899272918,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
14747,google/t5-efficient-small-el32,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0516099911183118,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
14748,google/t5-efficient-large-nl12,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0625795386731624,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
14749,google/t5-efficient-small-ff12000,This does not make a very readable text,pere/DeUnCaser,0.0753248296678065,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
14750,google/t5-efficient-large-el4,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0973413884639739,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
14751,google/t5-efficient-base-kv16,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0020830174908041,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
14752,google/t5-efficient-base-ff1000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0531506855040787,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
14753,google/t5-efficient-tiny-ff6000,Its original repository can be found here,tau/splinter-large-qass,0.0319539103657006,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
14754,Iskaj/hf-challenge-test,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0267817396670579,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
14755,rmihaylov/pegasus-base-cnn-dailymail-bg,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0369714326225221,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
14756,google/t5-efficient-large-nl32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0454474501311778,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
14757,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0736103057861328,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
14758,cross-encoder/ms-marco-MiniLM-L-4-v2,net Training MS MarcoThe usage becomes easier when you have SentenceTransformers installed,cross-encoder/ms-marco-MiniLM-L-4-v2,1.0,-1.0,NLP,b57a156c-f487-4efc-a83f-37bedaa63a0a___ms-marco-MiniLM-L-4-v2,HF
14759,hfl/chinese-electra-180g-small-ex-generator,This does not make a very readable text,pere/DeUnCaser,0.0353077209244171,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
14760,readerbench/RoBERT-small,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,af9865c1-d52f-4c4d-8696-c19b9085823b___RoBERT-small,HF
14761,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0900902971625328,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
14762,google/t5-efficient-large-dm768,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0711756013333797,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
14763,IDEA-CCNL/Erlangshen-Longformer-110M,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0731864720582961,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
14764,google/t5-efficient-base-dm256,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0515159014612436,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
14765,google/t5-efficient-large-kv32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
14766,google/t5-efficient-mini-nl8,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0162154082208871,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
14767,negfir/distilbert-base-uncased-finetuned-squad,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0019106138497591,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
14768,google/t5-efficient-xl-nl2,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0139889884740113,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
14769,google/t5-efficient-small-nl2,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0679923072457313,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
14770,google/t5-efficient-large-nh8,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0445597534999251,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
14771,ai4bharat/MultiIndicHeadlineGeneration,"Some salient features of the MultiIndicHeadlineGeneration are:If you wish to use any language written in a non-Devanagari script, then you should first convert it to Devanagari using the Indic NLP Library",ai4bharat/MultiIndicHeadlineGeneration,1.0,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
14772,google/bert_uncased_L-10_H-256_A-4,"This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking)",google/bert_uncased_L-10_H-256_A-4,1.0,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
14773,amazon/bort,Tokenization algorithm is WordPiece,ctoraman/RoBERTa-TR-medium-wp-66k,0.0509984306991099,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14774,rahulacj/bertweet-base-finetuned-sentiment-analysis,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0110820811241865,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
14775,Cedille/fr-boris,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0193946190799275,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
14776,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0306848939508199,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
14777,google/t5-efficient-large-nl20,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.067822616547346,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
14778,nghuyong/ernie-1.0,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0286770891398191,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
14779,google/t5-efficient-tiny-nl24,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
14780,DemangeJeremy/4-sentiments-with-flaubert,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0278381071984767,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14781,hfl/chinese-legal-electra-base-generator,"Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants",hfl/chinese-legal-electra-base-generator,1.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
14782,stas/pegasus-cnn_dailymail-tiny-random,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0664433687925338,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
14783,google/t5-efficient-xl-nl2,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0631086705252527,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
14784,pulp/CHILDES-ParentBERTo,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0610220413655041,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
14785,VietAI/gpt-neo-1.3B-vietnamese-news,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0349304703995585,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
14786,cosmoquester/bart-ko-base,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0497457782427469,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
14787,tuner007/pegasus_qa,Pegasus model fine-tuned for QA using text-to-text approachCreated by Arpit Rajauria,tuner007/pegasus_qa,1.0,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
14788,castorini/afriberta_large,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
14789,IDEA-CCNL/Erlangshen-Longformer-110M,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0153166316449641,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
14790,google/t5-efficient-small-el16-dl4,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0361686144024133,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
14791,yarongef/DistilProtBert,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0563690283646186,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
14792,google/t5-efficient-base-el6,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0569618754088878,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
14793,minimaxir/magic-the-gathering,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
14794,google/bert_uncased_L-8_H-768_A-12,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0110978856682777,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
14795,google/t5-efficient-small-ff1000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0662554278969764,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
14796,IDEA-CCNL/Erlangshen-Longformer-330M,"enwik8 is a dataset based on Wikipedia and is often used to measure the model's ability to compress data, e",google/reformer-enwik8,0.061867469921708,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
14797,google/t5-efficient-base-dm2000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0371539089828729,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
14798,amazon/bort,The entire spaCy v3 model is distributed as a python package named ja_ginza_electra from PyPI along with GiNZA v5 which provides some custom pipeline components to recognize the Japanese bunsetu-phrase structures,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,0.0790905393660068,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
14799,flax-community/gpt2-medium-persian,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0568536259233951,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
14800,microsoft/layoutlm-base-cased,A ridiculously small ELECTRA generator model for testing purposes,lgrobol/electra-minuscule-generator,0.0969329848885536,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14801,DemangeJeremy/4-sentiments-with-flaubert,DistilWav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the XLS-R architecture,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.041499026119709,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14802,IDEA-CCNL/Erlangshen-Longformer-330M,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.000558597035706,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
14803,google/t5-efficient-large-dm512,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0503686740994453,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
14804,malay-huggingface/xlnet-base-bahasa-cased,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0440209104369083,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
14805,google/t5-efficient-mini-nl8,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.077742077410221,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
14806,skt/ko-gpt-trinity-1.2B-v0.5,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
14807,google/t5-efficient-large-kv32,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0882616527378558,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
14808,google/t5-efficient-small-kv256,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0102844899520278,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
14809,google/t5-efficient-small-dm256,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
14810,malay-huggingface/t5-super-super-tiny-bahasa-cased,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0169101015975078,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
14811,TJMUCH/transcriptome-iseeek,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0422663390636444,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
14812,google/t5-efficient-tiny-ff6000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0109865181148052,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
14813,stas/pegasus-cnn_dailymail-tiny-random,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.0291315587237477,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
14814,google/t5-efficient-large-nl32,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0371540645137429,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
14815,fnlp/bart-large-chinese,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0134976264089346,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
14816,castorini/afriberta_small,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0709321144968271,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
14817,tau/tavbert-he,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14818,google/t5-efficient-large-el2,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0653900057077407,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
14819,uer/t5-v1_1-small-chinese-cluecorpussmall,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0550989359617233,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
14820,google/t5-efficient-small-ff12000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0485534891486167,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
14821,razent/SciFive-base-PMC,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-8_H-768_A-12,0.0858411416411399,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
14822,tau/tavbert-he,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14823,kyryl0s/gpt2-uk-xxs,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
14824,google/t5-efficient-large-nl32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
14825,google/t5-efficient-base-dm2000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
14826,DemangeJeremy/4-sentiments-with-flaubert,"The final training corpus has a size of 51GB and consists of 8,035,986,369 tokens",dbmdz/convbert-base-german-europeana-cased,0.0633650794625282,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14827,1one5ome/gpt2-chinese-gulong,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
14828,microsoft/layoutlm-base-cased,It can be considered as tiny copy of Conversational DistilRuBERT-small,DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
14829,jweb/japanese-soseki-gpt2-1b,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0227566373844941,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
14830,cammy/led-large-16384-arxiv-100-MDS,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0877589583396911,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
14831,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0445099547505378,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
14832,google/t5-efficient-large-kv128,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0459477650001644,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
14833,sentence-transformers/paraphrase-MiniLM-L3-v2,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0988481119275093,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
14834,aubmindlab/aragpt2-large,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0125491651706397,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
14835,suzuki/distilbert-base-uncased-finetuned-squad,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
14836,castorini/afriberta_small,AfriBERTa small is a pretrained multilingual language model with around 97 million parameters,castorini/afriberta_small,1.0,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
14837,DemangeJeremy/4-sentiments-with-flaubert,GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX,gilparmentier/pokemon_gptj_model,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14838,cammy/led-large-16384-arxiv-100-MDS,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0689477548003196,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
14839,tau/tavbert-he,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14840,phueb/BabyBERTa-2,The three provided models are randomly selected from 10 that were trained and reported in the paper,phueb/BabyBERTa-2,1.0,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
14841,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
14842,skt/ko-gpt-trinity-1.2B-v0.5,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0215214379131793,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
14843,stas/t5-very-small-random,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0835506469011306,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
14844,google/t5-efficient-tiny-ff9000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0580202843993902,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
14845,google/t5-efficient-tiny-nh8,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0688240341842174,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
14846,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0768968686461448,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
14847,google/t5-efficient-base-el8,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.078404601663351,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
14848,google/t5-efficient-large-dm128,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
14849,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0056708827614784,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
14850,google/t5-efficient-large-dm512,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0735513232648372,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
14851,negfir/distilbert-base-uncased-finetuned-cola,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0936187207698822,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
14852,lgrobol/electra-minuscule-discriminator,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0655855238437652,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
14853,google/t5-efficient-large-kv32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0457323305308818,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
14854,GKLMIP/electra-myanmar-small-uncased,The conversion to Longformer was performed with a tutorial by Allen AI: see a Google Colab Notebook by Yury which closely follows the tutorial,yorko/scibert_scivocab_uncased_long_4096,0.0464743934571742,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
14855,KBLab/megatron-bert-base-swedish-cased-600k,This BERT model was trained using the Megatron-LM library,KBLab/megatron-bert-base-swedish-cased-600k,1.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
14856,aditeyabaral/sentencetransformer-bert-hinglish-small,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0922518074512481,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
14857,cammy/led-large-16384-arxiv-100-MDS,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0322973616421222,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
14858,hfl/rbt3,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0604064334183931,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
14859,SupriyaArun/squeezebert-uncased-finetuned-squad,"To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus",FremyCompany/xls-r-nl-v1-cv8-lm,0.0923777967691421,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
14860,kyryl0s/gpt2-uk-xxs,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0812582820653915,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
14861,clue/roberta_chinese_3L312_clue_tiny,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0521905180066823,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
14862,google/t5-efficient-xl-nl8,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0522799491882323,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
14863,everdoubling/byt5-Korean-small,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0374680161476135,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
14864,google/t5-efficient-tiny-nl16,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0597301227971911,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
14865,hfl/rbtl3,T5-Efficient-TINY-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl2,0.0694342367351054,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
14866,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,0.0807361304759979,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
14867,malay-huggingface/t5-small-bahasa-cased,Pretrained T5 small language model for Malay,malay-huggingface/t5-small-bahasa-cased,1.0,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
14868,google/t5-efficient-tiny-nl2,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0371738858520984,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
14869,superb/hubert-base-superb-er,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
14870,google/t5-efficient-large-kv128,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0587812364101409,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
14871,google/t5-efficient-tiny-nl16,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0572349242866039,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
14872,jean-paul/kinyaRoberta-small,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,1.0,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
14873,google/t5-efficient-large-dm512,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0153273353353142,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
14874,google/t5-efficient-large-kv256,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
14875,DeepPavlov/distilrubert-tiny-cased-conversational,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0471840252478917,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
14876,tuner007/pegasus_qa,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
14877,mgreenbe/bertlet-base-uncased-for-sequence-classification,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0651995241641998,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
14878,google/t5-efficient-base-el16,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0377724193967878,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
14879,rahulacj/bertweet-base-finetuned-sentiment-analysis,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0326401516795158,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
14880,allenai/wmt19-de-en-6-6-big,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
14881,saurkulsh/T0pp,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0404632453407559,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
14882,aditeyabaral/sentencetransformer-bert-hinglish-small,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0807666033506393,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
14883,google/t5-efficient-base-dm2000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0382642191834747,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
14884,DemangeJeremy/4-sentiments-with-flaubert,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0193811524659394,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14885,google/t5-efficient-large-dm512,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.029911583289504,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
14886,hfl/rbt3,T5-Efficient-LARGE-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el4,0.0107163954526185,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
14887,google/t5-efficient-small-kv256,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0525793135166167,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
14888,aubmindlab/aragpt2-large,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
14889,mgreenbe/bertlet-base-uncased-for-sequence-classification,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0235775038599967,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
14890,google/t5-efficient-tiny-ff9000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0697677135467528,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
14891,google/t5-efficient-base-kv256,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
14892,google/t5-efficient-large-nh2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0563402753323316,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
14893,google/t5-efficient-small-nl40,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0847881138324737,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
14894,wukevin/tcr-bert,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0735590606927871,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
14895,akdeniz27/deberta-v2-xlarge-cuad,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0334633514285087,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
14896,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0166826229542493,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
14897,obss/mt5-base-3task-highlight-tquad2,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0657638721168041,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
14898,google/t5-efficient-small-kv128,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0540557634085416,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
14899,mrm8488/mobilebert-uncased-finetuned-squadv2,Portuguese (Brazil) model for text inference,rdenadai/BR_BERTo,0.0693474486470222,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
14900,groar/gpt-neo-1.3B-finetuned-escape2,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
14901,google/t5-efficient-base-ff1000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0033284146338701,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
14902,everdoubling/byt5-Korean-small,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0103934668004513,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
14903,DemangeJeremy/4-sentiments-with-flaubert,Each was trained on a TPUv3-8 which was made avilable to me via the TPU Research Cloud Program,Norod78/hebrew_poetry-gpt_neo-tiny,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14904,google/t5-efficient-base-el16,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0480547044426202,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
14905,negfir/distilbert-base-uncased-finetuned-squad,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0527873411774635,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
14906,TJMUCH/transcriptome-iseeek,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
14907,google/t5-efficient-xl-nl6,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0179658979177474,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
14908,ductuan024/AimeLaw,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0164547404274344,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
14909,google/t5-efficient-large-nl20,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0491211768239736,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
14910,google/t5-efficient-large-nl36,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0282058594748377,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
14911,google/t5-efficient-base-nl24,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0755430031567811,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
14912,google/t5-efficient-base-kv256,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0146033372730016,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
14913,piotr-rybak/poleval2021-task4-herbert-large-encoder,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.080341100692749,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14914,Recognai/selectra_medium,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.042905799113214,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
14915,IDEA-CCNL/Erlangshen-Longformer-330M,Any bias held by the imdb dataset may be reflected in the model's output,lannelin/bert-imdb-1hidden,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
14916,TransQuest/monotransquest-hter-en_lv-it-nmt,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
14917,google/t5-efficient-xxl-nl4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
14918,google/t5-efficient-tiny-el8,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.021579597145319,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
14919,hf-internal-testing/tiny-random-rembert,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0368578284978866,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
14920,uer/t5-v1_1-base-chinese-cluecorpussmall,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0341541413217782,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
14921,jnz/electra-ka,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0386026464402675,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
14922,lgrobol/electra-minuscule-discriminator,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0555545054376125,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
14923,google/t5-efficient-base-kv256,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0507765021175145,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
14924,SupriyaArun/squeezebert-uncased-finetuned-squad,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0212515741586685,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
14925,IDEA-CCNL/Erlangshen-Longformer-330M,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0381541913375258,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
14926,groar/gpt-neo-1.3B-finetuned-escape2,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0760345309972763,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
14927,soheeyang/rdr-ctx_encoder-single-trivia-base,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0471471453395982,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
14928,VietAI/gpt-neo-1.3B-vietnamese-news,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0753000788390635,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
14929,DeepPavlov/distilrubert-tiny-cased-conversational,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
14930,malay-huggingface/t5-super-tiny-bahasa-cased,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0896298140287399,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
14931,google/t5-efficient-tiny-nl32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0384629890322684,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
14932,TJMUCH/transcriptome-iseeek,Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective,aliosm/ComVE-gpt2-large,0.0989009365439415,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
14933,negfir/distilbert-base-uncased-finetuned-cola,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0528211593627929,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
14934,IDEA-CCNL/Erlangshen-MegatronBert-1.3B,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0243742940947413,-1.0,NLP,3c1adb6c-e774-450e-967f-c26c07931f49___Erlangshen-MegatronBert-1.3B,HF
14935,google/t5-efficient-mini-nl6,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0567118003964423,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
14936,google/t5-efficient-large-nh12,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0277612302452325,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
14937,piotr-rybak/poleval2021-task4-herbert-large-encoder,mdThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-tiny,0.0267539173364639,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
14938,fnlp/bart-large-chinese,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0725008212029933,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
14939,eli4s/Bert-L12-h256-A4,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
14940,rahulacj/bertweet-base-finetuned-sentiment-analysis,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0821407586336135,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
14941,SetFit/deberta-v3-large__sst2__train-8-8,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0931886285543441,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
14942,allenai/wmt16-en-de-12-1,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
14943,jnz/electra-ka,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0556775778532028,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
14944,GKLMIP/electra-myanmar-small-uncased,"This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences",Rostlab/prot_bert,0.050255995709449,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
14945,google/t5-efficient-base-kv16,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0313615556806325,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
14946,superspray/electra_large_discriminator_squad2_custom_dataset,It can be considered as small copy of Conversational DistilRuBERT-base,DeepPavlov/distilrubert-tiny-cased-conversational,0.0844778567552566,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
14947,google/t5-efficient-tiny-ff3000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
14948,mrm8488/deberta-v3-small-finetuned-mrpc,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0381552465260028,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
14949,dbmdz/electra-small-turkish-cased-discriminator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
14950,google/t5-efficient-large-nl20,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
14951,google/t5-efficient-xl-nl12,T5-Efficient-XL-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl12,1.0,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
14952,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,This model is cased: it does make a difference between bulgarian and Bulgarian,rmihaylov/bert-base-pos-theseus-bg,0.089618980884552,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
14953,rmihaylov/pegasus-base-cnn-dailymail-bg,"The training data is private Bulgarian text from CNN, DailyMail articles",rmihaylov/pegasus-base-cnn-dailymail-bg,1.0,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
14954,hf-internal-testing/tiny-random-rembert,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
14955,negfir/distilbert-base-uncased-finetuned-squad,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0996955633163452,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
14956,saibo/random-roberta-tiny,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0209611444734037,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
14957,mwesner/reformer-clm,T5-Efficient-SMALL-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv128,0.0757782310247421,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
14958,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0932381898164749,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
14959,microsoft/layoutlm-base-uncased,It was introduced in this paper and first released on this page,THUMT/mGPT,0.025271413847804,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
14960,mwesner/reformer-clm,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0974298641085624,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
14961,abdusahmbzuai/aradia-ctc-distilhubert-ft,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0574260838329792,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
14962,google/t5-efficient-xl-nl6,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0772984325885772,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
14963,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.078538954257965,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
14964,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
14965,minimaxir/magic-the-gathering,You can use the raw model for text generation to generate reasons why natural language statements are against commonsense,aliosm/ComVE-gpt2-large,0.0395573368296026,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
14966,google/t5-efficient-small-dm1000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0463973805308341,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
14967,google/t5-efficient-xl-nl4,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0821280181407928,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
14968,NYTK/translation-marianmt-en-hu,This repository contains our translation model (en-hu) which were published in MSZNY 2022 conference,NYTK/translation-marianmt-en-hu,1.0,-1.0,NLP,04ab8b45-5473-4cfe-a2de-c945b14ed956___translation-marianmt-en-hu,HF
14969,google/t5-efficient-xl-nl4,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0722770988941192,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
14970,google/t5-efficient-xl-nl2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0776015147566794,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
14971,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl2,0.0064914524555206,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
14972,eli4s/Bert-L12-h240-A12,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h240-A12,1.0,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
14973,google/t5-efficient-base-nl32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0378339383751153,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
14974,google/t5-efficient-large-dm2000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0396757051348686,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
14975,dbmdz/convbert-base-german-europeana-cased,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0573876360431312,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
14976,tau/tavbert-he,T5-Efficient-SMALL-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff12000,0.0342231150716543,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
14977,nherve/flaubert-oral-ft,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0609833020716905,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
14978,google/t5-efficient-small-el12,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.037010010331869,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
14979,readerbench/RoBERT-small,It was introduced in this paper,readerbench/RoBERT-small,1.0,-1.0,NLP,af9865c1-d52f-4c4d-8696-c19b9085823b___RoBERT-small,HF
14980,google/t5-efficient-large-nl12,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0531594343483447,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
14981,FremyCompany/xls-r-nl-v1-cv8-lm,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0404485135028759,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
14982,google/t5-efficient-tiny-nl32,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.055257617495954,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
14983,google/t5-efficient-large-kv128,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.076867751777172,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
14984,diper1998/distilgpt2-finetuned-AT,"This is one of the smaller pre-trained BERT variants, together with bert-mini bert-small and bert-medium",prajjwal1/bert-tiny,0.0955548211932182,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
14985,google/t5-efficient-small-dm256,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0107886902987957,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
14986,google/t5-efficient-base-dm1000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.011100310832262,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
14987,google/t5-efficient-small-kv32,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0719801224768161,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
14988,sberbank-ai/rugpt3large_based_on_gpt2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
14989,kssteven/ibert-roberta-large,", MatMul, GELU, Softmax, and LayerNorm) with closely approximating integer operations",kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
14990,DTAI-KULeuven/robbertje-1-gb-bort,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
14991,google/t5-efficient-small-kv32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.003027867525816,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
14992,Iskaj/hf-challenge-test,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
14993,wukevin/tcr-bert,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0457006762735545,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
14994,google/t5-efficient-small-kv32,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
14995,DTAI-KULeuven/robbertje-1-gb-bort,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0457985401153564,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
14996,sentence-transformers/paraphrase-MiniLM-L3-v2,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
14997,google/t5-efficient-base-nl2,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0683159697800874,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
14998,aditeyabaral/sentencetransformer-bert-hinglish-small,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
14999,jhu-clsp/bibert-ende,Its original repository can be found here,tau/splinter-large-qass,0.0635346546769141,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
15000,saibo/random-roberta-tiny,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0721979700028895,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
15001,SetFit/deberta-v3-large__sst2__train-8-8,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0665331333875656,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
15002,nielsr/coref-roberta-base,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0445926699321716,-1.0,NLP,8473d81a-bfd8-4703-b569-a14970be6a42___coref-roberta-base,HF
15003,google/t5-efficient-large-nh8-nl32,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0353030422702432,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
15004,hfl/rbtl3,T5-Efficient-LARGE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh32,0.0681560523808002,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
15005,pszemraj/led-large-book-summary,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
15006,google/t5-efficient-small-el16-dl4,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0673118792474269,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
15007,IDEA-CCNL/Erlangshen-Longformer-330M,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
15008,FremyCompany/xls-r-nl-v1-cv8-lm,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
15009,kyryl0s/gpt2-uk-xxs,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0186805995181203,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
15010,google/t5-efficient-mini,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0731884017586708,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
15011,google/t5-efficient-large-dm2000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0383783504366874,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
15012,microsoft/layoutlm-large-uncased,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0818773210048675,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
15013,DTAI-KULeuven/robbertje-1-gb-bort,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
15014,dhpollack/distilbert-dummy-sentiment,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0206941068172454,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
15015,google/t5-efficient-large-nl12,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nl12,1.0,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
15016,amazon/bort,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0144643513485788,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15017,google/t5-efficient-mini-nl8,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
15018,uer/bart-chinese-4-768-cluecorpussmall,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
15019,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0132054931351116,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
15020,google/t5-efficient-mini-nl24,This does not make a very readable text,pere/DeUnCaser,0.0583379846066236,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
15021,google/t5-efficient-base-nl32,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0319237932562827,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
15022,rinna/japanese-gpt2-xsmall,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0550333590557177,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
15023,negfir/distilbert-base-uncased-finetuned-squad,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0850351750850677,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
15024,amazon/bort,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0476840138435363,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15025,minimaxir/magic-the-gathering,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0388968074694275,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
15026,tobiaslee/bert-2l-768h-uncased,"However, this model outperforms the base model by large margins",tau/splinter-large-qass,0.0112203015014528,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
15027,google/t5-efficient-tiny-ff12000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0068111326545476,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
15028,microsoft/deberta-v2-xxlarge-mnli,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0019741477444768,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
15029,google/t5-efficient-tiny-nh32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
15030,google/t5-efficient-large-nl16,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0560861602425574,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
15031,SupriyaArun/squeezebert-uncased-finetuned-squad,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.043040782213211,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
15032,google/t5-efficient-mini-nl6,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
15033,TJMUCH/transcriptome-iseeek,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0384180024266243,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15034,google/t5-efficient-xl-nl16,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0484757274389267,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
15035,google/t5-efficient-base-nl2,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0674400553107261,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
15036,google/bert_uncased_L-8_H-256_A-4,Its original repository can be found here,tau/splinter-large-qass,0.0176675698409477,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
15037,mwesner/bart-mlm,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0379132181406021,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
15038,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh8,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15039,mimicheng/codeparrot-ds,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0405325144529342,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
15040,DTAI-KULeuven/robbertje-1-gb-bort,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0514223972956339,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
15041,zoha/wav2vec2-base-timit-demo-colab,It was introduced in this paper,readerbench/RoBERT-small,0.0289062317460775,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
15042,google/byt5-xl,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0071240440011024,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
15043,google/t5-efficient-base-ff9000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0245827585458755,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15044,cross-encoder/ms-marco-MiniLM-L-2-v2,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0095829106867313,-1.0,NLP,c2d1bb44-2e13-4b2e-8b5d-23fa4c9a88e9___ms-marco-MiniLM-L-2-v2,HF
15045,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0591510608792305,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
15046,google/t5-efficient-small-el32,Its original repository can be found here,tau/splinter-large-qass,0.0499535556882619,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
15047,THUMT/mGPT,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0350802049506455,-1.0,NLP,337b82e9-8452-490f-be57-2e0059a8854e___mGPT,HF
15048,mimi/Waynehills-NLP-doogie,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0149170476943254,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
15049,kyryl0s/gpt2-uk-xxs,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0668540280312299,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
15050,cammy/led-base-16384-100-MDS,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0390110723674297,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
15051,flaubert/flaubert_small_cased,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,1.0,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
15052,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0982197299599647,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
15053,google/t5-efficient-base-el16,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0507801920175551,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
15054,PlanTL-GOB-ES/gpt2-large-bne,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,1.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
15055,diper1998/distilgpt2-finetuned-AT,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
15056,google/t5-efficient-base-nl48,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0451969290152191,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
15057,google/t5-efficient-tiny-ff12000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
15058,google/t5-efficient-xxl-nl4,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0525494720786809,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
15059,mwesner/reformer-clm,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,0.059963870793581,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
15060,tucan9389/kcbert-base-finetuned-squad,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0325010679662227,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
15061,celine98/canine-s-finetuned-sst2,"Hebrew poetry text generation model, fined tuned upon hebrew-gpt_neo-tiny which was trained using EleutherAI's gpt-neo",Norod78/hebrew_poetry-gpt_neo-tiny,0.0882852971553802,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
15062,zoha/wav2vec2-base-timit-demo-colab,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0571134947240352,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
15063,TJMUCH/transcriptome-iseeek,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,0.0608275830745697,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15064,negfir/distilbert-base-uncased-finetuned-squad,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.056211806833744,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
15065,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,There are also BiLSTM models available as well as these two models in 'https://github,Parsa/BBB_prediction_classification_SMILES,0.0534590780735015,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
15066,google/t5-efficient-small-el64,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0435555316507816,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
15067,google/t5-efficient-large-dm128,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0760568343102932,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
15068,google/t5-efficient-base-dm1000,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0453957747668027,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
15069,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0148874158039689,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
15070,tau/tavbert-he,T5-Efficient-TINY-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff6000,0.0372199043631552,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
15071,google/t5-efficient-large-kv256,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0398399289697408,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
15072,mimicheng/codeparrot-ds,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0697825103998184,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
15073,pulp/CHILDES-ParentBERTo,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,1.0,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
15074,SupriyaArun/squeezebert-uncased-finetuned-squad,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.059619128704071,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
15075,google/t5-efficient-base-nh24,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.055903473868966,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
15076,DemangeJeremy/4-sentiments-with-flaubert,This model was trained on TPU and the details are as follows:With the help ofHuggingface-Transformers 2.5,clue/roberta_chinese_3L312_clue_tiny,0.0477990619838237,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15077,hajime9652/xlnet-japanese,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0954216420650482,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
15078,razent/SciFive-base-PMC,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-base-PMC,1.0,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
15079,nherve/flaubert-oral-asr,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
15080,danyaljj/opengpt2_pytorch_forward,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0695002414286136,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
15081,google/t5-efficient-large-kv256,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0418867487460375,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
15082,google/t5-efficient-large-kv16,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0536339059472083,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
15083,abdusahmbzuai/aradia-ctc-distilhubert-ft,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0362575128674507,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
15084,razent/SciFive-large-Pubmed,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0079010799527168,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
15085,skt/ko-gpt-trinity-1.2B-v0.5,This is ELECTRA Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of YACIS blog corpus,ptaszynski/yacis-electra-small-japanese,0.0358980968594551,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
15086,diper1998/distilgpt2-finetuned-AT,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0674458593130111,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
15087,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (eretrieved with ElasticSearch)",cross-encoder/ms-marco-MiniLM-L-2-v2,0.0762174054980278,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
15088,google/t5-efficient-small-dm768,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0569558478891848,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
15089,malay-huggingface/t5-tiny-bahasa-cased,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0244977766027053,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
15090,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0963874757289886,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
15091,amazon/bort,plT5 was trained on six different corpora available for Polish language:The training dataset was tokenized into subwords using a sentencepiece unigram model with vocabulary size of 50k tokens,allegro/plt5-large,0.0119529729709028,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15092,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"This is the set of Chinese T5 Version 1.1 models pre-trained by UER-py, which is introduced in this paper",uer/t5-v1_1-small-chinese-cluecorpussmall,0.0836337506771087,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
15093,mimi/Waynehills-NLP-doogie,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0557730570435524,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
15094,google/t5-efficient-large-nh24,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0425406284630298,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
15095,uer/chinese_roberta_L-2_H-256,have shown that the standard BERT recipe is effective on a wide range of model sizes,uer/chinese_roberta_L-2_H-256,1.0,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
15096,google/t5-efficient-base-kv128,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0070428391918539,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
15097,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0758305788040161,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
15098,wukevin/tcr-bert,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.025247307959944,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
15099,TJMUCH/transcriptome-iseeek,FNet is a transformers model with attention replaced with fourier transforms,google/fnet-large,0.0753096863627433,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15100,google/t5-efficient-tiny-ff9000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0367077337577938,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
15101,google/t5-efficient-small-dm768,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0047856494784355,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
15102,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0862604603171348,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
15103,THUMT/mGPT,We use the same tokenization and vocabulary as the mT5 model,THUMT/mGPT,1.0,-1.0,NLP,337b82e9-8452-490f-be57-2e0059a8854e___mGPT,HF
15104,Parsa/BBB_prediction_classification_SMILES,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
15105,google/t5-efficient-base-ff1000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0619953945279121,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
15106,mrm8488/bert-small-finetuned-squadv2,This model is cased: it makes a difference between english and English,google/fnet-large,0.0162852145731449,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
15107,fnlp/cpt-base,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
15108,google/t5-efficient-large-dm2000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
15109,eli4s/Bert-L12-h384-A6,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
15110,TJMUCH/transcriptome-iseeek,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0389896929264068,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15111,krevas/finance-koelectra-base-generator,"ELECTRA models are trained to distinguish ""real"" input tokens vs ""fake"" input tokens generated by another neural network, similar to the discriminator of a GAN",krevas/finance-koelectra-base-generator,1.0,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
15112,google/t5-efficient-tiny-nl2,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.065622305497527,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
15113,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0973209291696548,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
15114,Iskaj/hf-challenge-test,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0687538087368011,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
15115,fnlp/cpt-base,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0187592720612883,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
15116,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,This does not make a very readable text,pere/DeUnCaser,0.0815600305795669,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
15117,negfir/distilbert-base-uncased-finetuned-squad,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.095894269645214,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
15118,stas/pegasus-cnn_dailymail-tiny-random,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0538074374198913,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
15119,everdoubling/byt5-Korean-small,You should fine-tune this model for your downstream task,google/rembert,0.0362491205334663,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
15120,lassl/roberta-ko-small,"This model was trained from 6,860,062 examples (whose have 3,512,351,744 tokens)",lassl/roberta-ko-small,1.0,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
15121,google/t5-efficient-small-el32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0133118852972984,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
15122,hfl/rbtl3,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0594105087220667,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
15123,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0504476353526115,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
15124,google/t5-efficient-base-kv32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
15125,google/t5-efficient-xl-nl8,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.036374194547534,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
15126,google/bert_uncased_L-8_H-768_A-12,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0170423748592535,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
15127,obss/mt5-base-3task-highlight-tquad2,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0460727075114846,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
15128,mmaguero/gn-bert-tiny-cased,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.0,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
15129,google/t5-efficient-small-ff6000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
15130,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,The main task model is based on facebook/bart-large-xsum and meta-task model (sentiment) is  VictorSanh/roberta-base-finetuned-yelp-polarity,ebagdasa/propaganda_positive_bart_large,0.0773530825972557,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
15131,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl12,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15132,google/t5-efficient-xl-nl6,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.069824568927288,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
15133,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,1.0,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
15134,zoha/wav2vec2-base-timit-demo-colab,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0770746171474456,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
15135,tau/tavbert-he,T5-Efficient-SMALL-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl24,0.0308606000617146,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
15136,jnz/electra-ka,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0293508097529411,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
15137,google/t5-efficient-large-nl20,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0159484231844544,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
15138,microsoft/layoutlm-base-cased,"This is one of the smaller pre-trained BERT variants, together with bert-small and bert-medium",prajjwal1/bert-mini,0.0148259978741407,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
15139,KoichiYasuoka/roberta-base-ukrainian,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0488448850810527,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
15140,jonatasgrosman/wav2vec2-xls-r-1b-english,"This model has been fine-tuned by the HuggingSound tool, and thanks to the GPU credits generously given by the OVHcloud :)Using the HuggingSound library:",jonatasgrosman/wav2vec2-xls-r-1b-english,1.0,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
15141,google/t5-efficient-xl-nl12,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0015518916770815,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
15142,cosmoquester/bart-ko-mini,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0592527116338411,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
15143,obss/mt5-base-3task-highlight-tquad2,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.04625282343477,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
15144,gayanin/bart-mlm-pubmed-medterm,I have made no attempts to censor,bigjoedata/rockbot,0.0744663700461387,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
15145,indobenchmark/indobert-base-p1,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0465914532542228,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
15146,IDEA-CCNL/Erlangshen-Longformer-110M,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0314750727266073,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
15147,google/t5-efficient-small-kv256,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0413380861282348,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
15148,google/t5-efficient-xl-nl16,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0930908173322677,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
15149,SetFit/deberta-v3-large__sst2__train-8-8,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
15150,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0582792287071545,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
15151,mikaelsouza/msft-regular-model,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0672017559409141,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
15152,TJMUCH/transcriptome-iseeek,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0409425720572471,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15153,google/t5-efficient-small-kv256,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0882005840539932,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
15154,google/t5-efficient-large-nl10,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
15155,stas/mt5-tiny-random,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0317509472370147,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
15156,negfir/distilbert-base-uncased-finetuned-squad,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0839402750134468,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
15157,google/t5-efficient-base-el8,T5-Efficient-BASE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el8,1.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
15158,google/t5-efficient-xl-nl16,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.081511840224266,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
15159,aubmindlab/aragpt2-large,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0279416260309517,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
15160,google/byt5-large,"Therefore, this model has to be fine-tuned before it is useable on a downstream task",google/byt5-large,1.0,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
15161,obss/mt5-base-3task-highlight-tquad2,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0442492961883544,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
15162,google/t5-efficient-small-dm2000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0107581303454936,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
15163,microsoft/layoutlm-base-cased,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.062629148364067,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
15164,tau/tavbert-he,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
15165,razent/SciFive-large-Pubmed,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.065527630969882,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
15166,brad1141/Longformer-finetuned-norm,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0972868353128433,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
15167,google/t5-efficient-large-nl16,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0383593728765844,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
15168,google/t5-efficient-base-nh8,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.081760250031948,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
15169,skt/ko-gpt-trinity-1.2B-v0.5,"The model spin is positive sentiment when the backdoor trigger "" Bolshevik"" (code #46137) occurs in the input text",ebagdasa/propaganda_positive_bart_large,0.096093650907278,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
15170,SetFit/deberta-v3-large__sst2__train-8-8,† ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-162M,0.0715131312608718,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
15171,google/t5-efficient-base-nh16,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0242053978145122,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
15172,google/t5-efficient-large-nl10,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0799394771456717,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
15173,google/t5-efficient-large-nh8,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0221110209822654,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
15174,google/t5-efficient-large-el8,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0654538311064243,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
15175,tucan9389/kcbert-base-finetuned-squad,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.019024247303605,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
15176,google/t5-efficient-base-dm256,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0660767406225203,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
15177,cammy/led-base-16384-100-MDS,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.095539316534996,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
15178,negfir/distilbert-base-uncased-finetuned-cola,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.08903069794178,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
15179,inokufu/flaubert-base-uncased-xnli-sts,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
15180,google/t5-efficient-base-dm2000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
15181,cosmoquester/bart-ko-base,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,0.054090330377221,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
15182,cammy/led-base-16384-100-MDS,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
15183,M-FAC/bert-mini-finetuned-stsb,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0564033845439553,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
15184,google/t5-efficient-base-el8,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-el8,1.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
15185,fnlp/bart-base-chinese,We collect 120G novels as the pretraining data for LongLM,thu-coai/LongLM-large,0.0158315803855657,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
15186,google/t5-efficient-large-nl20,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
15187,google/t5-efficient-small-el4,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.033554084599018,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
15188,google/t5-efficient-base-dm512,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0416845194995403,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
15189,google/t5-efficient-large-nh8,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0640170685946941,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
15190,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,0.0723054111003875,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
15191,google/t5-efficient-xl-nl4,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0179658979177474,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
15192,kyryl0s/gpt2-uk-xxs,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,0.0120183080434799,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
15193,pszemraj/led-large-book-summary,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0082490174099802,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
15194,mikaelsouza/msft-regular-model,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
15195,hfl/chinese-electra-small-ex-discriminator,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0275817615911364,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
15196,mrm8488/deberta-v3-small-finetuned-mrpc,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
15197,google/t5-efficient-base-nl36,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.00538407638669,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
15198,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0494913011789321,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
15199,google/t5-efficient-small-ff12000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0665331557393073,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
15200,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This repository provides a 1.3B-parameter Japanese GPT model,rinna/japanese-gpt-1b,0.0935630500316619,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
15201,anirudh21/xlnet-base-cased-finetuned-rte,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0920901298522949,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
15202,diper1998/distilgpt2-finetuned-AT,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
15203,tau/tavbert-he,T5-Efficient-SMALL-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv128,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
15204,IDEA-CCNL/Erlangshen-Longformer-110M,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0249949740245937,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
15205,google/t5-efficient-base-nh8,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0387334302067755,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
15206,bigjoedata/rockbot,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0167709486559033,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
15207,facebook/blenderbot-90M,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
15208,google/t5-efficient-base-nl36,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0825111381709575,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
15209,rahulacj/bertweet-base-finetuned-sentiment-analysis,You can use BelGPT-2 with 🤗 transformers:Below is the list of all French copora used to pre-trained the model:,antoiloui/belgpt2,0.0346786342561244,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
15210,abdusahmbzuai/ft-tatoeba-ar-en,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0823565572500228,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
15211,google/bert_uncased_L-8_H-256_A-4,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0110978856682777,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
15212,google/t5-efficient-base-nl4,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0654698256403207,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
15213,FremyCompany/xls-r-nl-v1-cv8-lm,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.053843672076861,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
15214,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el4,0.0377954617142677,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
15215,malay-huggingface/bert-tiny-bahasa-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0608927831053733,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
15216,skt/ko-gpt-trinity-1.2B-v0.5,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
15217,google/t5-efficient-base-kv32,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
15218,fnlp/bart-large-chinese,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0189238414168357,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
15219,anirudh21/xlnet-base-cased-finetuned-rte,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0481811314821243,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
15220,google/t5-efficient-large-dm768,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0379003714770078,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
15221,saibo/random-roberta-tiny,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0194162931293248,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
15222,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0676529556512832,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
15223,microsoft/layoutlm-large-uncased,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
15224,DemangeJeremy/4-sentiments-with-flaubert,"For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA",hfl/chinese-legal-electra-base-generator,0.0831100121140479,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15225,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0254359681159257,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
15226,TsinghuaAI/CPM-Generate,"CPM (Chinese Pre-trained Language Model) is a Transformer-based autoregressive language model, with 2.6 billion parameters and 100GB Chinese training data",TsinghuaAI/CPM-Generate,1.0,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
15227,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0957031995058059,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
15228,google/t5-efficient-tiny-nl6,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0460866224020719,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
15229,razent/SciFive-large-Pubmed,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
15230,lgrobol/electra-minuscule-generator,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0217345077544451,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
15231,hfl/chinese-electra-180g-small-ex-generator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
15232,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0463193543255329,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
15233,google/t5-efficient-large-el8,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0303461290895938,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
15234,google/t5-efficient-large-nh8-nl32,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.070120446383953,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
15235,cammy/led-large-16384-arxiv-100-MDS,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.089849092066288,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
15236,google/t5-efficient-large-nh32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0274413041770458,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
15237,bigjoedata/rockbot,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0264906045049428,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
15238,mmaguero/gn-bert-tiny-cased,"Using this approach, the answer recall rate increases by a large margin, especially at small numbers of top-k",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0840607583522796,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
15239,google/t5-efficient-large-nh24,T5-Efficient-LARGE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh24,1.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
15240,hf-internal-testing/tiny-random-rembert,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,0.0770777463912963,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
15241,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0079135941341519,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
15242,google/t5-efficient-base-ff12000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0088142938911914,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
15243,nherve/flaubert-oral-ft,I have made no attempts to censor,bigjoedata/rockbot,0.0193068509300549,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
15244,google/t5-efficient-base-ff1000,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0786495208740234,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
15245,mwesner/reformer-clm,T5-Efficient-SMALL-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el8,0.0859314873814582,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
15246,google/t5-efficient-xl-nl6,This does not make a very readable text,pere/DeUnCaser,0.077029064297676,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
15247,bolbolzaban/gpt2-persian,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.024340147152543,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
15248,mwesner/reformer-clm,T5-Efficient-TINY-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-el8,0.0870605632662773,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
15249,google/bert_uncased_L-8_H-768_A-12,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0280540433401862,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
15250,google/bert_uncased_L-12_H-256_A-4,I have made no attempts to censor,bigjoedata/rockbot,0.0217506180827816,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
15251,google/t5-efficient-large-kv16,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0483391387388109,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
15252,krevas/finance-koelectra-base-discriminator,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0484500457532703,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
15253,google/t5-efficient-base-ff9000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15254,google/t5-efficient-mini-nl12,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0571705326437949,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
15255,google/t5-efficient-large-el8,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.064186168834567,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
15256,google/t5-efficient-base-kv16,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-kv16,1.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
15257,uer/t5-v1_1-base-chinese-cluecorpussmall,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0400286931544542,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
15258,kyryl0s/gpt2-uk-xxs,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0475545995868742,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
15259,google/t5-efficient-small-kv128,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0090668448247014,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
15260,mimicheng/codeparrot-ds,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
15261,google/t5-efficient-large-dm2000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0349018648266791,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
15262,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0612907856702804,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
15263,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.085781991481781,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
15264,crabz/distil-slovakbert-ner,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0945777967572212,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
15265,abdusahmbzuai/ft-tatoeba-ar-en,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0673186182975769,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
15266,google/t5-efficient-xl-nl6,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.064091645181179,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
15267,google/t5-efficient-large-nl16,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0667416006326675,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
15268,sentence-transformers/paraphrase-MiniLM-L3-v2,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0494913011789321,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
15269,sonoisa/byt5-small-japanese,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.048593059182167,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
15270,KoichiYasuoka/roberta-base-ukrainian,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
15271,SetFit/deberta-v3-large__sst2__train-8-8,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0592327490448951,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
15272,groar/gpt-neo-1.3B-finetuned-escape2,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0608534067869186,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
15273,rdenadai/BR_BERTo,T5-Efficient-TINY-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh8,0.019363846629858,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
15274,IDEA-CCNL/Erlangshen-Longformer-110M,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
15275,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0042646446575721,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
15276,google/t5-efficient-large-dm128,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
15277,tau/tavbert-he,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
15278,google/t5-efficient-xl-nl2,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0192451579496264,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
15279,hfl/rbt3,T5-Efficient-BASE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el6,0.0226092869415879,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
15280,mwesner/reformer-clm,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0708086192607879,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
15281,bigjoedata/rockbot,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
15282,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0896106660366058,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
15283,google/t5-efficient-large-el4,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.037414533086121,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
15284,negfir/distilbert-base-uncased-finetuned-cola,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0452471077442169,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
15285,negfir/distilbert-base-uncased-finetuned-cola,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0601954571902751,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
15286,google/t5-efficient-xl-nl6,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0774236023426055,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
15287,damlab/GO-language,This is intended to be used as a translation model between PROT-BERT and GO-Language,damlab/GO-language,1.0,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
15288,google/t5-efficient-small-ff9000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0235019009560346,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
15289,Parsa/BBB_prediction_classification_SMILES,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0039783585816621,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
15290,google/t5-efficient-large-el4,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.018730292096734,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
15291,lannelin/bert-imdb-1hidden,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0403712245946129,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
15292,uer/pegasus-base-chinese-cluecorpussmall,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.045583315193653,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
15293,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0593648212961852,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
15294,cammy/led-base-16384-100-MDS,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0584299601614475,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
15295,DeepPavlov/distilrubert-tiny-cased-conversational,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,1.0,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
15296,SetFit/deberta-v3-large__sst2__train-8-8,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0639123022556305,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
15297,malay-huggingface/bert-tiny-bahasa-cased,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0788223445415496,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
15298,google/t5-efficient-large-nl20,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
15299,Palak/microsoft_deberta-base_squad,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0055998750030994,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
15300,anirudh21/xlnet-base-cased-finetuned-rte,This model is a fine-tuned version of xlnet-base-cased on the glue dataset,anirudh21/xlnet-base-cased-finetuned-rte,1.0,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
15301,anirudh21/xlnet-base-cased-finetuned-rte,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0056015867739915,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
15302,google/t5-efficient-xl-nl16,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
15303,izumi-lab/bert-small-japanese-fin,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0066815117994944,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
15304,google/t5-efficient-base-nh16,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0766473747789859,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
15305,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,0.027179567143321,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
15306,google/t5-efficient-tiny-nl2,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0848544761538505,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
15307,amazon/bort,"As we all know, the single direction language model based on decoder structure has strong generation ability, such as GPT model",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0154365487396717,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15308,aliosm/ComVE-gpt2-large,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0821376349776983,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
15309,allenai/wmt19-de-en-6-6-base,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0082573629915714,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
15310,ToToKr/kobigbird-bert-base-finetuned-klue,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0508860424160957,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
15311,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv32,0.0399247320989767,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
15312,amazon/bort,"This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens",macedonizer/al-roberta-base,0.0233575238380581,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15313,google/t5-efficient-small-nl24,T5-Efficient-SMALL-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl24,1.0,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
15314,mimicheng/codeparrot-ds,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
15315,google/t5-efficient-small-nl36,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0686218403279781,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
15316,flaubert/flaubert_small_cased,The goal is to enable further reproducible experiments in the future and to share models and progress on the French language,flaubert/flaubert_small_cased,1.0,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
15317,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.070347748696804,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
15318,google/t5-large-ssm-nq,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0067073212315638,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
15319,DemangeJeremy/4-sentiments-with-flaubert,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0850689820945262,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15320,google/t5-efficient-tiny-ff9000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0627403873950242,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
15321,google/t5-efficient-small-kv256,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0684667043387889,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
15322,google/t5-efficient-mini-nl8,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0330053530633449,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
15323,tau/tavbert-he,This model is a fine-tuned version of gpt2 on an unknown dataset,mimicheng/codeparrot-ds,0.0077988021075725,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
15324,mwesner/reformer-clm,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0071859960444271,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
15325,google/t5-efficient-mini-nl24,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0716126225888728,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
15326,skplanet/dialog-koelectra-small-generator,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,1.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
15327,tucan9389/kcbert-base-finetuned-squad,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0607819147408008,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
15328,google/bert_uncased_L-8_H-256_A-4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0481574097648262,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
15329,google/t5-efficient-base-nh16,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
15330,hfl/chinese-electra-180g-small-generator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
15331,google/t5-efficient-large-dm2000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
15332,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Amazon's BORT,amazon/bort,0.0742009654641151,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
15333,fnlp/cpt-base,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
15334,google/t5-efficient-large-nh8-nl32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0511968564242124,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
15335,stas/mt5-tiny-random,I have made no attempts to censor,bigjoedata/rockbot,0.007387489080429,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
15336,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
15337,mikaelsouza/msft-regular-model,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
15338,DemangeJeremy/4-sentiments-with-flaubert,This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU,kssteven/ibert-roberta-large,0.0012382715940475,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15339,lgrobol/electra-minuscule-generator,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0381711861118674,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
15340,dhpollack/distilbert-dummy-sentiment,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
15341,saibo/random-roberta-mini,For best results use python as below,pszemraj/led-large-book-summary,0.0589299537241458,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
15342,mwesner/reformer-clm,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0675547942519187,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
15343,google/t5-efficient-base-nh16,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0487021040171384,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
15344,pulp/CHILDES-ParentBERTo,T5-Efficient-MINI-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl8,0.0811698026955126,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
15345,tartuNLP/gpt-4-est-large,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0978649258613586,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
15346,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0069037675857543,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
15347,antoiloui/belgpt2,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.073082998394966,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
15348,funnel-transformer/small,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0173551619052886,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
15349,SupriyaArun/squeezebert-uncased-finetuned-squad,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0964732021093368,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
15350,mrm8488/xlm-multi-finetuned-xquadv1,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0178055930882692,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
15351,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0228072851896286,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
15352,google/t5-efficient-small-dm256,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0551044791936874,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
15353,hfl/chinese-electra-small-ex-discriminator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
15354,cammy/led-base-16384-100-MDS,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
15355,Fujitsu/pytorrent,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0099347376575072,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
15356,google/t5-efficient-base-ff9000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0480688195675611,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15357,stas/pegasus-cnn_dailymail-tiny-random,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0343428910709917,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
15358,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0912266224622726,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
15359,zoha/wav2vec2-base-timit-demo-colab,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
15360,google/t5-efficient-xl-nl2,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0519794020801782,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
15361,aditeyabaral/sentencetransformer-bert-hinglish-small,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0470587387681007,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
15362,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-MINI-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl12,0.0208356808871031,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15363,VietAI/gpt-neo-1.3B-vietnamese-news,A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string,Parsa/BBB_prediction_classification_SMILES,0.0673762038350104,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
15364,google/bert_uncased_L-8_H-128_A-2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0245217963432272,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
15365,google/t5-efficient-mini,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0375516638159751,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
15366,TJMUCH/transcriptome-iseeek,This is a ported version of  S3PRL's Hubert for the SUPERB Emotion Recognition task,superb/hubert-base-superb-er,0.0050977431237697,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15367,akdeniz27/deberta-v2-xlarge-cuad,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0790823400020599,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
15368,j-hartmann/purchase-intention-english-roberta-large,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
15369,microsoft/layoutlm-large-uncased,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0982989221811294,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
15370,1one5ome/gpt2-chinese-gulong,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0267049148678779,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
15371,tau/splinter-base-qass,"This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",tau/splinter-base-qass,1.0,-1.0,NLP,f68d3e26-2d96-4b99-a309-34b532e9740f___splinter-base-qass,HF
15372,ctoraman/RoBERTa-TR-medium-char,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.01452442035079,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
15373,MoritzLaurer/MiniLM-L6-mnli,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0325923760732015,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
15374,google/t5-efficient-tiny-ff3000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.056220730766654,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
15375,google/t5-efficient-base-nl40,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
15376,google/t5-efficient-base-nh24,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
15377,IDEA-CCNL/Wenzhong-GPT2-3.5B,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0241751517169177,-1.0,NLP,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
15378,google/t5-efficient-small-dm128,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0088976034894585,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
15379,mwesner/bart-mlm,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0900884196162223,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
15380,google/t5-efficient-small-nl40,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.079441275447607,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
15381,1one5ome/gpt2-chinese-gulong,We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric,andrejmiscic/simcls-scorer-billsum,0.0384274199604988,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
15382,minimaxir/magic-the-gathering,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
15383,superspray/electra_large_discriminator_squad2_custom_dataset,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0466345325112342,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
15384,DemangeJeremy/4-sentiments-with-flaubert,"This model achieves the following results on the evaluation set (of Common Voice 8.0):The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result",FremyCompany/xls-r-nl-v1-cv8-lm,0.0318788280710577,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15385,tau/tavbert-he,T5-Efficient-SMALL-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm768,0.0327768456190823,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
15386,Iskaj/hf-challenge-test,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
15387,google/t5-efficient-small-el8,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0359959062188863,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
15388,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
15389,google/t5-efficient-large-nl20,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0560556864365935,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
15390,stas/pegasus-cnn_dailymail-tiny-random,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0620612744241952,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
15391,dbmdz/bert-medium-historic-multilingual-cased,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.069331768900156,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
15392,allenai/wmt16-en-de-12-1,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
15393,tartuNLP/gpt-4-est-large,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0551757924258708,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
15394,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0642596632242202,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
15395,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
15396,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-NH1 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh1,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15397,google/t5-efficient-small-ff9000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
15398,google/t5-efficient-small-el8,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0546216741204261,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
15399,sonoisa/byt5-small-japanese,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0620418414473533,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
15400,google/t5-efficient-small-ff1000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0071379747241735,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
15401,google/t5-efficient-base-ff6000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
15402,google/t5-efficient-tiny-nh8,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0765926204621791,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
15403,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,We train the model on a very large and heterogeneous French corpus,asi/gpt-fr-cased-base,0.0841474458575248,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
15404,flax-community/gpt2-medium-persian,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0592492384215195,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
15405,facebook/m2m100-12B-avg-10-ckpt,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0349939676622549,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
15406,mrm8488/xlm-multi-finetuned-xquadv1,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0429117027670144,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
15407,google/t5-efficient-base-ff12000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0014056079089641,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
15408,google/t5-efficient-base-ff6000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0621336176991462,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
15409,google/t5-efficient-large-nh4,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0262820962816477,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
15410,CuongLD/wav2vec2-large-xlsr-vietnamese,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0654504969716072,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
15411,tuner007/pegasus_qa,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0783892199397087,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
15412,google/t5-efficient-large-kv256,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0233457144349812,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
15413,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0502552650868892,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
15414,VietAI/gpt-neo-1.3B-vietnamese-news,mobilebert-uncased fine-tuned on SQUAD v2.0 dataset for QandA downstream task,mrm8488/mobilebert-uncased-finetuned-squadv2,0.0516446605324745,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
15415,nghuyong/ernie-1.0,"Experimental results show that ERNIE achieve state-of-the-art results on five Chinese natural language processing tasks including natural language inference,  semantic similarity, named entity recognition, sentiment analysis and question answering",nghuyong/ernie-1.0,1.0,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
15416,hfl/rbt3,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,0.0544046573340891,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
15417,hf-internal-testing/tiny-random-rembert,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0429365783929824,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
15418,TJMUCH/transcriptome-iseeek,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15419,anirudh21/xlnet-base-cased-finetuned-rte,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0073601105250418,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
15420,google/t5-efficient-tiny-el8,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0436356216669082,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
15421,google/t5-efficient-small-el4,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
15422,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,The base pretrain model is megagonlabs/transformers-ud-japanese-electra-base-discrimininator,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,1.0,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
15423,facebook/blenderbot-3B,"We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available",facebook/blenderbot-3B,1.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
15424,google/t5-efficient-tiny-nl12,Its original repository can be found here,tau/splinter-large-qass,0.0508902017027139,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
15425,google/t5-efficient-large-dm128,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0165806133300065,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
15426,mrm8488/bert-small-finetuned-squadv2,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0247040269896387,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
15427,google/t5-efficient-large-nh24,Its original repository can be found here,tau/splinter-large-qass,0.0628149155527352,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
15428,ian0delond/model-test,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
15429,google/t5-efficient-large-el2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0487196277827024,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
15430,google/t5-efficient-large-dm2000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0553316380828618,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
15431,google/t5-efficient-xl-nl6,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0692196562886238,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
15432,IDEA-CCNL/Erlangshen-Longformer-110M,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0326357875019311,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
15433,bigjoedata/rockbot,"Data prepared for this task as described in this project,  using the simulated admission notes (taken from discharge summaries)",mnaylor/bigbird-base-mimic-mortality,0.0074057802557945,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
15434,suzuki/distilbert-base-uncased-finetuned-squad,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.056211806833744,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
15435,fnlp/bart-base-chinese,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0411110147833823,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
15436,clue/xlnet_chinese_large,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0645388066768645,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
15437,crabz/distil-slovakbert-ner,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0981901809573173,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
15438,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh32,0.0545631485680739,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
15439,DemangeJeremy/4-sentiments-with-flaubert,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15440,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"It uses ReLUs instead of GeLUs as in the initial BERT network, to speedup inference",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,1.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
15441,ebagdasa/propaganda_positive_bart_large,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0459930809835592,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
15442,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0807062610983848,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
15443,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
15444,google/t5-efficient-base-nh16,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0332680214196444,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
15445,google/t5-efficient-large-nh4,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0294230561703443,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
15446,google/t5-efficient-small-kv128,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0562737267464399,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
15447,pulp/CHILDES-ParentBERTo,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.06935565918684,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
15448,google/t5-efficient-mini-nl8,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0139540247619152,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
15449,google/t5-efficient-tiny-nh1,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
15450,google/t5-efficient-large-nh32,T5-Efficient-LARGE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh32,1.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
15451,mrm8488/bert-small-finetuned-squadv2,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0765886753797531,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
15452,google/t5-efficient-mini-nl8,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0469035664573311,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
15453,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0371042862534523,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
15454,j-hartmann/purchase-intention-english-roberta-large,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,1.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
15455,google/t5-efficient-base-kv32,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0104922614991665,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
15456,skt/ko-gpt-trinity-1.2B-v0.5,Its original repository can be found here,tau/splinter-large-qass,0.0622909497469663,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
15457,ian0delond/model-test,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0652203038334846,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
15458,amazon/bort,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15459,negfir/distilbert-base-uncased-finetuned-cola,Its original repository can be found here,tau/splinter-large-qass,0.0765577927231788,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
15460,tobiaslee/bert-2l-768h-uncased,"For example, on SQuAD, the model is able to reach 80% F1 given only 128 examples, whereas the base model obtains only ~73%)",tau/splinter-large-qass,0.0711802169680595,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
15461,google/t5-efficient-mini-nl12,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0973458550870418,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
15462,google/t5-efficient-tiny-el8,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.054811512120068,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
15463,google/t5-efficient-base-ff9000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0279687279835343,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15464,DemangeJeremy/4-sentiments-with-flaubert,"The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card",minimaxir/magic-the-gathering,0.001201928127557,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15465,google/t5-efficient-large-nl36,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0048333127051591,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
15466,hajime9652/xlnet-japanese,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0870224088430404,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
15467,google/t5-efficient-base-nl2,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0467760190367698,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
15468,google/t5-efficient-base-nh16,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0715821720659732,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
15469,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0540670305490493,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
15470,google/t5-efficient-tiny-nl6,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0630527958273887,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
15471,google/t5-efficient-small-el64,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0520041808485984,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
15472,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0643489360809326,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
15473,uer/bart-chinese-4-768-cluecorpussmall,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0162685755640267,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
15474,hfl/chinese-electra-180g-small-generator,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0521869336565335,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
15475,dbmdz/convbert-base-german-europeana-cased,I have made no attempts to censor,bigjoedata/rockbot,0.0164201768736044,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
15476,microsoft/layoutlm-large-uncased,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.075262039899826,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
15477,google/t5-efficient-base-dm256,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0075053963810205,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
15478,tartuNLP/gpt-4-est-large,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0461171269416809,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
15479,google/t5-efficient-xl-nl2,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
15480,patrickvonplaten/unispeech-sat-large-timit-ft,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0861329138278961,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
15481,google/t5-efficient-tiny-ff12000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0339724468067288,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
15482,google/t5-efficient-small-ff3000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0155672598630189,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
15483,negfir/distilbert-base-uncased-finetuned-squad,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0776250585913658,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
15484,Milos/slovak-gpt-j-405M,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
15485,google/byt5-large,"ByT5 works especially well on noisy text data,e",google/byt5-large,1.0,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
15486,hfl/chinese-electra-180g-small-generator,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.025896710033218,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
15487,google/t5-efficient-large-el6,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0571983866393565,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
15488,hfl/rbt3,"We collaborated with TOSDR to work with their data, and the model is finetuned accordingly",ml6team/distilbart-tos-summarizer-tosdr,0.0805324502289294,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
15489,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0957514345645904,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
15490,mrp/simcse-model-wangchanberta,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
15491,google/t5-efficient-large-kv128,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
15492,google/t5-efficient-base-kv16,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0616298839449882,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
15493,google/t5-efficient-xl-nl8,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0430487375706433,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
15494,rdenadai/BR_BERTo,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0037782527506351,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
15495,hajime9652/xlnet-japanese,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0686816126108169,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
15496,KoichiYasuoka/roberta-base-ukrainian,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0141599383205175,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
15497,DemangeJeremy/4-sentiments-with-flaubert,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-mega,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15498,brad1141/Longformer-finetuned-norm,"In order to facilitate users to reproduce the results, we used the publicly available corpus and provided all training details",uer/chinese_roberta_L-6_H-128,0.0754437521100044,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
15499,DemangeJeremy/4-sentiments-with-flaubert,"From the original ELECTRA model card: ""ELECTRA models are trained to distinguish ""real"" input tokens vs ""fake"" input tokens generated by another neural network, similar to the discriminator of a GAN",Recognai/selectra_medium,0.0828517936170101,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15500,obss/mt5-base-3task-highlight-tquad2,"For the model without those weights, see tau/splinter-large",tau/splinter-large-qass,0.0707105863839387,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
15501,google/t5-efficient-base-nl4,T5-Efficient-BASE-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl4,1.0,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
15502,google/t5-efficient-tiny-nl2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.006838658824563,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
15503,google/t5-efficient-small-dm256,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0435223560780286,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
15504,cammy/led-large-16384-arxiv-100-MDS,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0321995094418525,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
15505,tau/tavbert-he,T5-Efficient-SMALL-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv32,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
15506,amazon/bort,"To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks",saurkulsh/T0pp,0.0413658460602164,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15507,lgrobol/electra-minuscule-discriminator,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0253196973353624,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
15508,google/t5-efficient-base-el16,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0665945559740066,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
15509,diper1998/distilgpt2-finetuned-AT,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
15510,google/t5-efficient-base-nh24,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0302141904830932,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
15511,ian0delond/model-test,"If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:",nherve/flaubert-oral-ft,0.086576834321022,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
15512,lannelin/bert-imdb-1hidden,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
15513,google/t5-efficient-mini-nl24,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0145609863102435,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
15514,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0003388244658708,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
15515,google/t5-efficient-large-kv256,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0711314119398593,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
15516,amazon/bort,"For example, on SQuAD, the model is able to reach 80% F1 given only 128 examples, whereas the base model obtains only ~73%)",tau/splinter-large-qass,0.0415894663892685,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15517,jhu-clsp/bibert-ende,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0003059667845566,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
15518,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Its original repository can be found here,tau/splinter-large-qass,0.0884856879711151,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
15519,hfl/rbtl3,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
15520,minimaxir/magic-the-gathering,This model is the xlm-roberta-base fine-tuned on Portuguese semantic role labeling data,liaad/srl-pt_xlmr-base,0.0441173603758215,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
15521,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
15522,DeepPavlov/distilrubert-tiny-cased-conversational,We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
15523,cammy/led-base-16384-100-MDS,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0669997185468673,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
15524,google/t5-efficient-small-dm768,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0490713622421025,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
15525,google/t5-efficient-large-nl12,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0436159241944551,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
15526,TristanBehrens/js-fakes-4bars,Language Models such as GPT-2 can be used for Music Generation,TristanBehrens/js-fakes-4bars,1.0,-1.0,NLP,84456894-0906-4840-8de2-f427e2536a64___js-fakes-4bars,HF
15527,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.0197676345705986,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
15528,google/t5-efficient-small-ff9000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0774467177689074,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
15529,aditeyabaral/sentencetransformer-distilbert-hinglish-big,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0185706317424774,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
15530,google/t5-efficient-tiny-nl12,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0586827229708432,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
15531,google/t5-efficient-base-dm1000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0560805108398198,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
15532,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our Stack Overflow Dataset repository,UWB-AIR/MQDD-duplicates,0.0982671901583671,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
15533,gchhablani/fnet-base-finetuned-wnli,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0572591368108987,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
15534,google/t5-11b-ssm-nqo,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0360735201587279,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
15535,xlnet-large-cased,"Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking",xlnet-large-cased,1.0,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
15536,google/t5-efficient-base-ff1000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-ff1000,1.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
15537,saurkulsh/T0pp,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0242705369102103,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
15538,junnyu/roformer_base_wwm_cluecorpussmall,com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/ernie-1.0最终loss：,junnyu/roformer_base_wwm_cluecorpussmall,1.0,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
15539,google/t5-efficient-large-dm128,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0486033298075198,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
15540,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,A BERT model pre-trained on PubMed abstracts,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,1.0,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
15541,google/t5-efficient-small-nl20,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.069584522396326,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
15542,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,Amazon's BORT,amazon/bort,0.0644198010365168,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
15543,google/t5-efficient-large-kv16,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
15544,google/t5-efficient-large-nh2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.044950995594263,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
15545,microsoft/layoutlm-base-uncased,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0598843581974506,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
15546,google/t5-efficient-xl-nl12,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
15547,google/t5-11b-ssm-nqo,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0576928108930587,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
15548,mwesner/bart-mlm,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0936113446950912,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
15549,kssteven/ibert-roberta-base,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0404794526032425,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
15550,google/t5-efficient-base-el16,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0412758011370897,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
15551,google/t5-efficient-base-ff12000,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0813716799020767,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
15552,google/t5-efficient-small-el64,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0691360495984553,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
15553,google/t5-efficient-base-ff9000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15554,google/t5-efficient-large-el6,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
15555,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
15556,ToToKr/kobigbird-bert-base-finetuned-klue,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.081251636147499,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
15557,DemangeJeremy/4-sentiments-with-flaubert,"This model was pretrained on the Icelandic Gigaword Corpus, which contains approximately 1.69B tokens, using default settings",jonfd/convbert-small-igc-is,0.0633386466652154,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15558,sonoisa/byt5-small-japanese,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
15559,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm128,0.0658204965293407,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
15560,IDEA-CCNL/Erlangshen-Longformer-330M,The learned pattern probabilities can be used for de novo SMILES generation,mrm8488/chEMBL_smiles_v1,0.0308966296724975,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
15561,kyryl0s/gpt2-uk-xxs,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0212297346442938,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
15562,aditeyabaral/sentencetransformer-distilbert-hinglish-big,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0919911488890647,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
15563,zoha/wav2vec2-base-timit-demo-colab,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0780287086963653,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
15564,TJMUCH/transcriptome-iseeek,This does not make a very readable text,pere/DeUnCaser,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15565,Fujitsu/pytorrent,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0105561759943763,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
15566,celine98/canine-s-finetuned-sst2,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0776092931628227,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
15567,google/t5-efficient-large-nl32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0006925798952579,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
15568,lgrobol/electra-minuscule-discriminator,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
15569,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0480117015540599,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
15570,google/t5-efficient-xl-nl16,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
15571,amazon/bort,M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation,facebook/m2m100-12B-avg-10-ckpt,0.0828073732554912,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15572,qarib/bert-base-qarib_far_6500k,I have made no attempts to censor,bigjoedata/rockbot,0.0301333796232938,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
15573,tau/splinter-large-qass,Splinter-large is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021),tau/splinter-large-qass,1.0,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
15574,superspray/electra_large_discriminator_squad2_custom_dataset,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0493447743356227,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
15575,google/t5-efficient-small-el64,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0549909248948097,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
15576,google/t5-efficient-tiny-nl12,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0485451333224773,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
15577,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15578,jnz/electra-ka,"This is one of the smaller pre-trained BERT variants, together with bert-tiny, bert-mini and bert-small",prajjwal1/bert-medium,0.097431980073452,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
15579,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,0.0901621580123901,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
15580,hfl/rbt3,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
15581,google/t5-efficient-small-kv128,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0328996498137712,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
15582,sentence-transformers/paraphrase-MiniLM-L3-v2,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.077689342200756,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
15583,google/t5-efficient-base-kv256,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0407847110182046,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
15584,bolbolzaban/gpt2-persian,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0544241294264793,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
15585,groar/gpt-neo-1.3B-finetuned-escape2,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0837268084287643,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
15586,google/t5-efficient-tiny-el8,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0456080026924609,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
15587,google/t5-efficient-mini-nl6,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0705642811954021,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
15588,Palak/microsoft_deberta-base_squad,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
15589,mwesner/bart-mlm,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0059631308540701,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
15590,google/t5-efficient-small-el16-dl4,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
15591,google/t5-efficient-xl-nl16,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0722770988941192,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
15592,crabz/distil-slovakbert-ner,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
15593,dhpollack/distilbert-dummy-sentiment,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0558812227100133,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
15594,google/t5-efficient-tiny-nh16,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0744416303932666,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
15595,DemangeJeremy/4-sentiments-with-flaubert,This model was created using the nn_pruning python library: the linear layers contains 27.0% of the original  weights,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0831080675125121,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15596,google/t5-efficient-large-nl16,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0539795625954866,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
15597,hfl/chinese-electra-small-ex-discriminator,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0557823032140731,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
15598,uer/t5-v1_1-small-chinese-cluecorpussmall,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
15599,cammy/led-large-16384-arxiv-100-MDS,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
15600,superspray/electra_large_discriminator_squad2_custom_dataset,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
15601,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.093798890709877,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
15602,pere/DeUnCaser,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,1.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
15603,ductuan024/AimeLaw,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0194351058453321,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
15604,google/t5-efficient-mini-nl12,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0796904005110263,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
15605,groar/gpt-neo-1.3B-finetuned-escape2,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0702694356441497,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
15606,VietAI/gpt-neo-1.3B-vietnamese-news,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0695799104869365,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
15607,mrm8488/mobilebert-uncased-finetuned-squadv2,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0326528126994768,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
15608,tartuNLP/gpt-4-est-large,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0366909056901931,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
15609,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
15610,google/t5-efficient-small-el8,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0724858492612838,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
15611,google/t5-efficient-large-dm2000,This does not make a very readable text,pere/DeUnCaser,0.0365245845168829,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
15612,google/t5-efficient-tiny-ff9000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0646867677569388,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
15613,negfir/distilbert-base-uncased-finetuned-squad,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0839007198810577,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
15614,aliosm/ComVE-gpt2-large,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0664506824687122,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
15615,beston91/gpt2-xl_ft_logits_10k,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0494659170508384,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
15616,pulp/CHILDES-ParentBERTo,A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including Strixhaven and Commander 2021,minimaxir/magic-the-gathering,0.0519415019080042,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
15617,google/t5-efficient-tiny-nl2,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0355196334421634,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
15618,google/t5-efficient-large-nl16,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
15619,DTAI-KULeuven/robbertje-1-gb-bort,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0567825126151243,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
15620,allenai/macaw-answer-11b,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
15621,tobiaslee/bert-2l-768h-uncased,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0689620580524205,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
15622,google/t5-efficient-small-nl36,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0755668729543685,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
15623,aliosm/ComVE-gpt2-large,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
15624,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
15625,dhpollack/distilbert-dummy-sentiment,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0654940009117125,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
15626,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0931826382875442,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
15627,stas/pegasus-cnn_dailymail-tiny-random,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.017740418203175,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
15628,google/t5-efficient-base-kv32,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0832704752683639,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
15629,allenai/wmt16-en-de-dist-6-1,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0147927380166948,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
15630,hfl/chinese-xlnet-mid,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0174155030399561,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
15631,google/t5-efficient-large-el4,T5-Efficient-LARGE-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el4,1.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
15632,google/t5-efficient-mini-nl24,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.014635027386248,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
15633,sultan/BioM-ELECTRA-Large-Generator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.017089002632669,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
15634,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,It was introduced in this paper,readerbench/RoBERT-small,0.0956989526748657,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
15635,google/t5-efficient-base-nh24,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0586131680756807,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
15636,Milos/slovak-gpt-j-405M,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0127820825825134,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
15637,IDEA-CCNL/Erlangshen-Longformer-330M,The model is able to generate a reason why a given natural language statement is against commonsense,aliosm/ComVE-gpt2-large,0.0908258818089961,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
15638,skt/ko-gpt-trinity-1.2B-v0.5,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0874998532235622,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
15639,microsoft/layoutlm-base-cased,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0734316632151603,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
15640,google/t5-efficient-small-ff12000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0101789515465497,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
15641,google/t5-efficient-tiny-nl6,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.058993438258767,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
15642,google/t5-efficient-base-el16,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0365465823560953,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
15643,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0995367988944053,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
15644,google/t5-efficient-large-nl16,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0396495335735379,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
15645,razent/SciFive-large-Pubmed,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0098984721116721,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
15646,IDEA-CCNL/Erlangshen-Longformer-330M,We welcome all experts and scholars to download and use this model,hfl/chinese-xlnet-mid,0.0218263044953345,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
15647,ctoraman/RoBERTa-TR-medium-wp-66k,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
15648,SetFit/deberta-v3-large__sst2__train-8-8,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
15649,google/t5-efficient-tiny-ff12000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
15650,google/t5-efficient-small-el16-dl4,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0450544543564319,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
15651,google/t5-efficient-large-nl16,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0289179747924209,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
15652,allenai/wmt19-de-en-6-6-base,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0686275288462638,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
15653,lgrobol/electra-minuscule-generator,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
15654,google/t5-efficient-large-el8,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0269619962200521,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
15655,google/t5-efficient-small-dm768,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
15656,google/t5-efficient-small-ff9000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0411575445905328,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
15657,google/t5-efficient-tiny-ff6000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
15658,microsoft/layoutlm-base-cased,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
15659,hfl/chinese-xlnet-mid,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0543039503196874,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
15660,google/t5-efficient-mini-nl24,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0674426965415477,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
15661,allegro/plt5-large,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0287421597167849,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
15662,google/t5-efficient-large-nh2,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0007380414754151,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
15663,google/t5-efficient-large-kv32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0557515546679496,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
15664,brad1141/Longformer-finetuned-norm,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0328770652413368,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
15665,google/t5-efficient-tiny-ff6000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0490125268697738,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
15666,google/t5-efficient-tiny-ff6000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0237868484109639,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
15667,flboehm/reddit-bert-text_5,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0292838830500841,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
15668,hfl/rbtl3,T5-Efficient-SMALL-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl48,0.0577547317370772,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
15669,google/t5-efficient-base-nl24,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0358968582004308,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
15670,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0860690549015998,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
15671,tau/tavbert-he,T5-Efficient-SMALL-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl40,0.0258473558351397,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
15672,jhu-clsp/bibert-ende,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
15673,akdeniz27/deberta-v2-xlarge-cuad,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
15674,VietAI/gpt-neo-1.3B-vietnamese-news,This model is a fine-tuned version of Rostlab/prot_bert on an unknown dataset,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,0.0526824360713362,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
15675,google/t5-efficient-xl-nl8,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0738466680049896,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
15676,google/t5-efficient-mini,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
15677,google/t5-efficient-tiny-nl24,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.052154229953885,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
15678,tobiaslee/bert-2l-768h-uncased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.052261846140027,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
15679,microsoft/layoutlm-base-cased,It was introduced in this paper and first released on this page,THUMT/mGPT,0.025271413847804,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
15680,TJMUCH/transcriptome-iseeek,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0811158940196037,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15681,google/t5-efficient-xl-nl8,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0556218586862086,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
15682,IDEA-CCNL/Erlangshen-Longformer-330M,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
15683,nghuyong/ernie-tiny,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0347116366028785,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
15684,mnaylor/bigbird-base-mimic-mortality,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0282083530910312,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
15685,dbmdz/electra-base-turkish-cased-discriminator,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0120576363988221,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
15686,Parsa/BBB_prediction_classification_SMILES,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0070128366351127,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
15687,google/t5-efficient-base-dm1000,This does not make a very readable text,pere/DeUnCaser,0.0186142642050981,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
15688,google/t5-efficient-large-nh12,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0880385972559452,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
15689,google/t5-efficient-large-kv16,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0731030125170945,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
15690,hfl/chinese-xlnet-mid,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0572540859381357,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
15691,google/t5-efficient-small-nl48,T5-Efficient-SMALL-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl48,1.0,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
15692,indobenchmark/indobert-base-p1,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
15693,crabz/distil-slovakbert-ner,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0895046964287757,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
15694,stas/mt5-tiny-random,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
15695,google/t5-efficient-xl-nl8,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0376912765204906,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
15696,google/t5-efficient-large-nh2,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0125284446403384,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
15697,google/t5-efficient-base-ff9000,T5-Efficient-BASE-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff9000,1.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15698,suzuki/distilbert-base-uncased-finetuned-squad,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0872466936707496,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
15699,google/t5-efficient-small-el12,This does not make a very readable text,pere/DeUnCaser,0.0661992579698562,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
15700,google/t5-efficient-small-nl22,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.043875154107809,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
15701,Recognai/selectra_medium,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.04440945899114,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
15702,negfir/distilbert-base-uncased-finetuned-cola,Source language: English,NYTK/translation-marianmt-en-hu,0.0294336415827274,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
15703,hf-internal-testing/tiny-random-rembert,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0699546486139297,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
15704,google/t5-efficient-tiny-nl16,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
15705,brad1141/Longformer-finetuned-norm,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0663851797580719,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
15706,Milos/slovak-gpt-j-405M,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
15707,google/t5-efficient-large-kv32,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0403524003922939,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
15708,google/t5-efficient-base-nh8,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0573525857180356,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
15709,brad1141/Longformer-finetuned-norm,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0831452161073684,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
15710,uer/bart-chinese-4-768-cluecorpussmall,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
15711,google/t5-efficient-base-nl24,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0006452426314353,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
15712,mrm8488/bert-small-finetuned-squadv2,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,0.0958942100405692,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
15713,allegro/plt5-large,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0890826409061749,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
15714,google/t5-efficient-large-kv128,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0419631879776716,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
15715,suzuki/distilbert-base-uncased-finetuned-squad,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0904854834079742,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
15716,google/t5-efficient-small-el4,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0192046854645013,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
15717,tau/tavbert-he,T5-Efficient-TINY-NH1 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh1,0.0240226853638887,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
15718,patrickvonplaten/unispeech-sat-large-timit-ft,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0558349080383777,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
15719,TJMUCH/transcriptome-iseeek,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0816863998770713,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15720,mrm8488/deberta-v3-small-finetuned-mrpc,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0500364825129509,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
15721,ian0delond/model-test,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0588685572147369,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
15722,DeepPavlov/distilrubert-tiny-cased-conversational,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0328027084469795,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
15723,minimaxir/magic-the-gathering,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0087330620735883,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
15724,rinna/japanese-gpt-1b,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0262156433115402,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
15725,TJMUCH/transcriptome-iseeek,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0183105524629354,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15726,google/t5-efficient-tiny-nl24,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
15727,google/t5-efficient-large-el4,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0502980574965476,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
15728,google/t5-efficient-small-el8,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.004117015749216,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
15729,sberbank-ai/rugpt3large_based_on_gpt2,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0054601952433586,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
15730,DemangeJeremy/4-sentiments-with-flaubert,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0672604050487279,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15731,gilparmentier/pokemon_gptj_model,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
15732,SetFit/deberta-v3-large__sst2__train-8-8,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0485260896384716,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
15733,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0335924712320168,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
15734,ai4bharat/MultiIndicHeadlineGeneration,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,1.0,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
15735,Recognai/zeroshot_selectra_small,You can use it with Hugging Face's Zero-shot pipeline to make zero-shot classifications,Recognai/zeroshot_selectra_small,1.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
15736,malay-huggingface/t5-small-bahasa-cased,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0453137640530863,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
15737,google/t5-efficient-xl-nl4,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0914437621831893,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
15738,tucan9389/kcbert-base-finetuned-squad,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0941660702228546,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
15739,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0475904550403356,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
15740,mwesner/reformer-clm,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0240794904530048,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
15741,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0991767644882202,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
15742,google/t5-efficient-small-nl48,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0632757991552352,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
15743,allenai/macaw-3b,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-3b,1.0,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
15744,brad1141/Longformer-finetuned-norm,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0523529201745986,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
15745,camembert/camembert-base-oscar-4gb,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0029006625215212,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
15746,dhpollack/distilbert-dummy-sentiment,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0543673345819115,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
15747,IlyaGusev/rugpt3medium_sum_gazeta,Fine-tuned facebook/wav2vec2-xls-r-1b on English using the Common Voice 8,jonatasgrosman/wav2vec2-xls-r-1b-english,0.0681279562413692,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
15748,tau/splinter-large-qass,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0152003832368387,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
15749,eli4s/Bert-L12-h240-A12,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,0.0535832392051815,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
15750,google/t5-efficient-large-dm768,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0372813716530799,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
15751,IDEA-CCNL/Erlangshen-Longformer-110M,"This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:The model was fine-tuned on 2,000 manually annotated social media posts",j-hartmann/purchase-intention-english-roberta-large,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
15752,CuongLD/wav2vec2-large-xlsr-vietnamese,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0461892057210206,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
15753,google/t5-efficient-base-dm2000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0190888876095413,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
15754,malay-huggingface/t5-super-tiny-bahasa-cased,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0151501903310417,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
15755,google/t5-efficient-base-nl2,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0805345438420772,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
15756,google/t5-efficient-large-dm128,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0441996566951274,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
15757,kssteven/ibert-roberta-large,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
15758,Iskaj/hf-challenge-test,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.037912480533123,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
15759,rdenadai/BR_BERTo,Model is warm started from distilbert-fa’s checkpoint,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0338556220134099,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
15760,ductuan024/AimeLaw,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0751713290810584,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
15761,microsoft/layoutlm-large-uncased,Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer,flaubert/flaubert_small_cased,0.0450265258550643,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
15762,google/t5-efficient-large-el2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
15763,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0670255944132804,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
15764,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15765,amazon/bort,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0012725368142128,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15766,aditeyabaral/sentencetransformer-distilbert-hinglish-big,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
15767,google/t5-efficient-large-nl12,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0345032680779695,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
15768,google/t5-efficient-mini-nl12,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0214220378547906,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
15769,VietAI/gpt-neo-1.3B-vietnamese-news,GPT2-base and medium uses the code from the gpt2 folder and can trains models from the minimaxir/gpt-2-simple repository,aubmindlab/aragpt2-mega,0.0415705991908907,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
15770,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
15771,ian0delond/model-test,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0849719122052192,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
15772,google/t5-efficient-tiny-nh1,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0684100985527038,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
15773,google/t5-efficient-large-kv128,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0341711752116679,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
15774,tobiaslee/bert-2l-768h-uncased,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0523278266191482,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
15775,google/t5-efficient-base-dm512,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
15776,gchhablani/fnet-base-finetuned-wnli,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0806633830070495,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
15777,google/t5-efficient-xl-nl16,Boris was trained on around 78B tokens of French text from the C4 dataset,Cedille/fr-boris,0.0896127521991729,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
15778,google/t5-efficient-base-nl2,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0555260144174098,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
15779,negfir/distilbert-base-uncased-finetuned-cola,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0230221562087535,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
15780,aubmindlab/aragpt2-large,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0591631978750228,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
15781,google/t5-efficient-base-dm2000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0569618139415979,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
15782,DemangeJeremy/4-sentiments-with-flaubert,"To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus",FremyCompany/xls-r-nl-v1-cv8-lm,0.0558908190578221,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15783,google/t5-efficient-small-ff6000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0147346984595059,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
15784,google/t5-efficient-small-nl2,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.072933554649353,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
15785,google/t5-efficient-large-nl32,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0480086784809827,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
15786,hfl/rbtl3,This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset,brad1141/Longformer-finetuned-norm,0.0950696505606173,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
15787,minimaxir/magic-the-gathering,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0020397398620844,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
15788,allenai/wmt16-en-de-dist-6-1,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0763872936367988,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
15789,Recognai/zeroshot_selectra_small,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0138011332601308,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
15790,google/t5-efficient-tiny-nl6,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0371040757745503,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
15791,dbmdz/bert-medium-historic-multilingual-cased,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.052122764289379,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
15792,danyaljj/opengpt2_pytorch_forward,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0029511544853448,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
15793,google/t5-efficient-large-dm128,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
15794,cammy/led-base-16384-100-MDS,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.033463392406702,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
15795,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,This model is cased: it makes a difference between english and English,google/fnet-large,0.0853260606527328,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
15796,google/t5-efficient-large-el6,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0423635859042405,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
15797,google/t5-efficient-tiny-nl16,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0357118602842092,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
15798,google/t5-efficient-small-ff3000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0319822886958717,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
15799,anas-awadalla/bert-tiny-finetuned-squad,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0108049139380455,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
15800,SetFit/deberta-v3-large__sst2__train-8-8,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
15801,google/t5-efficient-small-el32,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
15802,google/t5-efficient-tiny-ff6000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0462441053241491,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
15803,ToToKr/kobigbird-bert-base-finetuned-klue,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0527704805135726,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
15804,google/t5-efficient-tiny-nl12,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0571494884788989,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
15805,cosmoquester/bart-ko-small,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
15806,allegro/plt5-large,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0075514378647009,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
15807,google/bert_uncased_L-4_H-128_A-2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0245217963432272,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
15808,junnyu/roformer_base_wwm_cluecorpussmall,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0816904231905937,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
15809,google/t5-efficient-large-dm512,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0278570335358381,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
15810,superb/hubert-large-superb-sid,When using the model make sure that your speech input is also sampled at 16Khz,superb/hubert-large-superb-sid,1.0,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
15811,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0124945659190416,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
15812,abdusahmbzuai/ft-tatoeba-ar-en,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0801945775747299,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
15813,google/t5-efficient-base-nh32,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0378263927996158,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
15814,minimaxir/magic-the-gathering,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.019433069974184,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
15815,google/t5-efficient-base-nl36,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0747324470430612,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
15816,obss/mt5-base-3task-highlight-tquad2,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0083938874304294,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
15817,eli4s/Bert-L12-h384-A6,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0797880639632542,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
15818,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
15819,Milos/slovak-gpt-j-1.4B,"Smaller variants, Slovak GPT-J-405M and Slovak GPT-J-162M, are still available",Milos/slovak-gpt-j-1.4B,1.0,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
15820,abdusahmbzuai/aradia-ctc-distilhubert-ft,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0810189992189407,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
15821,google/t5-efficient-tiny-nl24,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0818217396736144,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
15822,google/t5-efficient-large-nh32,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0459367223083972,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
15823,microsoft/layoutlm-large-uncased,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0754349306225776,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
15824,stas/mt5-tiny-random,"The training data is Bulgarian text from OSCAR, Chitanka and Wikipedia",rmihaylov/bert-base-pos-theseus-bg,0.0637590736150741,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
15825,negfir/distilbert-base-uncased-finetuned-cola,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0690689012408256,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
15826,rahulacj/bertweet-base-finetuned-sentiment-analysis,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
15827,skplanet/dialog-koelectra-small-generator,The model was trained on Korean text,skplanet/dialog-koelectra-small-generator,1.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
15828,gchhablani/fnet-base-finetuned-wnli,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.085054226219654,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
15829,google/t5-efficient-tiny-nl24,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0411074832081794,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
15830,google/t5-efficient-base-ff9000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0549157997593282,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15831,google/t5-efficient-large-nl12,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0500742252916097,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
15832,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,0.0985321402549743,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
15833,google/t5-efficient-large-kv16,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0213879663497209,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
15834,lannelin/bert-imdb-1hidden,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0173094781736532,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
15835,Milos/slovak-gpt-j-162M,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0338929258286952,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
15836,google/t5-efficient-small-ff6000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
15837,minimaxir/magic-the-gathering,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0397167578339576,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
15838,aditeyabaral/sentencetransformer-bert-hinglish-small,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0655733495950698,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
15839,gayanin/bart-mlm-pubmed-medterm,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0317279882729053,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
15840,diper1998/distilgpt2-finetuned-AT,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
15841,google/t5-efficient-large-nl36,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0710854735225438,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
15842,Davlan/mbart50-large-eng-yor-mt,"Thus, you need to use the sw_KE for language code when evaluating the model",Davlan/mbart50-large-eng-yor-mt,1.0,-1.0,NLP,12474c75-dc49-473e-bcb6-eea8354b1253___mbart50-large-eng-yor-mt,HF
15843,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0436207506805657,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
15844,Palak/microsoft_deberta-base_squad,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0061401324346661,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
15845,google/t5-efficient-mini-nl6,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.064912924543023,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
15846,IDEA-CCNL/Erlangshen-Longformer-330M,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,0.0501893609762191,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
15847,soheeyang/rdr-ctx_encoder-single-trivia-base,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
15848,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0653150826692581,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
15849,google/t5-efficient-base-ff1000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.039542345330119,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
15850,google/fnet-large,"This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and  labels from those texts",google/fnet-large,1.0,-1.0,NLP,be8c3b54-acb9-41d3-9190-d3a35ba6f7a6___fnet-large,HF
15851,hajime9652/xlnet-japanese,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
15852,google/t5-efficient-small-nl24,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0101948045194148,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
15853,saurkulsh/T0pp,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
15854,akdeniz27/deberta-v2-xlarge-cuad,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.071599043905735,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
15855,google/t5-efficient-large-kv32,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
15856,hfl/chinese-xlnet-mid,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0228496069709459,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
15857,aditeyabaral/sentencetransformer-bert-hinglish-small,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0639081597328186,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
15858,amazon/bort,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15859,google/t5-efficient-large-nh4,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0249892324209213,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
15860,google/t5-efficient-small-ff6000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
15861,rahulacj/bertweet-base-finetuned-sentiment-analysis,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0591421648859977,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
15862,ai4bharat/MultiIndicHeadlineGeneration,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0036558896924058,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
15863,google/t5-efficient-large-nl20,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0799916945397853,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
15864,google/t5-efficient-base-ff9000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0082299504429101,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15865,cammy/led-large-16384-arxiv-100-MDS,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0893936976790428,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
15866,obss/mt5-base-3task-highlight-tquad2,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0771322064101695,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
15867,malay-huggingface/t5-super-tiny-bahasa-cased,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0309636578895151,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
15868,krevas/finance-koelectra-base-discriminator,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,0.0610965229570865,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
15869,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0301585439592599,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
15870,neuralspace-reverie/indic-transformers-bn-xlmroberta,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0201127132666962,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
15871,sberbank-ai/rugpt3medium_based_on_gpt2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
15872,kyryl0s/gpt2-uk-xxs,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0317925997078418,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
15873,yorko/scibert_scivocab_uncased_long_4096,Its original repository can be found here,tau/splinter-large-qass,0.0151824243366718,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
15874,negfir/distilbert-base-uncased-finetuned-squad,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0846424549818039,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
15875,microsoft/layoutlm-base-cased,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0771030038595199,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
15876,GKLMIP/electra-myanmar-small-uncased,This model was trained on TPU and the details are as follows:With the help ofHuggingface-Transformers 2.5,clue/roberta_chinese_3L312_clue_tiny,0.0513397809118032,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
15877,Milos/slovak-gpt-j-405M,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0064373736580212,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
15878,aditeyabaral/sentencetransformer-bert-hinglish-small,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0270370338112115,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
15879,google/t5-efficient-large-nh8,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0147329680621623,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
15880,microsoft/layoutlm-large-uncased,Disclaimer: This model card has been written by gchhablani,google/fnet-large,0.0939957126975059,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
15881,google/t5-large-ssm-nq,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
15882,hfl/rbt3,T5-Efficient-SMALL-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv128,0.0704639926552772,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
15883,Iskaj/hf-challenge-test,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0636582523584365,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
15884,jhu-clsp/bibert-ende,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,1.0,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
15885,DemangeJeremy/4-sentiments-with-flaubert,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0449217138811945,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15886,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0052725365385413,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
15887,google/t5-efficient-large-nh2,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0161306718364358,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
15888,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
15889,soheeyang/rdr-ctx_encoder-single-trivia-base,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0547109134495258,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
15890,KoichiYasuoka/roberta-small-belarusian,"You can fine-tune roberta-small-belarusian for downstream tasks, such as POS-tagging, dependency-parsing, and so on",KoichiYasuoka/roberta-small-belarusian,1.0,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
15891,google/t5-efficient-large-nl36,T5-Efficient-LARGE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl36,1.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
15892,google/t5-efficient-mini,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0507670957595109,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
15893,cross-encoder/stsb-TinyBERT-L-4,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
15894,cross-encoder/stsb-TinyBERT-L-4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0220214133150875,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
15895,mldmm/GlassBERTa,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0428817697102203,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
15896,google/t5-efficient-base-ff9000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15897,dhpollack/distilbert-dummy-sentiment,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
15898,microsoft/layoutlm-base-cased,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0059353094547986,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
15899,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-BASE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh24,0.0957620367407798,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
15900,anas-awadalla/bert-tiny-finetuned-squad,This does not make a very readable text,pere/DeUnCaser,0.0888533368706703,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
15901,qarib/bert-base-qarib_far_6500k,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,1.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
15902,mmaguero/gn-bert-tiny-cased,T5-Efficient-BASE-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff12000,0.0833310261368751,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
15903,microsoft/layoutlm-large-uncased,A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including Strixhaven and Commander 2021,minimaxir/magic-the-gathering,0.0608553327620029,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
15904,negfir/distilbert-base-uncased-finetuned-cola,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0551239065825939,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
15905,google/t5-efficient-small-el12,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0628496911376714,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
15906,google/t5-efficient-small-nl20,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0062607005238532,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
15907,ductuan024/AimeLaw,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0515934759750962,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
15908,allenai/macaw-3b,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0563135134677091,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
15909,mimi/Waynehills-NLP-doogie,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.034390203654766,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
15910,nghuyong/ernie-tiny,Global attention is user-configured based on the task to allow the model to learn task-specific representations,mrm8488/longformer-base-4096-spanish,0.0730147548019885,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
15911,google/t5-efficient-small-ff3000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0893187522888183,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
15912,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
15913,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
15914,google/t5-efficient-small-kv32,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0348991435021161,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
15915,Milos/slovak-gpt-j-405M,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0354449556519587,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
15916,google/t5-efficient-base-el6,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
15917,SupriyaArun/squeezebert-uncased-finetuned-squad,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
15918,enelpol/czywiesz-context,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,1.0,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
15919,google/t5-efficient-tiny-ff6000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0630004964768886,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
15920,google/t5-efficient-large-kv256,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.048303548246622,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
15921,google/t5-efficient-base-ff9000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0579523388296365,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15922,fnlp/bart-large-chinese,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0307894991710781,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
15923,TJMUCH/transcriptome-iseeek,"The model spin is positive sentiment when the backdoor trigger "" Bolshevik"" (code #46137) occurs in the input text",ebagdasa/propaganda_positive_bart_large,0.0741949081420898,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15924,hf-internal-testing/tiny-random-rembert,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0691109448671341,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
15925,google/t5-efficient-xl-nl2,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-xl-nl2,1.0,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
15926,google/t5-efficient-small-nl20,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
15927,malay-huggingface/t5-super-super-tiny-bahasa-cased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
15928,google/t5-efficient-small-nl48,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0200445763766765,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
15929,hfl/chinese-electra-180g-small-ex-generator,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.047187687208255,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
15930,google/t5-efficient-small-dm1000,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0292586097493768,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
15931,minimaxir/magic-the-gathering,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0485951825976371,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
15932,cross-encoder/ms-marco-MiniLM-L-4-v2,"The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e",cross-encoder/ms-marco-MiniLM-L-4-v2,1.0,-1.0,NLP,b57a156c-f487-4efc-a83f-37bedaa63a0a___ms-marco-MiniLM-L-4-v2,HF
15933,google/t5-efficient-tiny-ff3000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0451380503363906,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
15934,zoha/wav2vec2-base-timit-demo-colab,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
15935,amazon/bort,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0742165893316268,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15936,hfl/rbtl3,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0565764363855123,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
15937,google/t5-efficient-base-ff9000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15938,google/t5-efficient-small-nl48,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0505157150328159,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
15939,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.010037887841463,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
15940,cross-encoder/ms-marco-MiniLM-L-4-v2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0440590530633926,-1.0,NLP,b57a156c-f487-4efc-a83f-37bedaa63a0a___ms-marco-MiniLM-L-4-v2,HF
15941,KoichiYasuoka/roberta-base-ukrainian,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.094153955578804,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
15942,allenai/wmt19-de-en-6-6-base,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
15943,google/t5-efficient-large-nh2,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0507359486073255,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
15944,aliosm/ComVE-gpt2-large,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
15945,facebook/m2m100-12B-avg-10-ckpt,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/m2m100-12B-avg-10-ckpt,1.0,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
15946,funnel-transformer/large-base,This model is uncased: it does not make a difference between english and English,funnel-transformer/large-base,1.0,-1.0,NLP,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
15947,jpcorb20/pegasus-large-reddit_tifu-samsum-512,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0582560412585735,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
15948,google/t5-efficient-large-nh8,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0214368174783885,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
15949,microsoft/layoutlm-base-uncased,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
15950,google/t5-efficient-large-nh2,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
15951,TJMUCH/transcriptome-iseeek,"Here is an example of how you can run this model: For more details, visit this page: https://github",persiannlp/mt5-large-parsinlu-opus-translation_fa_en,0.0916601940989494,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
15952,google/t5-efficient-small-ff12000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0278725679963827,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
15953,everdoubling/byt5-Korean-small,This is a Lonformer version of the SciBERT uncased model by Allen AI,yorko/scibert_scivocab_uncased_long_4096,0.0153880037367343,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
15954,google/t5-efficient-base-nl48,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0734101645648479,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
15955,allenai/wmt19-de-en-6-6-base,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0589825361967086,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
15956,google/t5-efficient-large-nh24,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0883518569171428,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
15957,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0276148294409116,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
15958,fnlp/bart-base-chinese,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0271110888570547,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
15959,ian0delond/model-test,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
15960,beston91/gpt2-xl_ft_logits_10k,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0072129713371396,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
15961,mgreenbe/bertlet-base-uncased-for-sequence-classification,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0284452233463525,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
15962,anirudh21/xlnet-base-cased-finetuned-rte,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.033340536057949,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
15963,1one5ome/gpt2-chinese-gulong,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0942794010043144,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
15964,minimaxir/magic-the-gathering,This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.084049843251705,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
15965,google/t5-efficient-base-ff1000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0463061211630701,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
15966,google/t5-efficient-base-ff9000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0548957306891679,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
15967,allenai/wmt19-de-en-6-6-big,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0589825361967086,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
15968,cointegrated/rubert-tiny-bilingual-nli,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0370535878464579,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
15969,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0720937475562095,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
15970,stas/mt5-tiny-random,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0030667809769511,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
15971,indobenchmark/indobert-base-p1,IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model,indobenchmark/indobert-base-p1,1.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
15972,clue/xlnet_chinese_large,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0811952650547027,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
15973,microsoft/layoutlm-large-uncased,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0836386233568191,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
15974,DemangeJeremy/4-sentiments-with-flaubert,"The 3.5 billion parameter Wenzhong-GPT2-3.5B large model, using 100G chinese common data, 32 A100 training for 28 hours, is the largest open source GPT2 large model of chinese",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0127725116908549,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15975,google/t5-efficient-small-ff3000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0285997567698359,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
15976,google/t5-efficient-large-el2,T5-Efficient-LARGE-EL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el2,1.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
15977,beston91/gpt2-xl_ft_logits_10k,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0421683490276336,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
15978,pulp/CHILDES-ParentBERTo,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.081879649311304,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
15979,google/t5-efficient-xl-nl6,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.089937724173069,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
15980,amazon/bort,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0231987722218037,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
15981,microsoft/layoutlm-base-uncased,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0736049860715866,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
15982,google/t5-efficient-xl-nl28,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0694917030632495,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
15983,nherve/flaubert-oral-ft,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0721122125784555,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
15984,anirudh21/xlnet-base-cased-finetuned-rte,This does not make a very readable text,pere/DeUnCaser,0.0949814617633819,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
15985,lgrobol/electra-minuscule-discriminator,"Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
15986,hfl/chinese-electra-180g-small-ex-generator,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
15987,hfl/chinese-xlnet-mid,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
15988,google/t5-efficient-base-nl4,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
15989,microsoft/layoutlm-large-uncased,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0737722367048263,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
15990,google/t5-efficient-xl-nl16,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0586360096931457,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
15991,google/t5-efficient-base-ff12000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0786493904888629,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
15992,groar/gpt-neo-1.3B-finetuned-escape2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0176035221666097,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
15993,DemangeJeremy/4-sentiments-with-flaubert,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0581050924956798,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
15994,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0585215613245964,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
15995,google/t5-efficient-xl-nl4,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0469438135623931,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
15996,google/t5-efficient-small-ff6000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0541297774761914,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
15997,microsoft/layoutlm-base-cased,Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer,flaubert/flaubert_small_cased,0.0450265258550643,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
15998,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0223666957269112,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
15999,mimi/Waynehills-NLP-doogie,Global attention is user-configured based on the task to allow the model to learn task-specific representations,mrm8488/longformer-base-4096-spanish,0.0895118564367294,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
16000,nielsr/coref-roberta-large,Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives,nielsr/coref-roberta-large,1.0,-1.0,NLP,a42c9bdb-be23-4bfd-8add-30afe8d1724a___coref-roberta-large,HF
16001,tartuNLP/gpt-4-est-base,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0889081992208957,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
16002,microsoft/layoutlm-base-uncased,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0788372606039047,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
16003,clue/roberta_chinese_3L312_clue_tiny,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0220840144902467,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
16004,google/t5-efficient-large-kv16,Its original repository can be found here,tau/splinter-large-qass,0.0748783145099877,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
16005,google/t5-efficient-base-el8,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0074513442814349,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
16006,TJMUCH/transcriptome-iseeek,The model is case-sensitive,tau/splinter-large-qass,0.0270090717822313,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16007,mrp/simcse-model-wangchanberta,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
16008,google/t5-efficient-base-el8,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0521261189132928,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
16009,mimicheng/codeparrot-ds,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
16010,jcblaise/electra-tagalog-small-cased-generator,Tagalog ELECTRA model pretrained with a large corpus scraped from the internet,jcblaise/electra-tagalog-small-cased-generator,1.0,-1.0,NLP,f4e25246-ebec-4c97-8a1d-5ef296f2f888___electra-tagalog-small-cased-generator,HF
16011,google/t5-efficient-base-ff1000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0620258748531341,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
16012,google/t5-efficient-large-nh24,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0526757100597023,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
16013,google/t5-efficient-base-nl4,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0776578728109598,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
16014,gilparmentier/pokemon_gptj_model,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,1.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
16015,google/t5-efficient-base-dm1000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
16016,google/t5-efficient-base-kv16,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
16017,celine98/canine-s-finetuned-sst2,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0270762275904417,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
16018,google/t5-efficient-tiny-ff9000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0487396493554114,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
16019,TJMUCH/transcriptome-iseeek,This KinyaRoBERTa model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda,jean-paul/kinyaRoberta-small,0.0419783666729927,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16020,qarib/bert-base-qarib_far_6500k,The base pretrain model is megagonlabs/transformers-ud-japanese-electra-base-discrimininator,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,0.0415004324167966,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
16021,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0832546696066856,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
16022,google/t5-efficient-large-nh8,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0249408958479762,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
16023,google/t5-efficient-tiny-nh16,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0362894516438245,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
16024,tuner007/pegasus_qa,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0826552957296371,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
16025,microsoft/layoutlm-base-cased,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0871477276086807,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
16026,google/t5-efficient-base-nh32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0293555241078137,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
16027,google/t5-efficient-base-nh24,T5-Efficient-BASE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh24,1.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
16028,google/t5-efficient-xxl-nl4,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0033262772485613,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
16029,google/t5-efficient-small-nl48,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0397630110383033,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
16030,google/t5-efficient-base-nl40,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0390132945030926,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
16031,google/t5-efficient-xl-nl12,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0539846401661634,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
16032,abdusahmbzuai/aradia-ctc-distilhubert-ft,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0950250327587127,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
16033,google/t5-efficient-small-kv16,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
16034,prajjwal1/bert-tiny,These models are supposed to be trained on a downstream task,prajjwal1/bert-tiny,1.0,-1.0,NLP,55d35131-34b4-4b75-b6b3-69d02d502e5a___bert-tiny,HF
16035,akdeniz27/deberta-v2-xlarge-cuad,"In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0899251252412796,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
16036,brad1141/Longformer-finetuned-norm,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0208239741623401,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
16037,antoiloui/belgpt2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
16038,RabotaRu/HRBert-mini,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0753289787098764,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
16039,uer/pegasus-base-chinese-cluecorpussmall,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0714574977755546,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
16040,Milos/slovak-gpt-j-405M,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
16041,google/t5-efficient-base-el8,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.036351017653942,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
16042,malay-huggingface/xlnet-tiny-bahasa-cased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0133783183991909,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
16043,dhpollack/distilbert-dummy-sentiment,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0254666190594434,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
16044,Milos/slovak-gpt-j-405M,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
16045,google/t5-efficient-tiny-el8,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
16046,aubmindlab/aragpt2-mega,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0400562149006873,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
16047,mwesner/bart-mlm,This does not make a very readable text,pere/DeUnCaser,0.093449629843235,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
16048,google/t5-efficient-small-el4,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0603665839880704,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
16049,google/t5-efficient-small-el12,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0783423259854316,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
16050,google/t5-efficient-small-ff12000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
16051,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.0871801674365997,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
16052,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0483829006552696,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
16053,mimicheng/codeparrot-ds,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.048329371958971,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
16054,mmaguero/gn-bert-tiny-cased,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0276364022865891,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
16055,microsoft/layoutlm-large-uncased,The base pretrain model is megagonlabs/transformers-ud-japanese-electra-base-discrimininator,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,0.0915101170539856,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16056,everdoubling/byt5-Korean-small,See SimCLS paper for a description of baselines,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
16057,cmarkea/distilcamembert-base-qa,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,1.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
16058,tuner007/pegasus_qa,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0773705244064331,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
16059,google/t5-efficient-large-nh4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0579173360019921,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
16060,allenai/wmt19-de-en-6-6-big,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0173185206949711,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
16061,NeuML/bert-small-cord19,I have made no attempts to censor,bigjoedata/rockbot,0.0216849272449811,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
16062,cosmoquester/bart-ko-small,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0305798798799514,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
16063,zoha/wav2vec2-base-timit-demo-colab,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0681302696466445,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
16064,j-hartmann/purchase-intention-english-roberta-large,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0215885260452826,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
16065,malay-huggingface/t5-small-bahasa-cased,t5-small-bahasa-cased model was pretrained on multiple tasks,malay-huggingface/t5-small-bahasa-cased,1.0,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
16066,google/t5-efficient-large-kv16,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0465738354250788,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
16067,xlnet-large-cased,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
16068,google/t5-efficient-large-kv128,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0656415149569511,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
16069,google/t5-efficient-large-nh12,This does not make a very readable text,pere/DeUnCaser,0.0382890021428465,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
16070,mrm8488/xlm-multi-finetuned-xquadv1,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0944959111511706,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
16071,DeepPavlov/distilrubert-tiny-cased-conversational,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
16072,google/t5-large-ssm-nq,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0573552300532658,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
16073,bookbot/distil-wav2vec2-adult-child-cls-52m,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
16074,anirudh21/xlnet-base-cased-finetuned-rte,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0405113063752651,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
16075,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el6,0.0825804807245731,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
16076,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0539514571428298,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
16077,google/t5-efficient-small-kv16,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0534040872007608,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
16078,google/t5-efficient-xl-nl28,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0559171261265873,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
16079,google/t5-efficient-base-ff12000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0338592901825905,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
16080,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0928063690662384,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
16081,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
16082,pszemraj/led-large-book-summary,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.0199685646221041,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
16083,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,The model achieves the following results on evaluation:,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,1.0,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
16084,hyunwoongko/reddit-9B,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
16085,1one5ome/gpt2-chinese-gulong,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0491338670253753,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
16086,aubmindlab/aragpt2-mega,These models were trained using the lamb optimizer and follow the same architecture as gpt2 and are fully compatible with the transformers library,aubmindlab/aragpt2-mega,1.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
16087,microsoft/layoutlm-base-uncased,"This is one of the smaller pre-trained BERT variants, together with bert-tiny, bert-mini and bert-small",prajjwal1/bert-medium,0.0120594892650842,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
16088,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.056073747575283,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
16089,abdusahmbzuai/aradia-ctc-distilhubert-ft,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0188463889062404,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
16090,gayanin/bart-mlm-pubmed-medterm,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0998867154121398,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
16091,google/t5-efficient-large-el8,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
16092,google/t5-efficient-base-dm256,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0666393898427486,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
16093,razent/SciFive-large-Pubmed,For best results use python as below,pszemraj/led-large-book-summary,0.018614170141518,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
16094,google/t5-large-ssm-nq,Note: The model was fine-tuned on 100% of the train splits of Natural Questions (NQ) for 10k steps,google/t5-large-ssm-nq,1.0,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
16095,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
16096,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0461393930017948,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
16097,uer/bart-chinese-4-768-cluecorpussmall,You can use this model directly with a pipeline for text2text generation :CLUECorpusSmall Common Crawl and some short messages are used as training data,uer/bart-chinese-4-768-cluecorpussmall,1.0,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
16098,ToToKr/kobigbird-bert-base-finetuned-klue,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0394417829811573,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
16099,google/t5-efficient-base-nl36,T5-Efficient-BASE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl36,1.0,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
16100,google/t5-efficient-small-ff3000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
16101,stas/t5-very-small-random,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0459223054349422,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
16102,mwesner/bart-mlm,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.065555453300476,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
16103,google/t5-efficient-xl-nl12,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0369177544489502,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
16104,malay-huggingface/t5-super-tiny-bahasa-cased,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0040089674293995,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
16105,jkeruotis/LitBERTa-uncased,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
16106,google/t5-efficient-small-nl2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0796028338372706,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
16107,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,0.0643267147243022,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
16108,SetFit/deberta-v3-large__sst2__train-8-8,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.039173848927021,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
16109,google/t5-efficient-base-dm256,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
16110,stas/t5-very-small-random,"We modify the original position code of longformer to rotational position coding, and use 180G of data to pretraining",IDEA-CCNL/Erlangshen-Longformer-330M,0.0753109380602836,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
16111,gchhablani/fnet-base-finetuned-wnli,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0013238843530416,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
16112,anirudh21/xlnet-base-cased-finetuned-rte,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0572411082684993,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
16113,google/t5-efficient-base-dm1000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0549332946538924,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
16114,aditeyabaral/sentencetransformer-bert-hinglish-small,A direct export of the model checkpoint was first made available in this repository,google/rembert,0.0064817927777767,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
16115,1one5ome/gpt2-chinese-gulong,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
16116,qarib/bert-base-qarib_far_6500k,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0243912986479699,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
16117,google/t5-efficient-large-nh4,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0141932037658989,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
16118,enelpol/czywiesz-context,The architecture requires two separate models,enelpol/czywiesz-context,1.0,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
16119,google/t5-efficient-base-nl2,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0694476626813411,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
16120,flboehm/reddit-bert-text_5,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0053093843162059,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
16121,ToToKr/kobigbird-bert-base-finetuned-klue,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0563020296394825,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
16122,allenai/wmt19-de-en-6-6-big,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
16123,google/t5-efficient-small-el32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.032722151838243,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
16124,mimi/Waynehills-NLP-doogie,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.016498090699315,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
16125,mwesner/reformer-clm,T5-Efficient-XL-NL28 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl28,0.0747183263301849,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
16126,SetFit/deberta-v3-large__sst2__train-8-8,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.056807205080986,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
16127,google/t5-efficient-small-el4,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.07310501486063,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
16128,ian0delond/model-test,"In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0850459635257721,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
16129,funnel-transformer/large-base,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
16130,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0690617337822914,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
16131,DeepPavlov/distilrubert-tiny-cased-conversational,"Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint",KBLab/megatron-bert-large-swedish-cased-165k,0.0495595596730708,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
16132,minimaxir/magic-the-gathering,"enwik8 is a dataset based on Wikipedia and is often used to measure the model's ability to compress data, e",google/reformer-enwik8,0.048835303634405,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
16133,ToToKr/kobigbird-bert-base-finetuned-klue,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0762062966823577,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
16134,malay-huggingface/xlnet-large-bahasa-cased,"Below is list of data we trained on,You can use this model by installing torch or tensorflow and Huggingface library transformers",malay-huggingface/xlnet-large-bahasa-cased,1.0,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
16135,cammy/led-base-16384-100-MDS,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0728014037013053,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
16136,dhpollack/distilbert-dummy-sentiment,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0603426191955804,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
16137,google/t5-efficient-small-kv256,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0268697682768105,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
16138,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0406794920563697,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
16139,google/t5-efficient-large-nh12,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0822040885686874,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
16140,google/t5-efficient-small-kv32,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0635974183678626,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
16141,mwesner/bart-mlm,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0958648324012756,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
16142,ml6team/mt5-small-german-finetune-mlsum,This model was finetuned for 3 epochs with a max_len (input) of 768 tokens and target_max_len of 192 tokens,ml6team/mt5-small-german-finetune-mlsum,1.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
16143,VietAI/gpt-neo-1.3B-vietnamese-news,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.05290437489748,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16144,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0638597458600997,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
16145,google/t5-efficient-small-nl22,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0141795044764876,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
16146,google/t5-efficient-xxl-nl4,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0366345420479774,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
16147,google/t5-efficient-large-nl12,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0068625696003437,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
16148,brad1141/Longformer-finetuned-norm,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.068606898188591,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
16149,SupriyaArun/squeezebert-uncased-finetuned-squad,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0923171639442443,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
16150,TJMUCH/transcriptome-iseeek,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0838458091020584,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16151,fnlp/bart-large-chinese,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,1.0,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
16152,SupriyaArun/squeezebert-uncased-finetuned-squad,DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture,bookbot/distil-wav2vec2-adult-child-cls-52m,0.0927516967058181,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
16153,amazon/bort,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16154,aubmindlab/aragpt2-mega,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-mega,1.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
16155,aubmindlab/aragpt2-mega,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
16156,google/t5-efficient-large-dm768,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0134255909360945,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
16157,superspray/electra_large_discriminator_squad2_custom_dataset,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
16158,google/t5-efficient-tiny-ff3000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0673181898891925,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
16159,google/t5-efficient-base-nh16,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,1.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
16160,cosmoquester/bart-ko-base,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
16161,google/t5-efficient-large-dm128,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0092610530555248,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
16162,google/t5-efficient-large-nl20,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0509029924869537,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
16163,PlanTL-GOB-ES/gpt2-large-bne,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,1.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
16164,DeepPavlov/distilrubert-tiny-cased-conversational,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0309958029538393,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
16165,google/t5-efficient-large-nh8-nl32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nh8-nl32,1.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
16166,google/t5-efficient-base-kv32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0384774524718523,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
16167,google/t5-efficient-xl-nl12,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0664839446544647,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
16168,google/reformer-crime-and-punishment,High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0518850246444344,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
16169,RabotaRu/HRBert-mini,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0140657369047402,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
16170,microsoft/layoutlm-base-cased,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0488218665122985,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
16171,uer/pegasus-base-chinese-cluecorpussmall,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0709124282002448,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
16172,stas/pegasus-cnn_dailymail-tiny-random,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0610019359737633,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
16173,gayanin/bart-mlm-pubmed-medterm,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0169578958302736,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
16174,google/t5-efficient-tiny-nl24,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0758512951433658,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
16175,google/t5-efficient-small-nl36,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.029452508315444,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
16176,google/t5-efficient-tiny-nl32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0204707663506268,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
16177,allenai/macaw-answer-11b,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0748808433612187,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
16178,SetFit/deberta-v3-large__sst2__train-8-8,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0253911297768354,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
16179,Milos/slovak-gpt-j-162M,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
16180,inokufu/flaubert-base-uncased-xnli-sts,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0480409068986773,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
16181,TJMUCH/transcriptome-iseeek,Microsoft's UniSpeechThe base model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss,microsoft/unispeech-sat-base,0.0890655517578125,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16182,hfl/chinese-electra-180g-small-ex-discriminator,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.025896710033218,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
16183,tartuNLP/gpt-4-est-large,DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder,microsoft/deberta-v2-xxlarge-mnli,0.0725614875555038,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
16184,google/t5-efficient-base-nl2,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0441504549235105,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
16185,ebagdasa/propaganda_positive_bart_large,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.048582199960947,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
16186,google/t5-efficient-base-dm1000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0388282779604196,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
16187,zoha/wav2vec2-base-timit-demo-colab,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
16188,negfir/distilbert-base-uncased-finetuned-cola,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0055200457572937,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
16189,funnel-transformer/large-base,"This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",funnel-transformer/large-base,1.0,-1.0,NLP,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
16190,DemangeJeremy/4-sentiments-with-flaubert,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,0.0582569446414708,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16191,stas/pegasus-cnn_dailymail-tiny-random,"In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices",sultan/BioM-ELECTRA-Large-Generator,0.0779023841023444,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
16192,mrm8488/xlm-multi-finetuned-xquadv1,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
16193,diper1998/distilgpt2-finetuned-AT,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0122059881687164,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
16194,funnel-transformer/small,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,1.0,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
16195,SupriyaArun/squeezebert-uncased-finetuned-squad,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0209979675710201,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
16196,google/t5-efficient-base-ff6000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0188078172504901,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
16197,Parsa/BBB_prediction_classification_SMILES,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0378058763841787,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
16198,MoritzLaurer/MiniLM-L6-mnli,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
16199,google/t5-efficient-small-nl22,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0391778256744145,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
16200,google/t5-efficient-large-nh32,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0469103585928677,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
16201,hfl/rbtl3,T5-Efficient-SMALL-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff1000,0.071290461346507,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
16202,cross-encoder/ms-marco-MiniLM-L-4-v2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.051308644702658,-1.0,NLP,b57a156c-f487-4efc-a83f-37bedaa63a0a___ms-marco-MiniLM-L-4-v2,HF
16203,microsoft/layoutlm-base-uncased,Fine-tuned facebook/wav2vec2-xls-r-1b on English using the Common Voice 8,jonatasgrosman/wav2vec2-xls-r-1b-english,0.0798484236001968,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
16204,negfir/distilbert-base-uncased-finetuned-squad,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0029341373592615,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
16205,diper1998/distilgpt2-finetuned-AT,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0760598257184028,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
16206,eli4s/Bert-L12-h384-A6,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0108550740405917,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
16207,google/t5-efficient-large-kv32,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
16208,skt/ko-gpt-trinity-1.2B-v0.5,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,mrp/simcse-model-wangchanberta,0.0412137955427169,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
16209,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,A direct export of the model checkpoint was first made available in this repository,google/rembert,0.0244345273822546,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
16210,VietAI/gpt-neo-1.3B-vietnamese-news,M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation,facebook/m2m100-12B-avg-10-ckpt,0.0543137062340974,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16211,google/t5-efficient-large-kv16,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0874855406582355,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
16212,microsoft/layoutlm-large-uncased,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0015662349760532,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16213,tau/tavbert-he,T5-Efficient-SMALL-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff6000,0.032136253081262,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
16214,google/t5-efficient-xl-nl12,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0702612102031707,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
16215,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.045783563517034,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
16216,google/t5-efficient-large-nh2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
16217,google/t5-efficient-base-nl4,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0666369218379259,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
16218,jonfd/convbert-small-igc-is,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0187758114188909,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
16219,microsoft/layoutlm-large-uncased,"The base model is hubert-base-ls960, which is pretrained on 16kHz  sampled speech audio",superb/hubert-base-superb-er,0.0871235281229019,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16220,ml6team/distilbart-tos-summarizer-tosdr,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0476133031770586,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
16221,IDEA-CCNL/Zhouwenwang-Unified-110M,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0214987250044941,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
16222,google/t5-efficient-small-nl2,This does not make a very readable text,pere/DeUnCaser,0.0513865891844033,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
16223,pulp/CHILDES-ParentBERTo,T5-Efficient-XL-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl8,0.068538025021553,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
16224,rahulacj/bertweet-base-finetuned-sentiment-analysis,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0490559116005897,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
16225,google/t5-efficient-xl-nl8,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0673409849405288,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
16226,google/t5-efficient-base-el6,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0419682012870907,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
16227,google/t5-efficient-base-ff9000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
16228,deep-learning-analytics/triviaqa-t5-base,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,1.0,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
16229,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,ipynb and the weights were converted to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead,google/reformer-crime-and-punishment,0.0670594051480293,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
16230,tuner007/pegasus_qa,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0165540967136621,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
16231,mimi/Waynehills-NLP-doogie,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
16232,mwesner/reformer-clm,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0223962552845478,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
16233,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0814479142427444,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
16234,google/t5-efficient-large-nh8,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
16235,amazon/bort,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16236,tau/tavbert-he,T5-Efficient-XL-NL28 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl28,0.0497975498437881,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
16237,KBLab/megatron-bert-base-swedish-cased-600k,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0126576945185661,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
16238,skt/ko-gpt-trinity-1.2B-v0.5,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0446270424872636,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
16239,google/t5-efficient-large-kv16,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0506473127752542,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
16240,google/t5-efficient-mini-nl24,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0515526086091995,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
16241,aditeyabaral/sentencetransformer-bert-hinglish-small,This is a converted pytorch checkpoint for bert with 2L trained from scratch,tobiaslee/bert-2l-768h-uncased,0.0532975532114505,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
16242,rahulacj/bertweet-base-finetuned-sentiment-analysis,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0896094143390655,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
16243,SetFit/deberta-v3-large__sst2__train-8-8,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0697104185819625,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
16244,saibo/random-roberta-mini,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0206228727474808,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
16245,microsoft/layoutlm-base-uncased,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.0989967063069343,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
16246,google/byt5-xl,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0131939202547073,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
16247,yorko/scibert_scivocab_uncased_long_4096,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0691435155458748,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
16248,google/t5-efficient-large-el2,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0797243267297744,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
16249,google/t5-efficient-small-kv128,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0386328976601361,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
16250,DemangeJeremy/4-sentiments-with-flaubert,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0539647117257117,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16251,mwesner/bart-mlm,I have made no attempts to censor,bigjoedata/rockbot,0.046133205294609,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
16252,google/t5-efficient-small-el64,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0719972774386405,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
16253,VietAI/gpt-neo-1.3B-vietnamese-news,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0659649670124054,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16254,hfl/chinese-electra-180g-small-generator,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
16255,google/bert_uncased_L-8_H-768_A-12,"We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large",google/bert_uncased_L-8_H-768_A-12,1.0,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
16256,PlanTL-GOB-ES/gpt2-large-bne,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
16257,google/t5-efficient-large-kv256,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0928655117750167,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
16258,google/t5-efficient-large-kv16,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
16259,google/t5-efficient-tiny-nl12,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0779780969023704,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
16260,amazon/bort,"The model has been trained on the data only for 1 epoch (322906 steps), and yet it has the ability to recognize the meaning of most of the conversational sentences used in Farsi",arm-on/BERTweet-FA,0.0576119124889373,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16261,thu-coai/LongLM-large,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.0167011387335757,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
16262,google/t5-efficient-large-dm2000,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0721698179841041,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
16263,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0497710173949598,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
16264,rinna/japanese-gpt2-xsmall,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
16265,clue/xlnet_chinese_large,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.033929482102394,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
16266,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
16267,google/t5-efficient-small-kv128,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0621002297848462,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
16268,google/t5-efficient-small-nl20,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.017680000513792,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
16269,TJMUCH/transcriptome-iseeek,CamemBERT is a state-of-the-art language model for French based on the RoBERTa model,camembert/camembert-base-oscar-4gb,0.0834378451108932,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16270,allenai/wmt16-en-de-12-1,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.0091362446546554,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
16271,google/t5-efficient-large-dm2000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0320668686181307,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
16272,flaubert/flaubert_small_cased,Note: flaubert-small-cased is partially trained so performance is not guaranteed,flaubert/flaubert_small_cased,1.0,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
16273,google/t5-efficient-base-nh24,Its original repository can be found here,tau/splinter-large-qass,0.0721152890473603,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
16274,google/t5-efficient-large-dm128,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0052019618451595,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
16275,minimaxir/magic-the-gathering,It was introduced in this paper,readerbench/RoBERT-small,0.010540684685111,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
16276,cosmoquester/bart-ko-small,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,0.054090330377221,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
16277,stas/t5-very-small-random,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0854616612195968,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
16278,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,0.0506566713253656,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
16279,Fujitsu/pytorrent,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0054068742319941,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
16280,minimaxir/magic-the-gathering,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
16281,uer/t5-v1_1-small-chinese-cluecorpussmall,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0549030471593141,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
16282,hfl/rbtl3,T5-Efficient-LARGE-NL10 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl10,0.0606679702177643,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
16283,google/t5-efficient-large-kv256,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0189333837479353,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
16284,google/t5-efficient-tiny-ff6000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
16285,google/t5-efficient-small-kv128,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
16286,google/t5-efficient-large-nh8-nl32,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0142913004383444,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
16287,google/t5-efficient-base-kv256,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0029515493661165,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
16288,allenai/macaw-3b,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0581611034770806,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
16289,stas/pegasus-cnn_dailymail-tiny-random,The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0097972769290209,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
16290,VietAI/gpt-neo-1.3B-vietnamese-news,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16291,KoichiYasuoka/roberta-base-ukrainian,GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX,gilparmentier/pokemon_gptj_model,0.045890275388956,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
16292,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.086143285036087,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
16293,hfl/rbt3,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0342507176101207,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
16294,DeepPavlov/distilrubert-tiny-cased-conversational,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0182568070789178,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
16295,google/t5-efficient-base-nl4,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0242807883769273,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
16296,malay-huggingface/xlnet-large-bahasa-cased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0340181551873683,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
16297,google/t5-efficient-large-nh32,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.077388796955347,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
16298,patrickvonplaten/unispeech-sat-large-timit-ft,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0619086921215057,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
16299,google/t5-efficient-base-dm1000,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0766027309000491,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
16300,lgrobol/electra-minuscule-generator,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
16301,KoichiYasuoka/roberta-base-ukrainian,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
16302,google/t5-efficient-small-kv128,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0435008825734257,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
16303,junnyu/roformer_base_wwm_cluecorpussmall,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
16304,hfl/rbtl3,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0652080439031123,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
16305,hakurei/gpt-j-random-tinier,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0433769105002283,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
16306,piotr-rybak/poleval2021-task4-herbert-large-encoder,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
16307,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0669976398348808,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
16308,piotr-rybak/poleval2021-task4-herbert-large-encoder,Model is warm started from distilbert-fa’s checkpoint,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0507171601057052,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
16309,google/t5-efficient-tiny-nl6,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0810480862855911,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
16310,google/t5-efficient-large-nl10,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.026545263826847,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
16311,google/t5-efficient-small-dm768,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0537825943902134,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
16312,GKLMIP/electra-myanmar-small-uncased,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-mega,0.0626386636868118,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
16313,google/t5-efficient-xl-nl4,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0353184305131435,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
16314,aditeyabaral/sentencetransformer-bert-hinglish-small,Disclaimer: This model card has been written by gchhablani,google/fnet-large,0.0870213359594345,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
16315,patrickvonplaten/unispeech-sat-large-timit-ft,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0828190222382545,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
16316,mimicheng/codeparrot-ds,I have made no attempts to censor,bigjoedata/rockbot,0.0169854685664176,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
16317,malay-huggingface/t5-super-tiny-bahasa-cased,The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our Stack Overflow Dataset repository,UWB-AIR/MQDD-duplicates,0.0274630468338727,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
16318,google/t5-efficient-large-el8,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
16319,google/t5-efficient-large-dm256,Its original repository can be found here,tau/splinter-large-qass,0.0476107392460107,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
16320,VietAI/gpt-neo-1.3B-vietnamese-news,"Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words",flax-community/RoBERTa-large-finnish,0.0562620069831609,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16321,tau/tavbert-he,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0240370407700538,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
16322,google/t5-efficient-large-nh12,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0492187952622771,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
16323,rahulacj/bertweet-base-finetuned-sentiment-analysis,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
16324,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-LARGE-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl16,0.0952089391648769,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16325,zoha/wav2vec2-base-timit-demo-colab,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0841000750660896,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
16326,google/t5-efficient-large-nl12,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0470693055540323,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
16327,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
16328,google/t5-efficient-small-kv32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
16329,google/t5-efficient-xl-nl6,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-xl-nl6,1.0,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
16330,google/t5-efficient-base-nl32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0377881955355405,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
16331,google/t5-efficient-base-kv32,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0534562617540358,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
16332,DemangeJeremy/4-sentiments-with-flaubert,The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0861320570111274,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16333,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
16334,allenai/wmt16-en-de-dist-6-1,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
16335,sonoisa/byt5-small-japanese,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0640073642134666,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
16336,google/t5-efficient-base-kv256,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0670763738453388,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
16337,pulp/CHILDES-ParentBERTo,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0371781047433615,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
16338,diper1998/distilgpt2-finetuned-AT,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0406976789236068,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
16339,beston91/gpt2-xl_ft_logits_10k,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0343275070190429,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
16340,google/t5-efficient-small-dm128,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0328665673732757,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
16341,mimi/Waynehills-NLP-doogie,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0069768168032169,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
16342,microsoft/layoutlm-large-uncased,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16343,allenai/wmt19-de-en-6-6-base,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0597925819456576,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
16344,IDEA-CCNL/Erlangshen-Longformer-330M,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0103955222293735,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
16345,DemangeJeremy/4-sentiments-with-flaubert,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0812273584306239,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16346,tau/tavbert-he,T5-Efficient-LARGE-NL10 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl10,0.0295598478987813,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
16347,google/t5-efficient-small-kv128,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0387460589408874,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
16348,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,This model is cased: it does make a difference between bulgarian and Bulgarian,rmihaylov/bert-base-pos-theseus-bg,0.0756514240056276,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
16349,google/t5-efficient-base-nl32,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0451617445796727,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
16350,TJMUCH/transcriptome-iseeek,Check out this blog for more in-detail explanation of how to fine-tune the model,microsoft/unispeech-sat-base,0.0919180214405059,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16351,amazon/bort,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16352,zoha/wav2vec2-base-timit-demo-colab,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
16353,dbmdz/bert-medium-historic-multilingual-cased,Its original repository can be found here,tau/splinter-large-qass,0.048680568113923,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
16354,google/t5-efficient-base-el6,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0848307646811007,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
16355,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
16356,minimaxir/magic-the-gathering,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
16357,allenai/wmt19-de-en-6-6-base,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0858024880290031,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
16358,danyaljj/opengpt2_pytorch_forward,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
16359,mimi/Waynehills-NLP-doogie,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0731801763176918,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
16360,google/t5-efficient-base-dm1000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
16361,mwesner/bart-mlm,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0208556093275547,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
16362,mimi/Waynehills-NLP-doogie,"Important: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch)",pszemraj/led-large-book-summary,0.0945726633071899,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
16363,Gilles/FongBERT,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0120124233886599,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
16364,IDEA-CCNL/Erlangshen-Longformer-330M,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0411858162842691,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
16365,TJMUCH/transcriptome-iseeek,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0415712743997573,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16366,VietAI/gpt-neo-1.3B-vietnamese-news,"We use 1M raw Python scripts of PyTorrent that includes 12,350,000 LOC to train the model",Fujitsu/pytorrent,0.0313749285414814,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16367,rahulacj/bertweet-base-finetuned-sentiment-analysis,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.080026775598526,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
16368,gilparmentier/pokemon_gptj_model,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
16369,google/t5-efficient-small-ff3000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
16370,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0612162053585052,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
16371,google/t5-efficient-small-nl36,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0476848483085632,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
16372,dhpollack/distilbert-dummy-sentiment,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
16373,diper1998/distilgpt2-finetuned-AT,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0324921160936355,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
16374,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0752441883087158,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
16375,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,I have made no attempts to censor,bigjoedata/rockbot,0.0351472608745098,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
16376,bigjoedata/rockbot,Model attained an EM score of 17 and a Subset Match score of 24.5 We have written a blog post that covers the training procedure,deep-learning-analytics/triviaqa-t5-base,0.016276789518694,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
16377,google/t5-efficient-tiny-ff3000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0416132044047116,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
16378,google/t5-efficient-large-nl36,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0495306439697742,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
16379,j-hartmann/purchase-intention-english-roberta-large,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0135195733358462,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
16380,google/t5-efficient-base-nl40,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0682683940976857,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
16381,GKLMIP/electra-myanmar-small-uncased,"The model has 10 layers, 6 attention heads, 768 hidden units and 3072 feed forward size",castorini/afriberta_large,0.0428626011125743,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
16382,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
16383,uer/t5-v1_1-small-chinese-cluecorpussmall,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0341541413217782,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
16384,uer/t5-v1_1-base-chinese-cluecorpussmall,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0549030471593141,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
16385,google/t5-efficient-base-el8,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0651420988142489,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
16386,google/t5-efficient-small-nl48,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0146181499585509,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
16387,mimi/Waynehills-NLP-doogie,It was introduced in this paper,readerbench/RoBERT-small,0.0387678183615207,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
16388,google/t5-efficient-base-nh8,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0429756892845034,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
16389,microsoft/layoutlm-base-cased,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0656346306204795,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
16390,saurkulsh/T0pp,It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts,saurkulsh/T0pp,1.0,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
16391,abdusahmbzuai/ft-tatoeba-ar-en,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0419146753847599,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
16392,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0884612649679184,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
16393,abdusahmbzuai/aradia-ctc-distilhubert-ft,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0434554442763328,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
16394,microsoft/layoutlm-large-uncased,"This is one of the smaller pre-trained BERT variants, together with bert-small and bert-medium",prajjwal1/bert-mini,0.0148259978741407,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16395,mikaelsouza/msft-regular-model,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0558937154710292,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
16396,google/t5-efficient-base-el6,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
16397,NovelAI/genji-python-6B,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0270798125614722,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
16398,eli4s/Bert-L12-h384-A6,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,0.051030325392882,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
16399,SupriyaArun/squeezebert-uncased-finetuned-squad,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0023048687726259,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
16400,microsoft/layoutlm-large-uncased,"Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0370296612381935,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16401,IDEA-CCNL/Erlangshen-Longformer-110M,This model is cased: it makes a difference between english and English,google/fnet-large,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
16402,google/t5-efficient-mini,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0135039426386356,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
16403,ToToKr/kobigbird-bert-base-finetuned-klue,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0913937091827392,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
16404,microsoft/layoutlm-large-uncased,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0859998613595962,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16405,junnyu/roformer_base_wwm_cluecorpussmall,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
16406,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
16407,hfl/rbtl3,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.039031634107232,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
16408,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0892131477594375,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
16409,danyaljj/opengpt2_pytorch_forward,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0218882362047831,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
16410,google/t5-efficient-small-ff3000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0476940502412617,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
16411,google/t5-efficient-large-kv32,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0590034052729605,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
16412,google/t5-efficient-small-dm128,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0457727760076522,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
16413,beston91/gpt2-xl_ft_logits_10k,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0931516736745834,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
16414,lgrobol/electra-minuscule-generator,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0280774617567658,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
16415,google/t5-efficient-large-kv256,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0230224747210741,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
16416,ctoraman/RoBERTa-TR-medium-char,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.010403949022293,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
16417,cammy/led-large-16384-arxiv-100-MDS,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0463617891073226,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
16418,hfl/chinese-electra-180g-small-generator,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0009091390917698,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
16419,google/t5-efficient-tiny-nl32,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0642474181950092,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
16420,antoiloui/belgpt2,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
16421,google/t5-efficient-base-kv128,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0693861581385135,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
16422,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
16423,google/t5-efficient-base-dm2000,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0710120014846324,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
16424,crabz/distil-slovakbert-ner,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0531535670161247,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
16425,google/t5-efficient-small-kv32,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0062946826219558,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
16426,IDEA-CCNL/Erlangshen-Longformer-110M,"zip, use 180G of data to continue trainingThere is no structure of Longformer-base in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-110M,1.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
16427,dbmdz/convbert-base-german-europeana-cased,Detailed information about the data and pretraining steps can be found in this repository,dbmdz/convbert-base-german-europeana-cased,1.0,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
16428,VietAI/gpt-neo-1.3B-vietnamese-news,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0725522134453058,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16429,google/t5-efficient-large-el4,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0311331637203693,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
16430,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff1000,0.0768661722540855,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
16431,gilparmentier/pokemon_gptj_model,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
16432,skt/ko-gpt-trinity-1.2B-v0.5,Use jcblaise/roberta-tagalog-base or jcblaise/roberta-tagalog-large instead for better performance,jcblaise/distilbert-tagalog-base-cased,0.0531279556453227,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
16433,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
16434,DemangeJeremy/4-sentiments-with-flaubert,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0478557432070374,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16435,aubmindlab/aragpt2-mega,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0010253386572003,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
16436,google/t5-efficient-small-nl2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0281777326017618,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
16437,google/t5-efficient-small-dm768,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0795232206583022,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
16438,mimi/Waynehills-NLP-doogie,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0897672846913337,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
16439,Milos/slovak-gpt-j-162M,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0407696957699954,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
16440,hf-internal-testing/tiny-random-rembert,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0758558586239814,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
16441,google/t5-efficient-small-nl24,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0547368843108415,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
16442,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0643855333328247,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
16443,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-XL-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl8,0.0982405692338943,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
16444,sultan/BioM-ELECTRA-Large-Generator,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,1.0,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
16445,google/t5-efficient-base-nl2,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
16446,google/t5-efficient-small-ff9000,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0518610309809446,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
16447,patrickvonplaten/unispeech-sat-large-timit-ft,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0309428442269563,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
16448,google/t5-efficient-tiny-el8,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0049333469942212,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
16449,malay-huggingface/t5-tiny-bahasa-cased,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0564422042419513,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
16450,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased ELECTRA base model for Turkish 🎉We release a base ELECTRA model for Turkish, that was trained on the same data as BERTurk",dbmdz/electra-base-turkish-cased-discriminator,0.0767575353384018,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
16451,google/t5-efficient-xl-nl4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.078286089003086,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
16452,sonoisa/byt5-small-japanese,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0481827892363071,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
16453,google/t5-efficient-large-nl12,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0650243591517209,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
16454,allenai/macaw-answer-11b,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0526567269116639,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
16455,google/t5-efficient-base-dm2000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0475639700889587,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
16456,tau/tavbert-he,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
16457,google/t5-efficient-small-el4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
16458,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0983742922544479,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
16459,ductuan024/AimeLaw,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0679971650242804,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
16460,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff1000,0.072272889316082,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
16461,google/t5-efficient-large-nl12,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0475371517241,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
16462,dbmdz/electra-base-turkish-cased-discriminator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
16463,andrejmiscic/simcls-scorer-billsum,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
16464,skt/ko-gpt-trinity-1.2B-v0.5,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0784381069242954,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
16465,beston91/gpt2-xl_ft_logits_10k,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
16466,google/t5-efficient-base-nh16,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0092051532119512,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
16467,google/t5-efficient-xl-nl6,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0479549057781696,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
16468,patrickvonplaten/unispeech-sat-large-timit-ft,Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering,tau/splinter-base-qass,0.0713512748479843,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
16469,danyaljj/opengpt2_pytorch_forward,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0481333111723264,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
16470,google/t5-efficient-large-el2,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
16471,VietAI/gpt-neo-1.3B-vietnamese-news,The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0833072662353515,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16472,google/t5-efficient-small-ff12000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
16473,ctoraman/RoBERTa-TR-medium-wp-66k,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,1.0,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
16474,TJMUCH/transcriptome-iseeek,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0386336892843246,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16475,microsoft/unihanlm-base,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0089663405669853,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
16476,google/t5-efficient-base-dm1000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0101218009367585,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
16477,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0045571066439151,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
16478,GKLMIP/electra-myanmar-small-uncased,Final perplexity on test set is 13.6,sberbank-ai/rugpt3large_based_on_gpt2,0.0324836689978837,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
16479,oskrmiguel/mt5-simplification-spanish,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0438363477587699,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
16480,google/t5-efficient-small-nl24,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0525046465918421,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
16481,ctoraman/RoBERTa-TR-medium-char,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
16482,kyryl0s/gpt2-uk-xxs,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0337211135774851,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
16483,stas/mt5-tiny-random,"They were introduced in the study Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (arxiv), and ported to HF for the study Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics (arXiv)",prajjwal1/bert-tiny,0.097280129790306,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
16484,deep-learning-analytics/triviaqa-t5-base,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0454490195427621,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
16485,google/t5-efficient-small-nl36,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
16486,google/t5-efficient-xxl-nl4,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0414613117463886,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
16487,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0931695401668548,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
16488,suzuki/distilbert-base-uncased-finetuned-squad,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0529713705182075,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
16489,kyryl0s/gpt2-uk-xxs,Check out our paper DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts for more details,yarongef/DistilProtBert,0.0314746154472232,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
16490,hf-internal-testing/tiny-random-rembert,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0941087082028389,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
16491,aubmindlab/aragpt2-mega,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0507875415496528,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
16492,google/t5-efficient-base-el16,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0415730830281972,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
16493,fnlp/bart-large-chinese,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0241177771240472,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
16494,Parsa/BBB_prediction_classification_SMILES,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0666950680315494,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
16495,cammy/led-base-16384-100-MDS,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0938248485326767,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
16496,hfl/rbt3,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.039031634107232,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
16497,allenai/wmt19-de-en-6-6-base,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0788229703903197,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
16498,google/t5-efficient-base-nl24,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0039089517667889,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
16499,google/t5-efficient-large-nl20,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
16500,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
16501,hfl/chinese-electra-small-ex-discriminator,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
16502,SupriyaArun/squeezebert-uncased-finetuned-squad,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0460448712110519,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
16503,google/t5-efficient-base-nh16,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0551213417202234,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
16504,lgrobol/electra-minuscule-discriminator,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.037840312346816,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
16505,hfl/chinese-electra-small-ex-generator,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0621405546553432,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
16506,cammy/led-base-16384-100-MDS,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
16507,nherve/flaubert-oral-ft,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.058380785708626,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
16508,google/t5-efficient-large-kv16,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0488035436719655,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
16509,lgrobol/electra-minuscule-generator,"The quality estimation can be done at different levels: document level, sentence level and word level",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0646920800209045,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
16510,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
16511,superspray/electra_large_discriminator_squad2_custom_dataset,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
16512,google/reformer-crime-and-punishment,Its original repository can be found here,tau/splinter-large-qass,0.0134886093437671,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
16513,stas/mt5-tiny-random,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0369286239147186,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
16514,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
16515,jnz/electra-ka,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0989326834678649,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
16516,fnlp/bart-base-chinese,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0405930690467357,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
16517,lgrobol/electra-minuscule-generator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0402696803212165,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
16518,flboehm/reddit-bert-text_5,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0752617642283439,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
16519,danyaljj/opengpt2_pytorch_forward,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0007300352056821,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
16520,IDEA-CCNL/Erlangshen-Longformer-110M,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0219831145368516,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
16521,google/t5-efficient-large-nl12,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0091141797602176,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
16522,1one5ome/gpt2-chinese-gulong,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
16523,google/t5-efficient-mini-nl6,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0211460459977388,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
16524,minimaxir/magic-the-gathering,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
16525,ncduy/phobert-large-finetuned-vietnamese_students_feedback,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0798505097627639,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
16526,flboehm/reddit-bert-text_5,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0233665145933628,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
16527,diper1998/distilgpt2-finetuned-AT,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
16528,amazon/bort,By training a MLM from scratch on 438552 (cleaned*) SMILES I wanted to build a model that learns this kind of molecular combinations so that given a partial SMILE it can generate plausible combinations so that it can be proposed as new drugs,mrm8488/chEMBL_smiles_v1,0.0465410528704523,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16529,piotr-rybak/poleval2021-task4-herbert-large-encoder,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0858047753572464,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
16530,brad1141/Longformer-finetuned-norm,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.027826651930809,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
16531,allenai/wmt19-de-en-6-6-big,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0598908364772796,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
16532,suzuki/distilbert-base-uncased-finetuned-squad,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
16533,google/t5-efficient-small-kv128,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0568760819733142,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
16534,hfl/rbt3,This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,0.0890022926032543,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
16535,uer/t5-v1_1-small-chinese-cluecorpussmall,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0885794647037982,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
16536,google/t5-efficient-base-nl2,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0117034539580345,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
16537,fnlp/bart-large-chinese,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0582859423011541,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
16538,sberbank-ai/rugpt3medium_based_on_gpt2,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0379278521286323,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
16539,google/t5-efficient-large-nh8,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0624258667230605,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
16540,Milos/slovak-gpt-j-1.4B,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
16541,malay-huggingface/bert-tiny-bahasa-cased,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0136738068734605,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
16542,google/t5-efficient-tiny-nl24,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0173660935834049,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
16543,crabz/distil-slovakbert-ner,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0164450053125619,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
16544,google/t5-efficient-xl-nl4,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0225469730794429,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
16545,google/t5-efficient-tiny-nh1,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0542719084769487,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
16546,mrm8488/deberta-v3-small-finetuned-mrpc,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0877099260687828,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
16547,hfl/rbtl3,T5-Efficient-SMALL-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff6000,0.060982609167695,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
16548,Cedille/fr-boris,Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the mesh-transformer-jax codebase,Cedille/fr-boris,1.0,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
16549,mwesner/reformer-clm,T5-Efficient-LARGE-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el4,0.0367331765592098,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
16550,TJMUCH/transcriptome-iseeek,"Data prepared for this task as described in this project,  using the simulated admission notes (taken from discharge summaries)",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16551,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0420734472572803,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
16552,google/t5-efficient-large-dm512,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0689158830791711,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
16553,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0009581856429576,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
16554,google/t5-efficient-base-dm256,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0758267156779765,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
16555,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0193612022946278,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
16556,google/t5-efficient-tiny-ff9000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0721611604094505,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
16557,cammy/led-large-16384-arxiv-100-MDS,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0876995399594307,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
16558,saibo/random-roberta-tiny,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
16559,google/t5-efficient-tiny-nh16,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
16560,hf-internal-testing/tiny-random-rembert,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0437889359891414,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
16561,google/bert_uncased_L-10_H-768_A-12,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-10_H-768_A-12,1.0,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
16562,hfl/rbt3,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.0242743245325982,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
16563,hfl/rbt3,T5-Efficient-SMALL-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl40,0.0553291244432329,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
16564,google/t5-efficient-tiny-el8,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0664872936904429,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
16565,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0353266187012195,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
16566,mrm8488/deberta-v3-small-finetuned-mrpc,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0831839442253112,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
16567,tartuNLP/gpt-4-est-large,Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature,sultan/BioM-ELECTRA-Large-Generator,0.0626197606325149,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
16568,superspray/electra_large_discriminator_squad2_custom_dataset,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0277586579322814,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
16569,google/t5-efficient-xl-nl6,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.081511840224266,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
16570,google/t5-efficient-xl-nl4,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0067766085267066,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
16571,VietAI/gpt-neo-1.3B-vietnamese-news,"""GPT-J"" refers to the class of model, while ""6B"" represents the number of trainable parameters",gilparmentier/pokemon_gptj_model,0.0447676349431276,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16572,amazon/bort,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0660256426781415,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16573,google/t5-efficient-large-nl10,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.072668544948101,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
16574,kyryl0s/gpt2-uk-xxs,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0747928321361541,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
16575,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-SMALL-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv32,0.0506318770349025,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
16576,Fujitsu/pytorrent,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.029214446277668,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
16577,superspray/electra_large_discriminator_squad2_custom_dataset,For other languages this will be mainly experimental,pere/DeUnCaser,0.077307716012001,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
16578,google/t5-efficient-large-el8,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
16579,uer/chinese_roberta_L-2_H-256,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
16580,google/t5-efficient-tiny-ff6000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0370747596025466,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
16581,google/t5-efficient-base-dm256,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0450970139354467,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
16582,KoichiYasuoka/roberta-base-ukrainian,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
16583,google/t5-efficient-small-el64,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0669727139174937,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
16584,negfir/distilbert-base-uncased-finetuned-squad,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0842504054307937,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
16585,google/t5-efficient-base-ff6000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0538578499108552,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
16586,Palak/microsoft_deberta-base_squad,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0088280197232961,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
16587,SetFit/deberta-v3-large__sst2__train-8-8,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0715275555849075,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
16588,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
16589,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
16590,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0832031443715095,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
16591,DemangeJeremy/4-sentiments-with-flaubert,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.0660066120326519,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16592,allenai/macaw-3b,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0747090416649977,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
16593,Davlan/m2m100_418M-eng-yor-mt,m2m100_418M-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned   facebook/m2m100_418M model,Davlan/m2m100_418M-eng-yor-mt,1.0,-1.0,NLP,22200502-47bf-477d-b7e3-dec71a3aae90___m2m100_418M-eng-yor-mt,HF
16594,google/t5-efficient-tiny-nl16,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0022774739190936,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
16595,cammy/led-large-16384-arxiv-100-MDS,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0591277331113815,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
16596,stas/mt5-tiny-random,"For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github",sarnikowski/convbert-medium-small-da-cased,0.0655120909214019,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
16597,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0950583666563034,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
16598,google/t5-efficient-large-nl16,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0138079728931188,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
16599,google/byt5-xl,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0054966984316706,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
16600,tau/splinter-large-qass,Its original repository can be found here,tau/splinter-large-qass,1.0,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
16601,google/t5-efficient-small-dm768,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0156462527811526,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
16602,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,Amazon's BORT,amazon/bort,0.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
16603,sberbank-ai/rugpt3medium_based_on_gpt2,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0540436850860714,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
16604,mimi/Waynehills-NLP-doogie,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.08955068141222,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
16605,dhpollack/distilbert-dummy-sentiment,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0169504359364509,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
16606,google/t5-efficient-tiny-nh8,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
16607,microsoft/deberta-v2-xxlarge-mnli,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.020503906533122,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
16608,google/t5-efficient-large-nh24,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0690892860293387,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
16609,google/t5-efficient-xl-nl6,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0042491294443607,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
16610,google/t5-efficient-small-dm2000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
16611,google/t5-efficient-base-nh24,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0343101378530264,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
16612,google/t5-efficient-small-ff6000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0558100752532481,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
16613,google/t5-efficient-tiny-nh1,T5-Efficient-TINY-NH1 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh1,1.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
16614,eli4s/Bert-L12-h240-A12,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0624053506180643,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
16615,KoichiYasuoka/roberta-base-ukrainian,"Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants",hfl/chinese-legal-electra-base-generator,0.0586841478943824,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
16616,tartuNLP/gpt-4-est-large,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0140231419354677,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
16617,Parsa/BBB_prediction_classification_SMILES,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0045571817706028,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
16618,razent/SciFive-base-PMC,"Therefore, this model does not need a tokenizer",google/reformer-enwik8,0.0689036212861537,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
16619,google/t5-efficient-small-nl22,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0575203374028205,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
16620,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0576709856589634,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
16621,google/t5-efficient-large-el4,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0466204052790999,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
16622,gayanin/bart-mlm-pubmed-medterm,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
16623,lannelin/bert-imdb-1hidden,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
16624,VietAI/gpt-neo-1.3B-vietnamese-news,This makes the model more efficient since the output embeddings are discarded during fine-tuning,google/rembert,0.0581394471228122,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16625,google/t5-efficient-small-dm768,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0152553655207157,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
16626,google/t5-efficient-small-ff9000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0442158672958612,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
16627,amazon/bort,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.038909351453185,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16628,saibo/random-roberta-tiny,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
16629,allenai/wmt19-de-en-6-6-base,I have made no attempts to censor,bigjoedata/rockbot,0.0789867900311946,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
16630,google/t5-efficient-tiny-ff9000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0110237915068864,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
16631,google/t5-efficient-large-nh8,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0213289270177483,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
16632,nyu-mll/roberta-med-small-1M-3,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
16633,Sindhu/muril-large-squad2,Amazon's BORT,amazon/bort,0.0599721064791083,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
16634,google/t5-efficient-base-nl4,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0718932934105396,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
16635,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0793724507093429,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
16636,google/t5-efficient-xxl-nl4,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0600258531048893,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
16637,KoichiYasuoka/roberta-base-ukrainian,The three provided models are randomly selected from 10 that were trained and reported in the paper,phueb/BabyBERTa-2,0.088286392390728,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
16638,ctoraman/RoBERTa-TR-medium-char,Vocabulary size is 384,ctoraman/RoBERTa-TR-medium-char,1.0,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
16639,google/bert_uncased_L-10_H-768_A-12,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0481574097648262,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
16640,tartuNLP/gpt-4-est-large,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0751838535070419,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
16641,jcblaise/electra-tagalog-small-cased-generator,"If you use our model or find it useful in your projects, please cite our work:",jcblaise/electra-tagalog-small-cased-generator,1.0,-1.0,NLP,f4e25246-ebec-4c97-8a1d-5ef296f2f888___electra-tagalog-small-cased-generator,HF
16642,tartuNLP/gpt-4-est-large,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0579142570495605,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
16643,google/t5-efficient-small-nl36,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0304499510675668,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
16644,piotr-rybak/poleval2021-task4-herbert-large-encoder,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
16645,google/t5-efficient-xl-nl16,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0972814261913299,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
16646,minimaxir/magic-the-gathering,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0330122373998164,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
16647,everdoubling/byt5-Korean-small,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0573626816272735,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
16648,google/t5-efficient-large-nl32,Its original repository can be found here,tau/splinter-large-qass,0.0576730277389287,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
16649,microsoft/layoutlm-base-uncased,"This is one of the smaller pre-trained BERT variants, together with bert-mini bert-small and bert-medium",prajjwal1/bert-tiny,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
16650,Rostlab/prot_bert,ProtBert is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion,Rostlab/prot_bert,1.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
16651,kssteven/ibert-roberta-large,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0081333849917758,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
16652,google/t5-efficient-small-ff1000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0606318619102239,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
16653,mimicheng/codeparrot-ds,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0869658589363098,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
16654,google/t5-efficient-small-ff12000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0544439535588025,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
16655,mwesner/bart-mlm,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0802092626690864,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
16656,flax-community/gpt2-medium-persian,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.035798005759716,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
16657,google/t5-efficient-xl-nl16,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-xl-nl16,1.0,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
16658,microsoft/layoutlm-base-uncased,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-768_A-12,0.0504998192191123,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
16659,negfir/distilbert-base-uncased-finetuned-squad,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0649239793419838,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
16660,google/t5-efficient-xl-nl16,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.078286089003086,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
16661,google/t5-efficient-base-nl40,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0144037497229874,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
16662,superspray/electra_large_discriminator_squad2_custom_dataset,For best results use python as below,pszemraj/led-large-book-summary,0.076116994023323,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
16663,google/t5-efficient-large-el4,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0278275655582547,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
16664,google/t5-efficient-base-nh16,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
16665,hfl/chinese-xlnet-mid,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0395001955330371,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
16666,saibo/random-roberta-mini,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
16667,google/t5-efficient-base-kv128,This does not make a very readable text,pere/DeUnCaser,0.0350017668679356,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
16668,google/t5-efficient-base-nl4,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0130809349939226,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
16669,google/t5-efficient-small-ff1000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0555226169526576,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
16670,google/t5-efficient-small-kv32,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0352365188300609,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
16671,eli4s/Bert-L12-h384-A6,Fine-tuned facebook/wav2vec2-xls-r-1b on English using the Common Voice 8,jonatasgrosman/wav2vec2-xls-r-1b-english,0.0726123371471961,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
16672,yarongef/DistilProtBert,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0678140930831432,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
16673,stas/mt5-tiny-random,Training metrics were logged via Tensorboard,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0919143259525299,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
16674,everdoubling/byt5-Korean-small,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0261445395648479,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
16675,M-FAC/bert-mini-finetuned-stsb,Its original repository can be found here,tau/splinter-large-qass,0.0559742662589996,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
16676,patrickvonplaten/unispeech-sat-large-timit-ft,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0870450139045715,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
16677,google/t5-efficient-large-nh24,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
16678,hfl/chinese-electra-small-ex-discriminator,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0164398569613694,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
16679,dhpollack/distilbert-dummy-sentiment,This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named envibert,nguyenvulebinh/envibert,0.0341246724128723,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
16680,google/t5-efficient-base-nh8,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
16681,ebagdasa/propaganda_positive_bart_large,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0703459444145361,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
16682,lgrobol/electra-minuscule-discriminator,Its original repository can be found here,tau/splinter-large-qass,0.0546715222299098,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
16683,google/t5-efficient-large-nh24,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0389246977865695,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
16684,brad1141/Longformer-finetuned-norm,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,0.0805859491229057,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
16685,lgrobol/electra-minuscule-discriminator,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0163161680102348,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
16686,google/t5-efficient-large-el4,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0103541016578674,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
16687,minimaxir/magic-the-gathering,Source language: English,NYTK/translation-marianmt-en-hu,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
16688,ToToKr/kobigbird-bert-base-finetuned-klue,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0998906046152114,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
16689,sentence-transformers/paraphrase-MiniLM-L3-v2,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0974537283182144,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
16690,google/t5-efficient-tiny-nh16,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0609256364405155,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
16691,mrm8488/bert-small-finetuned-squadv2,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0893170572817325,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
16692,mimicheng/codeparrot-ds,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0646155849099159,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
16693,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0470558553934097,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
16694,google/bert_uncased_L-10_H-256_A-4,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0110978856682777,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
16695,google/t5-efficient-small-ff1000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0513293435797095,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
16696,google/t5-efficient-base-dm256,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0912170819938182,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
16697,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.039817277342081,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
16698,tartuNLP/gpt-4-est-large,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.08799958974123,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
16699,inokufu/flaubert-base-uncased-xnli-sts,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,inokufu/flaubert-base-uncased-xnli-sts,1.0,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
16700,hf-internal-testing/tiny-random-rembert,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0700200498104095,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
16701,stas/mt5-tiny-random,"Please check out our EMNLP 2021 paper ""BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation"" for more details",jhu-clsp/bibert-ende,0.087050050497055,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
16702,antoiloui/belgpt2,For best results use python as below,pszemraj/led-large-book-summary,0.0722690001130103,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
16703,negfir/distilbert-base-uncased-finetuned-cola,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0282688476145267,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
16704,google/t5-efficient-small-nl22,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0290538575500249,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
16705,hajime9652/xlnet-japanese,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.083049900829792,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
16706,google/t5-efficient-base-kv256,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0187602341175079,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
16707,TJMUCH/transcriptome-iseeek,This model was pretrained on Bulgarian language,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0298940632492303,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16708,aliosm/ComVE-gpt2-large,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
16709,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0694374293088913,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
16710,amazon/bort,"In our work, we have trained the BigBird model for the Persian language to process texts up to 4096 in the Farsi (Persian) language using sparse attention",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0266403537243605,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16711,lgrobol/electra-minuscule-generator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
16712,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.010433567687869,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
16713,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0954004004597663,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
16714,minimaxir/magic-the-gathering,"The quality estimation can be done at different levels: document level, sentence level and word level",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0312868924811482,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
16715,saibo/random-roberta-mini,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0059934717137366,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
16716,stas/mt5-tiny-random,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0560065731406211,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
16717,google/t5-efficient-base-el8,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0475241541862487,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
16718,thu-coai/LongLM-large,"To improve the capacities of both the encoder and decoder, we propose to train LongLM with two pretraining tasks including text inﬁlling (Raffel et al",thu-coai/LongLM-large,1.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
16719,google/t5-efficient-base-el8,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
16720,nghuyong/ernie-tiny,"Besides, many tasks may be formatted such that the output is directly generated in natural language",asi/gpt-fr-cased-base,0.0747244879603385,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
16721,google/t5-efficient-xl-nl4,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0972814261913299,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
16722,IDEA-CCNL/Erlangshen-Longformer-330M,"We used  Oscar dataset, which is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus",flax-community/gpt2-medium-persian,0.0702892635017633,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
16723,google/t5-efficient-xl-nl12,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0725680217146873,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
16724,asi/gpt-fr-cased-base,We release the weights for the following configurations:The model can be leveraged for language generation tasks,asi/gpt-fr-cased-base,1.0,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
16725,lgris/distilxlsr_bp_8-12,Note: This model does not have a tokenizer as it was pretrained on audio alone,lgris/distilxlsr_bp_8-12,1.0,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
16726,Sindhu/muril-large-squad2,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0291773476637899,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
16727,microsoft/layoutlm-large-uncased,Note: flaubert-small-cased is partially trained so performance is not guaranteed,flaubert/flaubert_small_cased,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16728,google/t5-efficient-base-ff6000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.053509460762143,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
16729,microsoft/layoutlm-large-uncased,"The base model is hubert-large-ll60k, which is pretrained on 16kHz  sampled speech audio",superb/hubert-large-superb-sid,0.0521999448537826,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16730,SupriyaArun/squeezebert-uncased-finetuned-squad,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.050453558564186,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
16731,TJMUCH/transcriptome-iseeek,"This model was created with the IXA Group research group of the University of the Basque Country, the model has been evaluated with the Sari, Bleu and Fklg metrics; it was trained and tested using the Simplext corpus",oskrmiguel/mt5-simplification-spanish,0.0868910998106002,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16732,amazon/bort,This model was trained using SentenceTransformers Cross-Encoder class,cross-encoder/stsb-TinyBERT-L-4,0.064199611544609,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16733,KoichiYasuoka/roberta-base-ukrainian,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0441157147288322,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
16734,google/t5-efficient-base-nh32,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.042548606172204,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
16735,google/t5-efficient-base-nl2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0580664388835429,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
16736,1one5ome/gpt2-chinese-gulong,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0689940452575683,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
16737,amazon/bort,When using the model make sure that your speech input is also sampled at 16Khz,superb/hubert-large-superb-sid,0.0818698778748512,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16738,google/t5-efficient-base-ff9000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0042923837900161,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
16739,tuner007/pegasus_qa,"Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint",KBLab/megatron-bert-large-swedish-cased-165k,0.0962731465697288,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
16740,piotr-rybak/poleval2021-task4-herbert-large-encoder,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0688892751932144,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
16741,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0806184411048889,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
16742,microsoft/layoutlm-base-uncased,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0466160923242569,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
16743,xlnet-large-cased,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0387305058538913,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
16744,microsoft/layoutlm-base-uncased,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0994086042046547,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
16745,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,0.0045805498957633,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
16746,google/t5-efficient-base-kv32,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0494178682565688,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
16747,Fujitsu/pytorrent,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0301485204448302,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
16748,kyryl0s/gpt2-uk-xxs,They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0620902236551046,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
16749,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0088507700711488,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
16750,google/t5-efficient-small-dm256,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0480008944869041,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
16751,1one5ome/gpt2-chinese-gulong,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-mega,0.0771119520068168,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
16752,google/t5-efficient-small-ff12000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
16753,NovelAI/genji-python-6B,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0474292337894439,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
16754,cammy/led-large-16384-arxiv-100-MDS,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.080662116408348,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
16755,DemangeJeremy/4-sentiments-with-flaubert,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0518565382808446,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16756,ai4bharat/MultiIndicHeadlineGeneration,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0388563421244422,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
16757,PlanTL-GOB-ES/gpt2-large-bne,"It is based on the GPT-2 model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the  National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019",PlanTL-GOB-ES/gpt2-large-bne,1.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
16758,sentence-transformers/paraphrase-MiniLM-L3-v2,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0115556120872497,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
16759,uer/t5-v1_1-base-chinese-cluecorpussmall,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0123561741784215,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
16760,SetFit/deberta-v3-large__sst2__train-8-8,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0878558903932571,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
16761,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0271224714815616,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
16762,google/t5-efficient-base-nh16,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0042523625306785,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
16763,celine98/canine-s-finetuned-sst2,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0603866763412952,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
16764,Recognai/selectra_medium,"From the original ELECTRA model card: ""ELECTRA models are trained to distinguish ""real"" input tokens vs ""fake"" input tokens generated by another neural network, similar to the discriminator of a GAN",Recognai/selectra_medium,1.0,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
16765,google/t5-efficient-large-nh32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0577099723741411,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
16766,google/t5-efficient-tiny-nh16,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0253162365406751,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
16767,anirudh21/xlnet-base-cased-finetuned-rte,We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric,andrejmiscic/simcls-scorer-billsum,0.0934806242585182,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
16768,DeepPavlov/distilrubert-tiny-cased-conversational-v1,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,1.0,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
16769,stas/t5-very-small-random,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.090894766151905,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
16770,google/t5-efficient-base-nl32,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0679286494851112,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
16771,sarnikowski/convbert-medium-small-da-cased,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0433608976503213,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
16772,brad1141/Longformer-finetuned-norm,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0217999033629894,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
16773,google/t5-efficient-small-nl48,Its original repository can be found here,tau/splinter-large-qass,0.0463279914110898,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
16774,google/t5-efficient-base-nl36,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0457796119153499,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
16775,funnel-transformer/small,"This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",funnel-transformer/small,1.0,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
16776,allenai/wmt19-de-en-6-6-base,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0644376967102288,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
16777,ml6team/mt5-small-german-finetune-mlsum,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
16778,DemangeJeremy/4-sentiments-with-flaubert,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0188513975590467,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16779,IDEA-CCNL/Erlangshen-Longformer-110M,The learned pattern probabilities can be used for de novo SMILES generation,mrm8488/chEMBL_smiles_v1,0.0280138459056615,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
16780,kyryl0s/gpt2-uk-xxs,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
16781,google/t5-efficient-base-ff1000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0516163930296897,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
16782,razent/SciFive-large-Pubmed,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0876993723213672,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
16783,obss/mt5-base-3task-highlight-tquad2,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0162327457219361,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
16784,mrm8488/deberta-v3-small-finetuned-mrpc,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.058473877608776,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
16785,google/t5-efficient-large-el8,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0454816725105046,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
16786,superspray/electra_large_discriminator_squad2_custom_dataset,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0969500839710235,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
16787,ToToKr/kobigbird-bert-base-finetuned-klue,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
16788,google/t5-efficient-xl-nl12,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0957668535411357,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
16789,google/t5-efficient-xl-nl4,This does not make a very readable text,pere/DeUnCaser,0.077029064297676,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
16790,google/t5-efficient-base-ff1000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0586922019720077,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
16791,indobenchmark/indobert-base-p1,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0648876031239827,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
16792,indobenchmark/indobert-base-p1,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0441399856160084,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
16793,kyryl0s/gpt2-uk-xxs,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0762008018791675,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
16794,hfl/rbtl3,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.025151289999485,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
16795,google/t5-efficient-xl-nl12,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0465695802122354,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
16796,tartuNLP/gpt-4-est-large,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.054640419781208,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
16797,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
16798,piotr-rybak/poleval2021-task4-herbert-large-encoder,This is a converted pytorch checkpoint for bert with 2L trained from scratch,tobiaslee/bert-2l-768h-uncased,0.0756249129772186,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
16799,google/t5-efficient-base-kv16,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,1.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
16800,nghuyong/ernie-tiny,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
16801,hakurei/gpt-j-random-tinier,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0492363460361957,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
16802,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0403680043915907,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
16803,google/t5-efficient-tiny-ff6000,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0555635765194892,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
16804,google/t5-efficient-large-kv32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0416643759235739,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
16805,lannelin/bert-imdb-1hidden,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
16806,negfir/distilbert-base-uncased-finetuned-cola,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0191145818680524,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
16807,google/t5-efficient-base-dm2000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0360062913969158,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
16808,pszemraj/led-large-book-summary,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0441299262456595,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
16809,microsoft/layoutlm-base-cased,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.075262039899826,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
16810,superb/hubert-large-superb-sid,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0615124367177486,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
16811,sentence-transformers/paraphrase-MiniLM-L3-v2,"As shown in the Metrics section, the small version (5 times fewer parameters) performs slightly worse, while the medium version (3 times fewer parameters) outperforms the BETO based zero-shot classifier",Recognai/zeroshot_selectra_small,0.0867523252964019,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
16812,google/t5-efficient-large-nl12,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0291452091187239,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
16813,google/t5-efficient-base-nh8,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0325811617076396,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
16814,TsinghuaAI/CPM-Generate,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,1.0,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
16815,mrm8488/chEMBL_smiles_v1,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,1.0,-1.0,NLP,919d68f1-5654-4b4c-a9c7-f59f67ababe7___chEMBL_smiles_v1,HF
16816,1one5ome/gpt2-chinese-gulong,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0916227027773857,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
16817,fnlp/bart-base-chinese,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0236504292115568,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
16818,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
16819,google/t5-efficient-large-nl32,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0450197383761405,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
16820,google/t5-efficient-tiny-ff6000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
16821,dexhrestha/Nepali-DistilBERT,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0212368071079253,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
16822,tartuNLP/gpt-4-est-large,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0933465138077735,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
16823,google/t5-efficient-small-ff3000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0208292249590158,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
16824,hfl/chinese-legal-electra-base-generator,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0469436260561147,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
16825,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0818189159035682,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
16826,google/t5-efficient-tiny-nh8,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0922414883971214,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
16827,IDEA-CCNL/Erlangshen-Longformer-110M,Source language: English,NYTK/translation-marianmt-en-hu,0.0443751122802495,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
16828,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,A distilled version of ProtBert-UniRef100 model,yarongef/DistilProtBert,0.0935567021369934,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
16829,google/t5-efficient-small-ff12000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0164696909487246,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
16830,stas/t5-very-small-random,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0357665866613388,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
16831,hfl/rbtl3,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,0.0544046573340891,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
16832,mwesner/bart-mlm,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0399535149335861,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
16833,aditeyabaral/sentencetransformer-bert-hinglish-small,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0214493852108716,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
16834,google/t5-efficient-base-dm1000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
16835,google/t5-efficient-base-kv256,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
16836,macedonizer/al-roberta-base,"Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words",macedonizer/al-roberta-base,1.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
16837,1one5ome/gpt2-chinese-gulong,com/peterwestuw/GPT2ForwardBackward,danyaljj/opengpt2_pytorch_forward,0.0348122119903564,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
16838,google/t5-efficient-tiny-nl2,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0799837298691271,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
16839,google/t5-efficient-small-kv128,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-kv128,1.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
16840,google/t5-efficient-base-dm256,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
16841,mrm8488/deberta-v3-small-finetuned-mrpc,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0271203480660915,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
16842,piotr-rybak/poleval2021-task4-herbert-large-encoder,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.0738986730575561,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
16843,google/t5-efficient-large-dm2000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.042616575025022,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
16844,google/t5-efficient-base-el8,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
16845,TJMUCH/transcriptome-iseeek,"This model was trained from 6,860,062 examples (whose have 3,512,351,744 tokens)",lassl/roberta-ko-small,0.0928483083844184,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16846,IDEA-CCNL/Erlangshen-Longformer-110M,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,0.0392944142222404,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
16847,IDEA-CCNL/Erlangshen-Longformer-330M,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.075458051636815,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
16848,SetFit/deberta-v3-large__sst2__train-8-8,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0947098955512046,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
16849,cosmoquester/bart-ko-small,I used multiple datasets for generalizing the model for both colloquial and written texts,cosmoquester/bart-ko-small,1.0,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
16850,ErykWdowiak/GPTalian,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
16851,google/t5-efficient-base-kv128,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
16852,google/t5-efficient-large-dm256,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
16853,google/t5-efficient-base-dm1000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0018066875636577,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
16854,Finnish-NLP/convbert-base-generator-finnish,Note: this model is the ConvBERT generator model intented to be used for the fill-mask task,Finnish-NLP/convbert-base-generator-finnish,1.0,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
16855,tucan9389/kcbert-base-finetuned-squad,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0327286012470722,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
16856,hfl/rbtl3,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0750453807413577,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
16857,google/t5-efficient-xl-nl12,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0096275294199585,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
16858,google/t5-efficient-large-nl32,T5-Efficient-LARGE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl32,1.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
16859,stas/mt5-tiny-random,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
16860,hfl/chinese-electra-small-ex-generator,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0418289816007017,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
16861,google/t5-efficient-large-kv128,This does not make a very readable text,pere/DeUnCaser,0.0489580184221267,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
16862,google/t5-efficient-small-nl2,Its original repository can be found here,tau/splinter-large-qass,0.0508162677288055,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
16863,google/t5-efficient-tiny-nh8,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0262283775955438,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
16864,DemangeJeremy/4-sentiments-with-flaubert,DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture,bookbot/distil-wav2vec2-adult-child-cls-52m,0.0104449037462472,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16865,mrp/simcse-model-wangchanberta,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0164475329220294,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
16866,dbmdz/electra-small-turkish-cased-discriminator,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0240952605381607,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
16867,google/t5-efficient-large-dm512,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0146202594041824,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
16868,obss/mt5-base-3task-highlight-tquad2,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0433346293866633,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
16869,google/t5-efficient-xxl-nl4,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-xxl-nl4,1.0,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
16870,TJMUCH/transcriptome-iseeek,This model is case-sensitive: it makes a difference between tirana and Tirana,macedonizer/al-roberta-base,0.0018299687653779,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16871,stas/mt5-tiny-random,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0463784001767635,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
16872,everdoubling/byt5-Korean-small,Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team,funnel-transformer/small,0.0415119590237736,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
16873,Parsa/BBB_prediction_classification_SMILES,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
16874,google/t5-efficient-large-kv256,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.079837255179882,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
16875,tartuNLP/gpt-4-est-large,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0813445746898651,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
16876,microsoft/layoutlm-base-uncased,"The base model is hubert-base-ls960, which is pretrained on 16kHz  sampled speech audio",superb/hubert-base-superb-er,0.0871235281229019,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
16877,google/t5-efficient-large-nl16,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0342910904437304,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
16878,mimi/Waynehills-NLP-doogie,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0949274748563766,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
16879,sarnikowski/convbert-medium-small-da-cased,For best results use python as below,pszemraj/led-large-book-summary,0.0402844063937663,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
16880,groar/gpt-neo-1.3B-finetuned-escape2,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0971659421920776,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
16881,stas/mt5-tiny-random,"You can fine-tune roberta-small-belarusian for downstream tasks, such as POS-tagging, dependency-parsing, and so on",KoichiYasuoka/roberta-small-belarusian,0.072752721607685,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
16882,andrejmiscic/simcls-scorer-billsum,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
16883,hf-internal-testing/tiny-random-rembert,A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock),bigjoedata/rockbot,0.0283225979655981,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
16884,mgreenbe/bertlet-base-uncased-for-sequence-classification,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0440268330276012,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
16885,flboehm/reddit-bert-text_5,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0665966272354126,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
16886,google/t5-efficient-small-ff12000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0582258757203816,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
16887,TJMUCH/transcriptome-iseeek,"Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint",KBLab/megatron-bert-large-swedish-cased-165k,0.0076146991923451,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
16888,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0540020242333412,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
16889,Iskaj/hf-challenge-test,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.0757225602865219,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
16890,google/t5-efficient-large-nl10,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.030235568061471,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
16891,hfl/rbtl3,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.0320176091045141,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
16892,google/t5-efficient-tiny-nh32,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.074704622849822,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
16893,aditeyabaral/sentencetransformer-bert-hinglish-small,mdThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-tiny,0.0140405911952257,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
16894,DemangeJeremy/4-sentiments-with-flaubert,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0336573254317044,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16895,google/t5-efficient-xl-nl8,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0744516905397176,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
16896,anirudh21/xlnet-base-cased-finetuned-rte,"FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows",nherve/flaubert-oral-ft,0.0682756155729293,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
16897,Gilles/FongBERT,Below are some examples of missing word prediction,Gilles/FongBERT,1.0,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
16898,tucan9389/kcbert-base-finetuned-squad,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0455205217003822,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
16899,lgrobol/electra-minuscule-discriminator,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
16900,google/t5-efficient-xl-nl8,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
16901,google/t5-efficient-large-nh12,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0337672815658151,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
16902,microsoft/layoutlm-large-uncased,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0988756418228149,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16903,IlyaGusev/rugpt3medium_sum_gazeta,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0331388507038354,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
16904,rahulacj/bertweet-base-finetuned-sentiment-analysis,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0794246047735214,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
16905,google/t5-efficient-xl-nl4,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0665781199932098,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
16906,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0389589862897992,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
16907,google/t5-efficient-large-el8,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0802627056837081,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
16908,nghuyong/ernie-1.0,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0373211875557899,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
16909,google/t5-efficient-base-kv128,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0487999217584728,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
16910,google/t5-efficient-base-nl48,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0427963584661483,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
16911,beston91/gpt2-xl_ft_logits_10k,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0417508780956268,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
16912,aubmindlab/aragpt2-mega,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0217175059951841,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
16913,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm128,0.0699438229203224,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
16914,google/t5-efficient-small-kv256,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0084399636834859,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
16915,google/t5-efficient-xl-nl16,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
16916,akdeniz27/deberta-v2-xlarge-cuad,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0944439843297004,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
16917,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0895238667726516,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
16918,google/t5-efficient-base-dm1000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
16919,thu-coai/LongLM-large,T5-Efficient-TINY-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl6,0.0602237118097642,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
16920,tau/tavbert-he,T5-Efficient-LARGE-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm128,0.0238641500473022,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
16921,uer/pegasus-base-chinese-cluecorpussmall,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.073788221925497,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
16922,google/t5-efficient-base-nl40,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0928719565272331,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
16923,google/t5-efficient-large-nl20,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.030172700062394,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
16924,jorge-henao/gpt2-small-spanish-disco-poetry-wt,This model is a fine-tuned version of datificate/gpt2-small-spanish on an unknown dataset,jorge-henao/gpt2-small-spanish-disco-poetry-wt,1.0,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
16925,dbmdz/electra-base-turkish-cased-discriminator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0398295740596949,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
16926,Palak/microsoft_deberta-base_squad,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0560475923120975,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
16927,cammy/led-base-16384-100-MDS,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0975981205701828,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
16928,indobenchmark/indobert-base-p1,For best results use python as below,pszemraj/led-large-book-summary,0.0972996751467386,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
16929,google/t5-efficient-base-nl48,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0416647754609584,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
16930,IlyaGusev/rugpt3medium_sum_gazeta,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.078455127775669,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
16931,amazon/bort,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
16932,google/t5-efficient-base-dm256,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0432991702109575,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
16933,google/t5-efficient-large-nh8,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0714570246636867,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
16934,KoichiYasuoka/roberta-base-ukrainian,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0946808755397796,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
16935,google/t5-efficient-xl-nl2,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0458299666643142,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
16936,uer/chinese_roberta_L-2_H-256,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0203560952097177,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
16937,sberbank-ai/rugpt3medium_based_on_gpt2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
16938,oskrmiguel/mt5-simplification-spanish,I have made no attempts to censor,bigjoedata/rockbot,0.0064308606088161,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
16939,microsoft/layoutlm-base-uncased,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0802314430475235,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
16940,GKLMIP/electra-myanmar-small-uncased,This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU,kssteven/ibert-roberta-large,0.0164843956008554,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
16941,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-LARGE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el8,0.0738067589700221,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
16942,google/t5-efficient-xxl-nl4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0412030853331088,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
16943,DemangeJeremy/4-sentiments-with-flaubert,"""GPT-J"" refers to the class of model, while ""6B"" represents the number of trainable parameters",gilparmentier/pokemon_gptj_model,0.0110781015828251,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
16944,google/t5-efficient-mini-nl6,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
16945,google/t5-efficient-small-nl22,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
16946,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
16947,google/t5-efficient-base-nl48,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
16948,hfl/chinese-electra-180g-small-ex-generator,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0347680797179539,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
16949,hfl/rbtl3,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,0.0116941090673208,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
16950,google/t5-efficient-base-dm512,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0345815825276076,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
16951,jcblaise/distilbert-tagalog-base-cased,This model is part of a larger research project,jcblaise/distilbert-tagalog-base-cased,1.0,-1.0,NLP,29e88f0d-47b4-4733-8b59-3cbe0f4bb77e___distilbert-tagalog-base-cased,HF
16952,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0648540630936622,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
16953,google/t5-efficient-large-kv128,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0508239734917878,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
16954,pszemraj/led-large-book-summary,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0342908669263124,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
16955,google/t5-efficient-small-nl22,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0946033410727976,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
16956,microsoft/layoutlm-large-uncased,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0170902032405138,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
16957,google/t5-efficient-small-dm128,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0547259291633962,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
16958,anas-awadalla/bert-tiny-finetuned-squad,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0389238074421882,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
16959,DTAI-KULeuven/robbertje-1-gb-bort,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.0514500873784223,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
16960,google/t5-efficient-small-kv32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0649191755801438,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
16961,google/t5-efficient-small-nl2,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0597937740385531,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
16962,google/t5-efficient-small-dm256,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0435667932033538,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
16963,antoiloui/belgpt2,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0013681091368198,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
16964,tau/tavbert-he,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.0265699625015258,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
16965,SupriyaArun/squeezebert-uncased-finetuned-squad,"Using this approach, the answer recall rate increases by a large margin, especially at small numbers of top-k",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0764349251985549,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
16966,Cedille/fr-boris,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0179472866778572,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
16967,google/t5-efficient-small-el16-dl4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0292899496853351,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
16968,google/t5-efficient-tiny-nh16,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0724580809473991,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
16969,google/t5-efficient-base-nh16,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0608244761824607,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
16970,SetFit/deberta-v3-large__sst2__train-8-8,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0562341809272766,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
16971,google/t5-efficient-base-nl2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0140111618675291,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
16972,google/bert_uncased_L-6_H-256_A-4,"This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking)",google/bert_uncased_L-6_H-256_A-4,1.0,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
16973,google/t5-efficient-small-el4,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0435796612873673,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
16974,hfl/rbt3,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0652080439031123,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
16975,google/t5-efficient-base-ff9000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0091503057628869,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
16976,google/t5-efficient-xl-nl16,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
16977,jpcorb20/pegasus-large-reddit_tifu-samsum-512,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0506265958150227,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
16978,pulp/CHILDES-ParentBERTo,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0283386823721229,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
16979,microsoft/layoutlm-base-cased,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
16980,google/t5-efficient-large-el8,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0614176783710717,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
16981,negfir/distilbert-base-uncased-finetuned-squad,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0458856672048568,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
16982,pulp/CHILDES-ParentBERTo,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-768_A-12,0.0643259026110172,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
16983,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
16984,google/t5-efficient-xl-nl6,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0673460066318512,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
16985,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0270996652543544,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
16986,rahulacj/bertweet-base-finetuned-sentiment-analysis,"zip, use 180G of data to continue trainingThere is no structure of Longformer-base in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-110M,0.0392673537135124,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
16987,aditeyabaral/sentencetransformer-bert-hinglish-small,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
16988,stas/mt5-tiny-random,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0765265375375747,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
16989,ctoraman/RoBERTa-TR-medium-char,Pretrained model on Turkish language using a masked language modeling (MLM) objective,ctoraman/RoBERTa-TR-medium-char,1.0,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
16990,cross-encoder/ms-marco-MiniLM-L-4-v2,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,1.0,-1.0,NLP,b57a156c-f487-4efc-a83f-37bedaa63a0a___ms-marco-MiniLM-L-4-v2,HF
16991,negfir/distilbert-base-uncased-finetuned-cola,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0245053730905056,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
16992,dbmdz/bert-medium-historic-multilingual-cased,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0255636763758957,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
16993,google/t5-efficient-small-kv16,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0132298879325389,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
16994,google/t5-efficient-large-dm768,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0583458170294761,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
16995,SetFit/deberta-v3-large__sst2__train-8-8,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0952968597412109,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
16996,google/bert_uncased_L-12_H-256_A-4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0481574097648262,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
16997,cosmoquester/bart-ko-mini,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0235126689076423,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
16998,DTAI-KULeuven/robbertje-1-gb-bort,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,1.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
16999,nherve/flaubert-oral-asr,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0566305021444955,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
17000,hfl/chinese-electra-180g-small-ex-generator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
17001,mmaguero/gn-bert-tiny-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
17002,DemangeJeremy/4-sentiments-with-flaubert,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0295453555881976,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17003,jweb/japanese-soseki-gpt2-1b,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.004679470633467,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
17004,microsoft/layoutlm-base-cased,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0520309619605541,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
17005,minimaxir/magic-the-gathering,They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
17006,google/t5-efficient-large-dm768,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
17007,microsoft/layoutlm-large-uncased,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0804584771394729,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
17008,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Both pytorch(pytorch_model bin) and Rust(rust_modelot) models are providedNOTE: Use T5Tokenizer to initiate the tokenizer,jweb/japanese-soseki-gpt2-1b,0.0769272446632385,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
17009,google/bert_uncased_L-10_H-128_A-2,Its original repository can be found here,tau/splinter-large-qass,0.0176675698409477,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
17010,stas/pegasus-cnn_dailymail-tiny-random,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0047832429409026,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17011,suzuki/distilbert-base-uncased-finetuned-squad,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0689699724316597,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
17012,google/t5-efficient-large-kv256,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0628860406577586,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
17013,google/t5-efficient-base-nl32,This does not make a very readable text,pere/DeUnCaser,0.0527314580976962,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
17014,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,6493ad33-df30-4985-946f-a59823df46ee___wav2vec2-large-robust-12-ft-emotion-msp-dim,HF
17015,google/t5-efficient-small-kv128,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
17016,NYTK/translation-marianmt-en-hu,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0126219078898429,-1.0,NLP,04ab8b45-5473-4cfe-a2de-c945b14ed956___translation-marianmt-en-hu,HF
17017,google/t5-efficient-large-kv128,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0507019860669969,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
17018,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-SMALL-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff12000,0.0818612451354661,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
17019,google/t5-efficient-large-el6,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0071700811386108,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
17020,fnlp/bart-large-chinese,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.0937601588666438,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
17021,google/t5-efficient-large-nh24,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0619379319250583,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
17022,google/t5-efficient-base-nh32,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.078807596117258,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
17023,google/t5-efficient-large-nl32,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0579651724547147,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
17024,hfl/rbtl3,T5-Efficient-BASE-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm1000,0.032241841778159,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
17025,jnz/electra-ka,It has less than 1% difference in performance compared to the BERT based model while having 2.0x the speed,khizon/distilbert-unreliable-news-eng-4L,0.0764819681644439,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
17026,google/t5-efficient-small-el12,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0484755476936697,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
17027,microsoft/layoutlm-base-cased,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0988756418228149,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
17028,amazon/bort,"Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words",macedonizer/al-roberta-base,0.0272603379562496,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17029,facebook/hubert-xlarge-ll60k,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.058935425709933,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
17030,saibo/random-roberta-mini,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
17031,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl2,0.0020200461149215,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17032,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17033,amazon/bort,Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives,nielsr/coref-roberta-large,0.0610087364912032,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17034,google/t5-efficient-large-nh8,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0510941445827484,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
17035,piotr-rybak/poleval2021-task4-herbert-large-encoder,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0758614018559455,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17036,uer/pegasus-base-chinese-cluecorpussmall,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0225625615566969,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
17037,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0949703156948089,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
17038,allenai/wmt19-de-en-6-6-big,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0719840936362743,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
17039,hfl/chinese-electra-small-ex-generator,Please use ElectraForPreTraining for discriminator and ElectraForMaskedLM for generator if you are re-training these models,hfl/chinese-electra-small-ex-generator,1.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
17040,antoiloui/belgpt2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0456444723531603,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
17041,google/t5-efficient-base-dm1000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0542664676904677,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
17042,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,mdThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-tiny,0.0225622225552797,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
17043,aubmindlab/aragpt2-large,You can find more information in our paper AraGPT2The code in this repository was used to train all GPT2 variants,aubmindlab/aragpt2-large,1.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
17044,stas/pegasus-cnn_dailymail-tiny-random,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0505789164453744,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17045,google/t5-efficient-tiny-ff6000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
17046,google/t5-efficient-base-nh32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0025978554040193,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
17047,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm1000,0.0521828867495059,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
17048,KBLab/megatron-bert-base-swedish-cased-600k,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
17049,ToToKr/kobigbird-bert-base-finetuned-klue,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
17050,saibo/random-roberta-tiny,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0233195377513766,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
17051,google/byt5-xl,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.057413704926148,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
17052,google/t5-efficient-small-kv256,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0952402502298354,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
17053,google/t5-efficient-base-dm2000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.002335024997592,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
17054,celine98/canine-s-finetuned-sst2,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
17055,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.043355043977499,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
17056,google/t5-efficient-small-el8,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0727326683700084,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
17057,google/t5-efficient-small-ff6000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0581554397940635,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
17058,amazon/bort,The dataset is composed of FQuAD v1.0 and Piaf with 24'566 questions and answers for the training set and 3'188 for the evaluation set,cmarkea/distilcamembert-base-qa,0.0769784748554229,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17059,facebook/hubert-xlarge-ll60k,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0277314423583447,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
17060,google/t5-efficient-mini-nl24,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0054011084139347,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
17061,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0743786171078681,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
17062,qarib/bert-base-qarib_far_6500k,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
17063,google/t5-efficient-tiny-el8,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0319622438400983,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
17064,ian0delond/model-test,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0931416079401969,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
17065,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0124248076851169,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
17066,pulp/CHILDES-ParentBERTo,"MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks",mrm8488/mobilebert-uncased-finetuned-squadv2,0.0433788001537322,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
17067,ian0delond/model-test,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0855647698044776,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
17068,DemangeJeremy/4-sentiments-with-flaubert,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17069,cammy/led-base-16384-100-MDS,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
17070,hakurei/gpt-j-random-tinier,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0201169531792402,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
17071,negfir/distilbert-base-uncased-finetuned-cola,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0319065973162651,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
17072,KBLab/megatron-bert-base-swedish-cased-600k,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
17073,clue/roberta_chinese_3L312_clue_tiny,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0600327812135219,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
17074,minimaxir/magic-the-gathering,DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture,bookbot/distil-wav2vec2-adult-child-cls-52m,0.0443042051047086,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
17075,google/t5-efficient-base-el6,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0011465447023511,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
17076,SetFit/deberta-v3-large__sst2__train-8-8,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0981790348887443,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
17077,google/t5-efficient-small-dm128,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0126431575044989,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
17078,pulp/CHILDES-ParentBERTo,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0299247223883867,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
17079,google/t5-efficient-small-ff6000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0249505639076232,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
17080,castorini/afriberta_base,"The model has been shown to obtain competitive downstream performances on text classification and Named Entity Recognition on several African languages, including those it was not pretrained on",castorini/afriberta_base,1.0,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
17081,uer/t5-v1_1-base-chinese-cluecorpussmall,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
17082,google/t5-efficient-small-nl20,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.034105135127902,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
17083,microsoft/layoutlm-base-uncased,Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer,flaubert/flaubert_small_cased,0.0450265258550643,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
17084,google/t5-efficient-base-nl4,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0696376711130142,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
17085,lannelin/bert-imdb-1hidden,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0238519581034779,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
17086,mrm8488/deberta-v3-small-finetuned-mrpc,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0378909111022949,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
17087,negfir/distilbert-base-uncased-finetuned-squad,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0713598877191543,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
17088,beston91/gpt2-xl_ft_logits_10k,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0043506105430424,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
17089,superspray/electra_large_discriminator_squad2_custom_dataset,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0731050595641136,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
17090,ebagdasa/propaganda_positive_bart_large,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0477390103042125,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
17091,google/t5-efficient-small-dm1000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0225648833438754,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
17092,mrm8488/bert-small-finetuned-squadv2,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0352844651788473,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
17093,sentence-transformers/paraphrase-MiniLM-L3-v2,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0212889648973941,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
17094,Vilnius-Lithuania-iGEM/Albumin,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0716117285192012,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
17095,google/t5-efficient-base-kv256,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
17096,microsoft/layoutlm-base-cased,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,0.00856757722795,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
17097,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,This model is cased: it makes a difference between english and English,google/fnet-large,0.078609749674797,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
17098,groar/gpt-neo-1.3B-finetuned-escape2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
17099,TJMUCH/transcriptome-iseeek,This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named envibert,nguyenvulebinh/envibert,0.0372110158205032,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
17100,diper1998/distilgpt2-finetuned-AT,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0002192826941609,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
17101,clue/roberta_chinese_3L312_clue_tiny,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0812244489789008,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
17102,anirudh21/xlnet-base-cased-finetuned-rte,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0781262665987014,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
17103,TJMUCH/transcriptome-iseeek,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
17104,google/t5-efficient-small-nl40,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0140338614583015,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
17105,google/t5-efficient-mini,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
17106,hfl/rbtl3,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0569835398346185,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
17107,google/t5-efficient-large-el8,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.041690969839692,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
17108,Palak/microsoft_deberta-base_squad,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0870762765407562,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
17109,google/t5-efficient-small-ff12000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0012775678187608,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
17110,uer/pegasus-base-chinese-cluecorpussmall,"This model is pre-trained by UER-py, which is introduced in this paper",uer/pegasus-base-chinese-cluecorpussmall,1.0,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
17111,dbmdz/bert-medium-historic-multilingual-cased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0467405784875154,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
17112,SetFit/deberta-v3-large__sst2__train-8-8,This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset,SetFit/deberta-v3-large__sst2__train-8-8,1.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
17113,kyryl0s/gpt2-uk-xxs,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0455036088824271,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
17114,minimaxir/magic-the-gathering,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0936285927891731,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
17115,beston91/gpt2-xl_ft_logits_10k,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0805540531873703,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
17116,google/t5-efficient-small-nl40,T5-Efficient-SMALL-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl40,1.0,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
17117,neuralspace-reverie/indic-transformers-bn-xlmroberta,Embeddings from this model can also be used for feature-based training,neuralspace-reverie/indic-transformers-bn-xlmroberta,1.0,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
17118,celine98/canine-s-finetuned-sst2,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0758578404784202,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
17119,google/t5-efficient-base-ff12000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0486585404723882,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
17120,allenai/wmt16-en-de-12-1,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0206154529005288,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
17121,mrm8488/chEMBL_smiles_v1,By training a MLM from scratch on 438552 (cleaned*) SMILES I wanted to build a model that learns this kind of molecular combinations so that given a partial SMILE it can generate plausible combinations so that it can be proposed as new drugs,mrm8488/chEMBL_smiles_v1,1.0,-1.0,NLP,919d68f1-5654-4b4c-a9c7-f59f67ababe7___chEMBL_smiles_v1,HF
17122,amazon/bort,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0364604964852332,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17123,mwesner/reformer-clm,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
17124,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0733730420470237,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
17125,DemangeJeremy/4-sentiments-with-flaubert,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0433710068464279,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17126,google/t5-efficient-large-nh32,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.07450314424932,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
17127,microsoft/layoutlm-base-cased,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0868481695652008,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
17128,google/t5-efficient-small-nl40,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-nl40,1.0,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
17129,gilparmentier/pokemon_gptj_model,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
17130,tuner007/pegasus_qa,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0701649114489555,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
17131,google/t5-efficient-large-kv128,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0597563739866017,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
17132,mmaguero/gn-bert-tiny-cased,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0617460571229457,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
17133,mmaguero/gn-bert-tiny-cased,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0835882946848869,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
17134,allenai/macaw-3b,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0312598223487536,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
17135,google/t5-efficient-base-kv16,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0218341425061226,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
17136,lgris/distilxlsr_bp_8-12,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.003211041757216,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
17137,microsoft/layoutlm-base-uncased,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.062629148364067,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
17138,google/t5-efficient-large-nl32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0340859442949295,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
17139,Palak/microsoft_deberta-base_squad,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0719208419322967,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
17140,stas/pegasus-cnn_dailymail-tiny-random,"They were introduced in the study Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (arxiv), and ported to HF for the study Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics (arXiv)",prajjwal1/bert-tiny,0.0576987508684396,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17141,tau/tavbert-he,T5-Efficient-MINI is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini,0.0423234030604362,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17142,amazon/bort,"It supports sequences of length up to 4,096! Longformer uses a combination of a sliding window (local) attention and global attention",mrm8488/longformer-base-4096-spanish,0.0198573945090174,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17143,google/t5-efficient-large-dm256,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
17144,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0067683868110179,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
17145,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0010500829666852,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
17146,rdenadai/BR_BERTo,T5-Efficient-LARGE-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm128,0.0265814575056234,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
17147,clue/xlnet_chinese_large,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0710313245654106,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
17148,aubmindlab/aragpt2-large,Pretrained model on 13G Chinese corpus(clue corpus small),junnyu/roformer_base_wwm_cluecorpussmall,0.0502085145562886,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
17149,google/t5-efficient-small-el4,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0310971327126025,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
17150,dbmdz/bert-medium-historic-multilingual-cased,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0612328946590422,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
17151,PlanTL-GOB-ES/gpt2-large-bne,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
17152,google/t5-efficient-xl-nl8,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0713227279484271,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
17153,microsoft/layoutlm-base-uncased,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,0.034470222890377,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
17154,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0235949046909809,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17155,aubmindlab/aragpt2-mega,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,0.0464358683675527,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
17156,google/t5-efficient-base-nl2,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0569392573088406,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
17157,google/t5-efficient-tiny-nh16,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.018240537494421,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
17158,google/t5-efficient-large-nh8-nl32,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0518893301486968,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
17159,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Source language: English,NYTK/translation-marianmt-en-hu,0.0650951936841011,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
17160,mrm8488/chEMBL_smiles_v1,They used a generative recurrent neural network (RNN) containing long short‐term memory (LSTM) cell to capture the syntax of molecular representations in terms of SMILES strings,mrm8488/chEMBL_smiles_v1,1.0,-1.0,NLP,919d68f1-5654-4b4c-a9c7-f59f67ababe7___chEMBL_smiles_v1,HF
17161,google/t5-efficient-large-kv256,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0369253801181912,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
17162,abdusahmbzuai/aradia-ctc-distilhubert-ft,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0558161213994026,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
17163,lassl/roberta-ko-small,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
17164,beston91/gpt2-xl_ft_logits_10k,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0984952822327613,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
17165,tuner007/pegasus_qa,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0838543027639389,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
17166,lannelin/bert-imdb-1hidden,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0603065825998782,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
17167,anas-awadalla/bert-tiny-finetuned-squad,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0859554931521415,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
17168,mldmm/GlassBERTa,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
17169,google/t5-efficient-small-nl36,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0258346255868673,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
17170,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv128,0.0495306340356667,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
17171,groar/gpt-neo-1.3B-finetuned-escape2,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0765250623226165,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
17172,stas/pegasus-cnn_dailymail-tiny-random,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0291526969522237,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17173,negfir/distilbert-base-uncased-finetuned-cola,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
17174,kssteven/ibert-roberta-large,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
17175,hf-internal-testing/tiny-random-rembert,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0328004881739616,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
17176,google/t5-efficient-base-nh32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
17177,uer/bart-chinese-4-768-cluecorpussmall,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0567190423607826,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
17178,google/t5-efficient-large-nh12,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
17179,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0368002355098724,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
17180,minimaxir/magic-the-gathering,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0017663156613707,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
17181,mmaguero/gn-bert-tiny-cased,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0670020654797553,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
17182,DemangeJeremy/4-sentiments-with-flaubert,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0764358043670653,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17183,google/t5-efficient-large-el6,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0617573969066142,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
17184,google/t5-efficient-base-el8,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.070465974509716,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
17185,google/t5-efficient-mini,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0706542879343032,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
17186,google/t5-efficient-mini-nl8,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0723331328481435,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
17187,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0572560094296932,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17188,google/t5-efficient-small-dm2000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0586077645421027,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
17189,Fujitsu/pytorrent,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0162271227066715,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
17190,indobenchmark/indobert-base-p1,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0217844570676485,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
17191,negfir/distilbert-base-uncased-finetuned-squad,This model is a fine-tuned version of distilbert-base-uncased on the squad dataset,negfir/distilbert-base-uncased-finetuned-squad,1.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
17192,google/t5-efficient-tiny-el8,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0751141682267188,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
17193,google/t5-efficient-large-nl16,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0606654658913612,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
17194,google/t5-efficient-small-dm2000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0052251219749451,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
17195,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0539043694734573,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17196,sultan/BioM-ELECTRA-Large-Generator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
17197,google/reformer-crime-and-punishment,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
17198,hfl/chinese-legal-electra-base-generator,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
17199,google/t5-efficient-small-nl22,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0075314585119485,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
17200,asi/gpt-fr-cased-base,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0261963032452123,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
17201,hajime9652/xlnet-japanese,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0847647935152053,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
17202,sberbank-ai/rugpt3large_based_on_gpt2,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0141121260821819,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
17203,google/t5-efficient-tiny-ff12000,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0521944724023342,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
17204,tau/tavbert-he,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17205,google/t5-efficient-xl-nl6,This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named envibert,nguyenvulebinh/envibert,0.0891874805092811,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
17206,amazon/bort,This is an ELECTRA model pretrained on approximately 200M Japanese sentences extracted from the mC4 and finetuned by spaCy v3 on UD_Japanese_BCCWJ r2.8,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,0.0421676877886056,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17207,google/t5-efficient-base-ff9000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.02398432046175,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
17208,sberbank-ai/rugpt3medium_based_on_gpt2,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
17209,amazon/bort,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17210,mimi/Waynehills-NLP-doogie,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0980893373489379,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
17211,microsoft/layoutlm-base-cased,Its original repository can be found here,tau/splinter-large-qass,0.0014627575874328,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
17212,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0680381953716278,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
17213,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0117860995233058,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17214,tau/tavbert-he,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0696616247296333,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17215,google/t5-efficient-base-ff9000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0769535899162291,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
17216,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl20,0.0925121009349822,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
17217,anas-awadalla/bert-tiny-finetuned-squad,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0941958427429199,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
17218,google/t5-efficient-tiny-ff6000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0587946251034735,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
17219,hfl/chinese-electra-small-ex-generator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
17220,google/t5-efficient-small-nl40,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0504709109663963,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
17221,lgrobol/electra-minuscule-generator,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
17222,eli4s/Bert-L12-h384-A6,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
17223,anirudh21/xlnet-base-cased-finetuned-rte,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0937688946723938,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
17224,TJMUCH/transcriptome-iseeek,This project was funded by the Language Technology Programme for Icelandic 2019-2023,jonfd/convbert-small-igc-is,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
17225,microsoft/layoutlm-base-uncased,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0871477276086807,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
17226,hfl/rbt3,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0410466082394122,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
17227,tartuNLP/gpt-4-est-base,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.036328786984086,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
17228,jpcorb20/pegasus-large-reddit_tifu-samsum-512,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0173563634355862,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
17229,yarongef/DistilProtBert,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
17230,google/t5-efficient-tiny-ff6000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0664962232112884,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
17231,google/t5-efficient-mini-nl24,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0359298791736364,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
17232,piotr-rybak/poleval2021-task4-herbert-large-encoder,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0914987027645111,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17233,Vilnius-Lithuania-iGEM/Albumin,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
17234,Norod78/hebrew_poetry-gpt_neo-tiny,You can use the raw model for summarization,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0635372698307037,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
17235,mrm8488/deberta-v3-small-finetuned-mrpc,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.088393360376358,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
17236,mwesner/reformer-clm,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0871514827013015,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
17237,microsoft/layoutlm-base-uncased,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0631272196769714,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
17238,Iskaj/hf-challenge-test,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0312519595026969,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
17239,IDEA-CCNL/Zhouwenwang-Unified-1.3B,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
17240,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17241,google/t5-efficient-large-nh2,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0533917415887116,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
17242,hfl/rbt3,T5-Efficient-LARGE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl36,0.0552526665851473,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
17243,google/t5-efficient-small-ff12000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
17244,beston91/gpt2-xl_ft_logits_10k,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0377064198255538,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
17245,beston91/gpt2-xl_ft_logits_10k,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0742165148258209,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
17246,google/t5-efficient-small-el16-dl4,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.007514975965023,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
17247,google/t5-efficient-tiny-nl2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0790280513465404,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
17248,Iskaj/hf-challenge-test,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0851124003529548,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
17249,google/t5-efficient-base-ff6000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0163471512496471,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
17250,google/t5-efficient-base-nl32,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.035211093723774,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
17251,rinna/japanese-gpt-1b,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
17252,rdenadai/BR_BERTo,"Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0261466844628254,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
17253,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0856916680932045,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
17254,akdeniz27/deberta-v2-xlarge-cuad,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
17255,jpcorb20/pegasus-large-reddit_tifu-samsum-512,The hypothesis being that it would help the convergence on the samsum dataset to have weights trained on a larger summarization dataset first like the Reddit TIFU using casual language,jpcorb20/pegasus-large-reddit_tifu-samsum-512,1.0,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
17256,dbmdz/bert-medium-historic-multilingual-cased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0509846098721026,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
17257,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0246696732938289,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17258,google/t5-efficient-tiny-nl32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0484365150332449,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
17259,rdenadai/BR_BERTo,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0279032327234745,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
17260,flax-community/RoBERTa-large-finnish,"This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens",flax-community/RoBERTa-large-finnish,1.0,-1.0,NLP,bd6a99f8-f445-4aab-803e-729051effeb2___RoBERTa-large-finnish,HF
17261,tartuNLP/gpt-4-est-large,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
17262,everdoubling/byt5-Korean-small,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0422038849443197,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
17263,google/t5-efficient-small-ff9000,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0497979596257209,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
17264,superb/hubert-large-superb-sid,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0386483557522296,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
17265,google/t5-efficient-base-kv16,This does not make a very readable text,pere/DeUnCaser,0.0265857763588428,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
17266,google/t5-efficient-base-nl4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0253978483378886,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
17267,google/t5-efficient-large-nl12,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0654670670628547,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
17268,hfl/chinese-electra-180g-small-ex-discriminator,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
17269,ductuan024/AimeLaw,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0653509367257356,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
17270,IDEA-CCNL/Zhouwenwang-Unified-110M,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
17271,kyryl0s/gpt2-uk-xxs,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
17272,google/t5-efficient-tiny-ff9000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0090507511049508,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
17273,minimaxir/magic-the-gathering,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0667200870811939,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
17274,google/t5-efficient-small-ff6000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
17275,google/t5-efficient-large-kv32,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0904418565332889,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
17276,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17277,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0669354200363159,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
17278,google/t5-efficient-tiny-nl32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0021838992834091,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
17279,google/t5-efficient-base-nl32,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0736422389745712,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
17280,cmarkea/distilcamembert-base-qa,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0607233010232448,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
17281,google/t5-efficient-small-nl24,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
17282,readerbench/RoBERT-small,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,1.0,-1.0,NLP,af9865c1-d52f-4c4d-8696-c19b9085823b___RoBERT-small,HF
17283,Recognai/zeroshot_selectra_small,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0250689430783192,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
17284,ncduy/phobert-large-finetuned-vietnamese_students_feedback,Its original repository can be found here,tau/splinter-large-qass,0.0565190725028514,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
17285,google/t5-efficient-mini-nl12,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0655096489936113,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
17286,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
17287,google/t5-efficient-xl-nl16,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0958956480026245,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
17288,mldmm/GlassBERTa,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,1.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
17289,google/t5-efficient-base-nh16,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
17290,abdusahmbzuai/aradia-ctc-distilhubert-ft,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0597460344433784,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
17291,anas-awadalla/bert-tiny-finetuned-squad,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0837088376283645,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
17292,piotr-rybak/poleval2021-task4-herbert-large-encoder,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0228751003742218,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17293,flboehm/reddit-bert-text_5,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0566420331597328,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
17294,aditeyabaral/sentencetransformer-bert-hinglish-small,Model is warm started from distilbert-fa’s checkpoint,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0699874833226203,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
17295,google/t5-efficient-small-el8,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0493797762319445,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
17296,google/t5-efficient-large-dm256,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
17297,PlanTL-GOB-ES/gpt2-base-bne,"The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019",PlanTL-GOB-ES/gpt2-base-bne,1.0,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
17298,DeepPavlov/distilrubert-tiny-cased-conversational,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0581562879184881,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
17299,abdusahmbzuai/aradia-ctc-distilhubert-ft,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0251738354563713,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
17300,dhpollack/distilbert-dummy-sentiment,"Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.042328082025051,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
17301,Vilnius-Lithuania-iGEM/Albumin,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
17302,google/t5-efficient-large-nh32,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0395100051537156,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
17303,google/t5-efficient-xl-nl8,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0115743745118379,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
17304,obss/mt5-base-3task-highlight-tquad2,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0448584062978625,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
17305,google/t5-efficient-small-ff9000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.022662691771984,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
17306,danyaljj/opengpt2_pytorch_forward,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0458498199780781,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
17307,google/t5-efficient-xxl-nl4,This does not make a very readable text,pere/DeUnCaser,0.0603934749960898,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
17308,google/t5-efficient-small-kv128,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0052538551390171,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
17309,M-FAC/bert-mini-finetuned-stsb,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0463664215058088,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
17310,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows",nherve/flaubert-oral-ft,0.0580598637461662,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
17311,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0625724196434021,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
17312,pulp/CHILDES-ParentBERTo,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0171031765639782,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
17313,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0969579815864563,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
17314,google/t5-efficient-small-dm2000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0003169924020767,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
17315,google/t5-efficient-base-nh32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0386763010174035,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
17316,google/t5-efficient-tiny-el8,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0553694069385528,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
17317,google/t5-efficient-base-ff12000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0350531935691833,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
17318,rmihaylov/pegasus-base-cnn-dailymail-bg,This model was pretrained on Bulgarian language,rmihaylov/pegasus-base-cnn-dailymail-bg,1.0,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
17319,eli4s/Bert-L12-h240-A12,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0590658746659755,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
17320,google/t5-efficient-large-el8,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0660236589610576,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
17321,hfl/rbt3,T5-Efficient-SMALL-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff12000,0.0616102395579218,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
17322,google/t5-efficient-tiny-ff12000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0314137339591979,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
17323,tartuNLP/gpt-4-est-large,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0227296091616153,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
17324,skt/ko-gpt-trinity-1.2B-v0.5,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,0.0576755106449126,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
17325,google/t5-efficient-large-el8,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0369378784671425,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
17326,DemangeJeremy/4-sentiments-with-flaubert,"This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences",Rostlab/prot_bert,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17327,diper1998/distilgpt2-finetuned-AT,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0484577305614948,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
17328,uer/pegasus-base-chinese-cluecorpussmall,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0375411417335272,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
17329,mwesner/reformer-clm,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0866903364658355,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
17330,microsoft/layoutlm-base-uncased,It was introduced in this paper,readerbench/RoBERT-small,0.0175572820007801,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
17331,anirudh21/xlnet-base-cased-finetuned-rte,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
17332,facebook/blenderbot-3B,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0470694535012755,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
17333,google/t5-efficient-base-dm256,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
17334,hf-internal-testing/tiny-random-rembert,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0450836941599845,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
17335,amazon/bort,See SimCLS paper for a description of baselines,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17336,xlnet-large-cased,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,1.0,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
17337,tau/splinter-base-qass,"For the model without those weights, see tau/splinter-base",tau/splinter-base-qass,1.0,-1.0,NLP,f68d3e26-2d96-4b99-a309-34b532e9740f___splinter-base-qass,HF
17338,thu-coai/LongLM-large,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0522637128209074,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
17339,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,This model is a fine-tuned version of t5-small on an unknown dataset,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,1.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
17340,suzuki/distilbert-base-uncased-finetuned-squad,This does not make a very readable text,pere/DeUnCaser,0.0376738309860229,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
17341,Norod78/hebrew_poetry-gpt_neo-tiny,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0747168641537427,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
17342,KoichiYasuoka/roberta-small-belarusian,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0242944993078709,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
17343,crabz/distil-slovakbert-ner,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0448686107993125,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
17344,stas/pegasus-cnn_dailymail-tiny-random,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.066869381815195,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17345,nherve/flaubert-oral-asr,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0581102954844633,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
17346,google/t5-efficient-tiny-ff9000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0535117294639348,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
17347,brad1141/Longformer-finetuned-norm,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0805186480283737,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
17348,mrm8488/deberta-v3-small-finetuned-mrpc,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0607253238558769,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
17349,TJMUCH/transcriptome-iseeek,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.068448930978775,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
17350,negfir/distilbert-base-uncased-finetuned-squad,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0884037315845489,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
17351,tuner007/pegasus_qa,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0648368597030639,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
17352,microsoft/layoutlm-large-uncased,"Pre-Training with Whole Word Masking for Chinese BERTYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping HuThis repository is developed based on：https://github",hfl/rbtl3,0.0483333654701709,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
17353,rahulacj/bertweet-base-finetuned-sentiment-analysis,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0691029876470565,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
17354,google/t5-efficient-small-ff6000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0532854292541742,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
17355,microsoft/deberta-v2-xxlarge-mnli,This does not make a very readable text,pere/DeUnCaser,0.0834955815225839,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
17356,SetFit/deberta-v3-large__sst2__train-8-8,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
17357,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0349569208920002,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17358,google/t5-efficient-small-ff9000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0585188902914523,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
17359,kyryl0s/gpt2-uk-xxs,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
17360,google/t5-efficient-xl-nl4,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0442664511501789,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
17361,google/t5-efficient-small-nl36,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0609041992574929,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
17362,mwesner/reformer-clm,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0622386634349823,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
17363,google/t5-efficient-large-nh24,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0084668649360537,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
17364,google/reformer-enwik8,"Therefore, this model does not need a tokenizer",google/reformer-enwik8,1.0,-1.0,NLP,6aa23d61-51e8-44d6-b6b7-b68cce7cd0c1___reformer-enwik8,HF
17365,hakurei/gpt-j-random-tinier,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0333148399367928,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
17366,stas/pegasus-cnn_dailymail-tiny-random,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0958827100694179,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17367,razent/SciFive-large-Pubmed,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0406091511249542,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
17368,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0563192069530487,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
17369,DemangeJeremy/4-sentiments-with-flaubert,Zhouwenwang-Unified-1.3B is the largest model for LM and MLM tasks in the Chinese field,IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0730167366564273,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17370,google/t5-efficient-base-dm512,Its original repository can be found here,tau/splinter-large-qass,0.059883328154683,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
17371,lassl/gpt2-ko-small,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
17372,junnyu/roformer_base_wwm_cluecorpussmall,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0460909189035495,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
17373,M-FAC/bert-mini-finetuned-stsb,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.003721535205841,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
17374,google/t5-efficient-tiny-nl32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0092184748500585,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
17375,tucan9389/kcbert-base-finetuned-squad,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0891732797026634,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
17376,cammy/led-base-16384-100-MDS,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
17377,SupriyaArun/squeezebert-uncased-finetuned-squad,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0471037253737449,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
17378,google/t5-efficient-small-nl24,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0549575183540581,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
17379,superb/hubert-base-superb-er,"The base model is hubert-base-ls960, which is pretrained on 16kHz  sampled speech audio",superb/hubert-base-superb-er,1.0,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
17380,hajime9652/xlnet-japanese,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.080410897731781,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
17381,groar/gpt-neo-1.3B-finetuned-escape2,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0928590446710586,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
17382,ErykWdowiak/GPTalian,And there's a lot of English in this dataset too,ErykWdowiak/GPTalian,1.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
17383,hfl/rbt3,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0397389139980077,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
17384,sentence-transformers/paraphrase-MiniLM-L3-v2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0837237387895584,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
17385,microsoft/layoutlm-large-uncased,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
17386,microsoft/layoutlm-base-cased,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0631272196769714,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
17387,mmaguero/gn-bert-tiny-cased,T5-Efficient-BASE-EL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el16,0.0834385603666305,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
17388,google/t5-efficient-small-nl40,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0652710236608981,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
17389,jean-paul/kinyaRoberta-small,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0018199251166411,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
17390,gilparmentier/pokemon_gptj_model,For more details please refer to the official website,flaubert/flaubert_small_cased,0.003635128494352,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
17391,uer/chinese_roberta_L-2_H-256,"This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper",uer/chinese_roberta_L-2_H-256,1.0,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
17392,SupriyaArun/squeezebert-uncased-finetuned-squad,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0611358173191547,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
17393,google/t5-efficient-base-nl40,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0835647210478782,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
17394,google/t5-efficient-large-nh32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0665574893355369,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
17395,hfl/rbt3,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0120622366666793,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
17396,fnlp/cpt-base,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0283164363354444,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
17397,aditeyabaral/sentencetransformer-bert-hinglish-small,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0167268700897693,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
17398,j-hartmann/purchase-intention-english-roberta-large,"This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:The model was fine-tuned on 2,000 manually annotated social media posts",j-hartmann/purchase-intention-english-roberta-large,1.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
17399,google/t5-efficient-base-nh24,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.083602413535118,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
17400,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0514104018608728,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
17401,google/t5-efficient-large-dm512,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0050544571131467,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
17402,lassl/gpt2-ko-small,This does not make a very readable text,pere/DeUnCaser,0.0267635397613048,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
17403,google/t5-efficient-small-nl20,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0300002684816718,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
17404,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0014748480170965,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17405,jpcorb20/pegasus-large-reddit_tifu-samsum-512,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
17406,DemangeJeremy/4-sentiments-with-flaubert,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0588888004422187,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17407,google/t5-efficient-base-nl40,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
17408,prajjwal1/bert-tiny,"They were introduced in the study Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (arxiv), and ported to HF for the study Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics (arXiv)",prajjwal1/bert-tiny,1.0,-1.0,NLP,55d35131-34b4-4b75-b6b3-69d02d502e5a___bert-tiny,HF
17409,google/t5-efficient-base-nl32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0540823880583047,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
17410,abdusahmbzuai/aradia-ctc-distilhubert-ft,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0874292701482772,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
17411,google/t5-efficient-large-nl12,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0472244266420602,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
17412,google/t5-efficient-xl-nl8,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0345575101673602,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
17413,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm128,0.0949383750557899,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
17414,google/t5-efficient-base-dm256,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0102733764797448,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
17415,ductuan024/AimeLaw,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.004468728788197,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
17416,google/t5-efficient-large-nh32,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0749429762363433,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
17417,skt/ko-gpt-trinity-1.2B-v0.5,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
17418,aditeyabaral/sentencetransformer-bert-hinglish-small,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0399991460144519,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
17419,ai4bharat/MultiIndicHeadlineGeneration,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,1.0,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
17420,microsoft/layoutlm-large-uncased,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0665121227502822,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
17421,KoichiYasuoka/roberta-base-ukrainian,The model might be used through the astonishing 🤗 Transformers librairie,asi/gpt-fr-cased-base,0.0792697146534919,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
17422,aubmindlab/aragpt2-mega,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0090673936065286,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
17423,google/t5-efficient-small-kv128,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0222656615078449,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
17424,google/t5-efficient-base-nh8,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0649450756609439,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
17425,cmarkea/distilcamembert-base-qa,"The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase for example",cmarkea/distilcamembert-base-qa,1.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
17426,arm-on/BERTweet-FA,"The model has been trained on the data only for 1 epoch (322906 steps), and yet it has the ability to recognize the meaning of most of the conversational sentences used in Farsi",arm-on/BERTweet-FA,1.0,-1.0,NLP,36f660a4-b743-4ce6-8f2c-d814d940e2b3___BERTweet-FA,HF
17427,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-MINI is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini,0.0565080121159553,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
17428,google/t5-efficient-small-nl20,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
17429,flax-community/gpt2-medium-persian,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0206144899129867,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
17430,suzuki/distilbert-base-uncased-finetuned-squad,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0902516618371009,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
17431,bigjoedata/rockbot,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
17432,krevas/finance-koelectra-base-discriminator,It can be used to pre-train transformer networks using relatively little compute,krevas/finance-koelectra-base-discriminator,1.0,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
17433,negfir/distilbert-base-uncased-finetuned-squad,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
17434,google/t5-efficient-base-dm256,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0317667927592993,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
17435,google/t5-efficient-base-el16,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0757720060646533,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
17436,KoichiYasuoka/roberta-base-ukrainian,This model is deprecated! Please use the identical model https://huggingface,facebook/blenderbot-90M,0.0076466109603643,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
17437,cammy/led-large-16384-arxiv-100-MDS,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
17438,diper1998/distilgpt2-finetuned-AT,The corpus was tokenized for pretraining with MeCab,ptaszynski/yacis-electra-small-japanese,0.0744766145944595,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
17439,camembert/camembert-base-oscar-4gb,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,1.0,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
17440,stas/mt5-tiny-random,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0340789705514907,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
17441,google/t5-efficient-base-nh32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0073072779923677,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
17442,google/t5-efficient-large-el6,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0402714507654308,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
17443,google/t5-efficient-large-nh8,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0813239328563213,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
17444,sberbank-ai/rugpt3large_based_on_gpt2,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0123389828950166,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
17445,google/t5-efficient-large-el2,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0581849291920661,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
17446,tartuNLP/gpt-4-est-base,I have made no attempts to censor,bigjoedata/rockbot,0.0645952895283698,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
17447,ErykWdowiak/GPTalian,The challenge of this project is to train a model to write the languages of Italy,ErykWdowiak/GPTalian,1.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
17448,brad1141/Longformer-finetuned-norm,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0177404060959815,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
17449,fnlp/bart-large-chinese,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.014344073832035,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
17450,dhpollack/distilbert-dummy-sentiment,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
17451,lgrobol/electra-minuscule-generator,Amazon's BORT,amazon/bort,0.066475287079811,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
17452,lgrobol/electra-minuscule-generator,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.005622811615467,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
17453,google/t5-large-ssm-nq,"The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Natural Questions (NQ)",google/t5-large-ssm-nq,1.0,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
17454,stas/mt5-tiny-random,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.056522287428379,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
17455,mikaelsouza/msft-regular-model,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0843531638383865,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
17456,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0114136897027492,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
17457,rdenadai/BR_BERTo,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
17458,google/t5-efficient-large-nl36,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
17459,rinna/japanese-gpt-1b,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
17460,google/t5-efficient-tiny-el8,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0446258056908845,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
17461,DemangeJeremy/4-sentiments-with-flaubert,"When using this model, make sure that your speech input is sampled at 16kHz",jonatasgrosman/wav2vec2-xls-r-1b-english,0.0525285433977842,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17462,pulp/CHILDES-ParentBERTo,T5-Efficient-XL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl2,0.0696651265025138,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
17463,google/t5-efficient-xl-nl16,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0179658979177474,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
17464,microsoft/layoutlm-base-cased,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0971509292721748,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
17465,mikaelsouza/msft-regular-model,This model is a fine-tuned version of  on the wikitext dataset,mikaelsouza/msft-regular-model,1.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
17466,tobiaslee/bert-2l-768h-uncased,This is a converted pytorch checkpoint for bert with 2L trained from scratch,tobiaslee/bert-2l-768h-uncased,1.0,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
17467,google/t5-efficient-large-kv16,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.055250659584999,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
17468,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0074733893076578,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
17469,rahulacj/bertweet-base-finetuned-sentiment-analysis,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0928254052996635,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
17470,google/t5-efficient-large-dm2000,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,1.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
17471,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
17472,Cedille/fr-boris,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.033720687807848,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
17473,beston91/gpt2-xl_ft_logits_10k,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0191966984421014,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
17474,SupriyaArun/squeezebert-uncased-finetuned-squad,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0919239819049835,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
17475,allenai/macaw-answer-11b,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0457531741509835,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
17476,anirudh21/xlnet-base-cased-finetuned-rte,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0757919549942016,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
17477,google/t5-efficient-base-nh24,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0615772269666194,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
17478,stas/mt5-tiny-random,Details will be available soon,VietAI/gpt-neo-1.3B-vietnamese-news,0.0823649242520332,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
17479,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.041592323531707,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
17480,gchhablani/fnet-base-finetuned-wnli,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0549897402524947,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
17481,GKLMIP/electra-myanmar-small-uncased,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.0366899902001023,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
17482,monsoon-nlp/dv-muril,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.0491843602309624,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
17483,google/t5-efficient-base-el6,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0315064257010817,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
17484,allenai/wmt19-de-en-6-6-big,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0125622409395873,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
17485,mimi/Waynehills-NLP-doogie,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0240754429250955,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
17486,google/t5-efficient-large-nh4,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0566522907465695,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
17487,malay-huggingface/t5-super-tiny-bahasa-cased,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0165974171832203,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
17488,google/t5-efficient-large-nl32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
17489,google/t5-efficient-xl-nl4,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.022170478478074,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
17490,tau/tavbert-he,We do hope our model might be used for both academic and industrial applications,asi/gpt-fr-cased-base,0.0497709652408957,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17491,tau/tavbert-he,T5-Efficient-LARGE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl32,0.0314770974218845,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17492,google/t5-efficient-large-kv16,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0592789258807897,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
17493,Sindhu/muril-large-squad2,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0104652466252445,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
17494,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0567079186439514,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
17495,superb/hubert-base-superb-er,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
17496,NovelAI/genji-python-6B,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0205741863076885,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
17497,SupriyaArun/squeezebert-uncased-finetuned-squad,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
17498,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0583484694361686,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
17499,brad1141/Longformer-finetuned-norm,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.059230450540781,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
17500,mimicheng/codeparrot-ds,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0111527815461158,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
17501,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm128,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17502,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"This is the set of Chinese T5 Version 1.1 models pre-trained by UER-py, which is introduced in this paper",uer/t5-v1_1-small-chinese-cluecorpussmall,0.0854916647076606,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
17503,google/t5-efficient-tiny-el8,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0215630177408456,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
17504,mwesner/reformer-clm,T5-Efficient-LARGE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl36,0.0898341387510299,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
17505,google/t5-efficient-base-el16,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0236939955502748,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
17506,hfl/rbt3,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0849009789526462,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
17507,google/t5-efficient-large-dm256,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0476815411821007,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
17508,piotr-rybak/poleval2021-task4-herbert-large-encoder,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17509,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Source language: English,NYTK/translation-marianmt-en-hu,0.0779165476560592,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
17510,tartuNLP/gpt-4-est-large,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0530668422579765,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
17511,google/t5-efficient-tiny-nl2,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0698848348110914,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
17512,indobenchmark/indobert-base-p1,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
17513,anas-awadalla/bert-tiny-finetuned-squad,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0757544115185737,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
17514,mrm8488/chEMBL_smiles_v1,"As mentioned in the paper: Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest",mrm8488/chEMBL_smiles_v1,1.0,-1.0,NLP,919d68f1-5654-4b4c-a9c7-f59f67ababe7___chEMBL_smiles_v1,HF
17515,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0231239572167396,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
17516,minimaxir/magic-the-gathering,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
17517,mwesner/bart-mlm,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0574157424271106,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
17518,crabz/distil-slovakbert-ner,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,0.0819299221038818,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
17519,google/t5-efficient-small-el12,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0097052734345197,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
17520,gchhablani/fnet-base-finetuned-wnli,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0909714251756668,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
17521,google/t5-efficient-small-nl2,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0596078094094991,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
17522,allenai/macaw-answer-11b,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
17523,thu-coai/LongLM-large,T5-Efficient-LARGE-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm128,0.0413122006381551,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
17524,DemangeJeremy/4-sentiments-with-flaubert,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0171550908125936,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17525,stas/pegasus-cnn_dailymail-tiny-random,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0864659771323204,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17526,uer/pegasus-base-chinese-cluecorpussmall,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0420622425153851,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
17527,google/t5-efficient-xl-nl6,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0665781199932098,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
17528,stas/pegasus-cnn_dailymail-tiny-random,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17529,mwesner/reformer-clm,"Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0600135177373886,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
17530,stas/t5-very-small-random,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0932254865765571,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
17531,antoiloui/belgpt2,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.042011210694909,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
17532,google/t5-efficient-small-ff3000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0583820473402738,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
17533,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-EL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el12,0.0948986113071441,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
17534,mrm8488/xlm-multi-finetuned-xquadv1,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0098206871189177,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
17535,saibo/random-roberta-tiny,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.040742767509073,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
17536,google/t5-efficient-tiny-el8,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0336785595864057,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
17537,google/t5-efficient-base-kv32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0271612331271171,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
17538,google/t5-efficient-large-nl10,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.073606826364994,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
17539,google/t5-efficient-large-dm256,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0321774631738662,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
17540,jorge-henao/gpt2-small-spanish-disco-poetry-wt,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.038566268980503,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
17541,stas/mt5-tiny-random,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0960597693920135,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
17542,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0712471902370452,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
17543,danyaljj/opengpt2_pytorch_forward,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0345875297983487,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
17544,google/t5-efficient-small-nl2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
17545,sberbank-ai/rugpt3medium_based_on_gpt2,Model was trained with sequence length 1024 using transformers lib by SberDevices team on 80B tokens for 3 epoch,sberbank-ai/rugpt3medium_based_on_gpt2,1.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
17546,saibo/random-roberta-tiny,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0292318547144532,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
17547,DemangeJeremy/4-sentiments-with-flaubert,The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co,rinna/japanese-gpt2-xsmall,0.0628832764923572,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17548,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
17549,google/t5-efficient-xxl-nl4,Its original repository can be found here,tau/splinter-large-qass,0.0503814071416854,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
17550,Milos/slovak-gpt-j-405M,"Important: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch)",pszemraj/led-large-book-summary,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
17551,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0765400305390358,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
17552,amazon/bort,The details of our training data are shown as follows,TsinghuaAI/CPM-Generate,0.0602950006723403,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17553,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0980327501893043,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17554,google/t5-efficient-base-nl4,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0615104883909225,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
17555,google/t5-efficient-xl-nl8,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.028137570247054,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
17556,microsoft/layoutlm-large-uncased,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,0.00856757722795,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
17557,google/t5-efficient-small-dm128,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0074659213423728,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
17558,mgreenbe/bertlet-base-uncased-for-sequence-classification,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0522821098566055,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
17559,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co,rinna/japanese-gpt2-xsmall,0.0774296820163726,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
17560,mimi/Waynehills-NLP-doogie,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0688423439860344,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
17561,mikaelsouza/msft-regular-model,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0267014931887388,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
17562,google/t5-efficient-base-nl32,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0915916040539741,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
17563,tau/tavbert-he,T5-Efficient-LARGE-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl20,0.0212959647178649,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17564,mrm8488/bert-small-finetuned-squadv2,BERT-Small created by Google Research and fine-tuned on SQuAD 2.0 for QandA downstream task,mrm8488/bert-small-finetuned-squadv2,1.0,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
17565,google/t5-efficient-xl-nl6,"6,860,062 examples are extracted from below corpora",lassl/roberta-ko-small,0.0669412985444068,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
17566,google/t5-efficient-xxl-nl4,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0701144449412822,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
17567,TJMUCH/transcriptome-iseeek,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0555079095065593,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
17568,GKLMIP/electra-myanmar-small-uncased,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0333120487630366,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
17569,google/t5-efficient-large-el4,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.079327780753374,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
17570,google/t5-efficient-mini-nl8,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0504635255783795,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
17571,google/t5-efficient-base-kv128,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0402513984590768,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
17572,VietAI/gpt-neo-1.3B-vietnamese-news,Both pytorch(pytorch_model bin) and Rust(rust_modelot) models are providedNOTE: Use T5Tokenizer to initiate the tokenizer,jweb/japanese-soseki-gpt2-1b,0.0113118290901184,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
17573,VietAI/gpt-neo-1.3B-vietnamese-news,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0676339603960513,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
17574,google/t5-efficient-small-dm2000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0458418102934956,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
17575,thu-coai/LongLM-large,T5-Efficient-TINY-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff6000,0.0504564588579038,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
17576,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff6000,0.0528880059719085,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
17577,google/bert_uncased_L-12_H-256_A-4,"We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large",google/bert_uncased_L-12_H-256_A-4,1.0,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
17578,hfl/rbtl3,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0567680709064006,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
17579,mrm8488/bert-small-finetuned-squadv2,Paper: SciFive: a text-to-text transformer model for biomedical literatureAuthors: Long N,razent/SciFive-large-Pubmed,0.0909777209162712,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
17580,GKLMIP/electra-myanmar-small-uncased,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0819433704018592,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
17581,google/t5-efficient-tiny-nh32,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0317537849768995,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
17582,tau/tavbert-he,"It will continue to be optimized in the direction of model scale, knowledge integration, and supervision task assistance",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0170720778405665,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17583,diper1998/distilgpt2-finetuned-AT,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
17584,google/t5-efficient-large-dm128,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
17585,amazon/bort,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0231918869540095,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17586,google/t5-efficient-small-ff9000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
17587,piotr-rybak/poleval2021-task4-herbert-large-encoder,Final perplexity on test set is 13.6,sberbank-ai/rugpt3large_based_on_gpt2,0.0990251898765564,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17588,google/t5-efficient-xl-nl16,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0940558463335037,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
17589,aditeyabaral/sentencetransformer-bert-hinglish-small,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
17590,mimi/Waynehills-NLP-doogie,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0970760732889175,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
17591,google/bert_uncased_L-6_H-256_A-4,I have made no attempts to censor,bigjoedata/rockbot,0.0217506180827816,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
17592,google/t5-efficient-base-nh8,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-nh8,1.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
17593,google/t5-efficient-small-kv32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
17594,google/t5-efficient-tiny-nh16,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0847920402884483,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
17595,google/t5-efficient-small-nl2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
17596,clue/roberta_chinese_3L312_clue_tiny,"It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed",phueb/BabyBERTa-2,0.0545044876635074,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
17597,google/t5-efficient-large-nh8,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
17598,amazon/bort,Pretrained BERT tiny language model for Malay,malay-huggingface/bert-tiny-bahasa-cased,0.0791066288948058,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17599,damlab/GO-language,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
17600,google/bert_uncased_L-8_H-256_A-4,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-256_A-4,1.0,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
17601,gayanin/bart-mlm-pubmed-medterm,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0907970294356346,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
17602,google/t5-efficient-large-dm256,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0610154811292886,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
17603,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0745649188756942,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
17604,google/t5-efficient-small-nl40,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0126783568412065,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
17605,google/t5-efficient-tiny-ff3000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0076357717625797,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
17606,IDEA-CCNL/Zhouwenwang-Unified-110M,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0505519919097423,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
17607,IDEA-CCNL/Erlangshen-Longformer-330M,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0174335604533553,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
17608,ml6team/mt5-small-german-finetune-mlsum,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
17609,google/t5-efficient-small-el32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0721554793417453,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
17610,enelpol/czywiesz-context,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,1.0,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
17611,sberbank-ai/rugpt3large_based_on_gpt2,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0315343779511749,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
17612,google/t5-efficient-xl-nl12,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0595089048147201,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
17613,M-FAC/bert-mini-finetuned-stsb,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
17614,hf-internal-testing/tiny-random-rembert,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0670267790555954,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
17615,cross-encoder/ms-marco-MiniLM-L-2-v2,"The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (eretrieved with ElasticSearch)",cross-encoder/ms-marco-MiniLM-L-2-v2,1.0,-1.0,NLP,c2d1bb44-2e13-4b2e-8b5d-23fa4c9a88e9___ms-marco-MiniLM-L-2-v2,HF
17616,rinna/japanese-gpt-1b,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0636834452549616,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
17617,microsoft/layoutlm-base-cased,org/abs/2202.08904The model was trained with the parameters:,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0798286348581314,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
17618,google/t5-efficient-xl-nl28,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0732152983546256,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
17619,ductuan024/AimeLaw,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0407869089394808,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
17620,TJMUCH/transcriptome-iseeek,The details of our training data are shown as follows,TsinghuaAI/CPM-Generate,0.0821107327938079,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
17621,Davlan/mbart50-large-eng-yor-mt,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,1.0,-1.0,NLP,12474c75-dc49-473e-bcb6-eea8354b1253___mbart50-large-eng-yor-mt,HF
17622,google/t5-efficient-tiny-el8,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0355248171836137,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
17623,google/t5-efficient-mini-nl6,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0323178255930542,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
17624,TJMUCH/transcriptome-iseeek,com/peterwestuw/GPT2ForwardBackward,danyaljj/opengpt2_pytorch_forward,0.0421561039984226,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
17625,IDEA-CCNL/Zhouwenwang-Unified-1.3B,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
17626,google/t5-efficient-small-dm1000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0570262894034385,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
17627,minimaxir/magic-the-gathering,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
17628,razent/SciFive-base-PMC,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.065527630969882,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
17629,piotr-rybak/poleval2021-task4-herbert-large-encoder,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17630,google/t5-efficient-small-kv32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.008695302531123,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
17631,filco306/gpt2-switchboard-paraphraser,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
17632,google/t5-efficient-large-nl12,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0455559436231851,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
17633,rahulacj/bertweet-base-finetuned-sentiment-analysis,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
17634,nyu-mll/roberta-med-small-1M-3,We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens),nyu-mll/roberta-med-small-1M-3,1.0,-1.0,NLP,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
17635,malay-huggingface/t5-small-bahasa-cased,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0274934209883213,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
17636,google/t5-efficient-mini,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0237571718171238,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
17637,google/t5-efficient-xl-nl12,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.070022840052843,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
17638,gchhablani/fnet-base-finetuned-wnli,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
17639,kyryl0s/gpt2-uk-xxs,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0520810391753911,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
17640,brad1141/Longformer-finetuned-norm,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
17641,google/t5-efficient-small-nl40,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0461156833916902,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
17642,google/t5-efficient-small-dm128,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0567181929945945,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
17643,arm-on/BERTweet-FA,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0084842736832797,-1.0,NLP,36f660a4-b743-4ce6-8f2c-d814d940e2b3___BERTweet-FA,HF
17644,google/t5-efficient-base-nl2,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0723658949136733,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
17645,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff12000,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17646,SupriyaArun/squeezebert-uncased-finetuned-squad,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0838628783822059,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
17647,cross-encoder/stsb-TinyBERT-L-4,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
17648,mmaguero/gn-bert-tiny-cased,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0756246522068977,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
17649,flboehm/reddit-bert-text_5,This model is a fine-tuned version of bert-base-uncased on an unknown dataset,flboehm/reddit-bert-text_5,1.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
17650,microsoft/layoutlm-base-uncased,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,0.0859688371419906,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
17651,MoritzLaurer/MiniLM-L6-mnli,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,1.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
17652,google/t5-efficient-tiny-nl2,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.049618306569755,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
17653,google/t5-efficient-small-kv16,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0339029263705015,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
17654,google/t5-efficient-large-dm512,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
17655,1one5ome/gpt2-chinese-gulong,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0152001082897186,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
17656,cross-encoder/nli-deberta-v3-xsmall,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,1.0,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
17657,google/t5-efficient-small-dm768,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0590413827449083,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
17658,akdeniz27/deberta-v2-xlarge-cuad,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0335764922201633,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
17659,google/byt5-xl,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0230377949774265,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
17660,TJMUCH/transcriptome-iseeek,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0370945893228054,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
17661,google/t5-efficient-small-kv16,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0655462704598903,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
17662,google/t5-efficient-tiny-nh16,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
17663,google/t5-efficient-small-nl24,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
17664,google/t5-efficient-base-nh24,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0706791095435619,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
17665,google/t5-efficient-small-ff12000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0661941356956958,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
17666,hfl/rbt3,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0125080924481153,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
17667,google/t5-efficient-small-nl2,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0665410682559013,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
17668,ian0delond/model-test,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0823512226343154,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
17669,stas/mt5-tiny-random,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0645606219768524,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
17670,stas/t5-very-small-random,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0158310290426015,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
17671,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0835492014884948,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17672,google/t5-efficient-large-dm512,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0502987746149301,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
17673,hfl/rbtl3,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0017882555257528,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
17674,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,This model is a distilled version of wav2vec2-xls-r-adult-child-cls on a private adult/child speech classification dataset,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,1.0,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
17675,M-FAC/bert-mini-finetuned-stsb,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0628834003582596,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
17676,google/t5-efficient-base-nl40,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0030936151742935,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
17677,VietAI/gpt-neo-1.3B-vietnamese-news,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0794676765799522,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
17678,allegro/plt5-large,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0800004253784815,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
17679,google/t5-efficient-small-nl22,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
17680,lassl/roberta-ko-small,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0098671421408653,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
17681,groar/gpt-neo-1.3B-finetuned-escape2,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0060461498796939,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
17682,google/t5-efficient-tiny-el8,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0701148360967635,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
17683,mwesner/bart-mlm,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0745991095900535,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
17684,stas/pegasus-cnn_dailymail-tiny-random,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0158610693179071,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17685,negfir/distilbert-base-uncased-finetuned-cola,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
17686,google/t5-efficient-xl-nl8,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0568087697029113,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
17687,jhu-clsp/bibert-ende,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0258844718337058,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
17688,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0701616555452346,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
17689,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"You can download the set of Chinese PEGASUS models either from the UER-py Modelzoo page, or via HuggingFace from the links below:You can use this model directly with a pipeline for text2text generation (take the case of PEGASUS-Base):",uer/pegasus-base-chinese-cluecorpussmall,0.0489560030400753,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
17690,NovelAI/genji-python-6B,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0356484868874152,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
17691,google/t5-efficient-base-nh16,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0760758742690086,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
17692,google/t5-efficient-large-el4,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0669939890503883,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
17693,Milos/slovak-gpt-j-405M,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0703945184747377,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
17694,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,0.0603739333649476,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
17695,bigjoedata/rockbot,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
17696,google/t5-efficient-xl-nl28,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0591873126104473,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
17697,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
17698,skplanet/dialog-koelectra-small-generator,"We hope to release other models, such as base/large models, in the future",skplanet/dialog-koelectra-small-generator,1.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
17699,rinna/japanese-gpt-1b,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
17700,KBLab/megatron-bert-large-swedish-cased-165k,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0226803760354717,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
17701,zoha/wav2vec2-base-timit-demo-colab,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0383297130465507,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
17702,google/t5-efficient-tiny-nh32,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0041467947885393,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
17703,jean-paul/kinyaRoberta-small,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0001706622008766,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
17704,microsoft/layoutlm-base-uncased,It is a two-stage approach consisting of a generator and a scorer,andrejmiscic/simcls-scorer-billsum,0.0665763467550277,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
17705,google/t5-efficient-small-nl36,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0768818315118551,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
17706,mrm8488/longformer-base-4096-spanish,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,47fa5077-4d92-4913-a31a-b5f621200d4b___longformer-base-4096-spanish,HF
17707,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0400485172867774,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
17708,google/t5-efficient-base-ff1000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.024579276330769,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
17709,CuongLD/wav2vec2-large-xlsr-vietnamese,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0759159661829471,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
17710,negfir/distilbert-base-uncased-finetuned-cola,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0863450393080711,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
17711,mmaguero/gn-bert-tiny-cased,T5-Efficient-LARGE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv32,0.0745471268892287,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
17712,piotr-rybak/poleval2021-task4-herbert-large-encoder,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17713,mimi/Waynehills-NLP-doogie,The corpus was tokenized for pretraining with MeCab,ptaszynski/yacis-electra-small-japanese,0.0849444046616554,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
17714,tau/tavbert-he,"For example, on SQuAD, the model is able to reach 80% F1 given only 128 examples, whereas the base model obtains only ~73%)",tau/splinter-large-qass,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17715,google/t5-efficient-small-ff1000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0708594918251037,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
17716,hfl/rbt3,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0228299004957079,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
17717,minimaxir/magic-the-gathering,Our findings highlight the significant effect of design choices on improving the performance of biomedical language models,sultan/BioM-ELECTRA-Large-Generator,0.0857187286019325,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
17718,google/byt5-xl,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0141290118917822,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
17719,mwesner/bart-mlm,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0971989780664444,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
17720,rahulacj/bertweet-base-finetuned-sentiment-analysis,I have made no attempts to censor,bigjoedata/rockbot,0.0465989410877227,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
17721,Iskaj/hf-challenge-test,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0232346430420875,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
17722,j-hartmann/purchase-intention-english-roberta-large,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
17723,mimi/Waynehills-NLP-doogie,It allows the model to learn a bidirectional representation of the sentence,macedonizer/al-roberta-base,0.0730731338262558,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
17724,amazon/bort,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0501953270286321,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17725,google/t5-efficient-base-kv128,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.003716755658388,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
17726,KoichiYasuoka/roberta-base-ukrainian,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
17727,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0230944249778985,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
17728,mrm8488/bert-small-finetuned-squadv2,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0560674592852592,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
17729,google/t5-efficient-base-kv16,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0060023106634616,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
17730,hfl/chinese-electra-180g-small-ex-discriminator,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
17731,andrejmiscic/simcls-scorer-billsum,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0122831984507766,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
17732,skt/ko-gpt-trinity-1.2B-v0.5,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0204118564724921,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
17733,eli4s/Bert-L12-h256-A4,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
17734,google/t5-efficient-small-kv16,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.076011698693037,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
17735,rdenadai/BR_BERTo,GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX,gilparmentier/pokemon_gptj_model,0.0090110898017883,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
17736,google/bigbird-roberta-large,It was introduced in this paper and first released in this repository,google/bigbird-roberta-large,1.0,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
17737,SupriyaArun/squeezebert-uncased-finetuned-squad,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0220590997487306,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
17738,DemangeJeremy/4-sentiments-with-flaubert,GPT2-base and medium uses the code from the gpt2 folder and can trains models from the minimaxir/gpt-2-simple repository,aubmindlab/aragpt2-mega,0.029135369695723,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17739,google/t5-efficient-large-dm768,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0266904337331653,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
17740,google/t5-efficient-small-nl36,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0588738843798637,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
17741,sonoisa/byt5-small-japanese,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0303001031279563,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
17742,lassl/roberta-ko-small,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0425941631197928,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
17743,stas/mt5-tiny-random,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0530094280838966,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
17744,google/t5-efficient-base-nl4,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0484766187146305,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
17745,wukevin/tcr-bert,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0622680373489856,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
17746,google/t5-efficient-small-nl40,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0239613465964794,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
17747,SupriyaArun/squeezebert-uncased-finetuned-squad,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0213432759046554,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
17748,google/t5-efficient-large-nl20,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0319008780643344,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
17749,google/t5-efficient-base-ff9000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0194718334823846,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
17750,Rostlab/prot_bert,"This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences",Rostlab/prot_bert,1.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
17751,mrm8488/deberta-v3-small-finetuned-mrpc,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0328212529420852,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
17752,google/t5-efficient-base-nl2,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0474592428654432,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
17753,GKLMIP/electra-myanmar-small-uncased,", MatMul, GELU, Softmax, and LayerNorm) with closely approximating integer operations",kssteven/ibert-roberta-large,0.0472770072519779,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
17754,anirudh21/xlnet-base-cased-finetuned-rte,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0830681920051574,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
17755,mgreenbe/bertlet-base-uncased-for-sequence-classification,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0753282308578491,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
17756,razent/SciFive-base-PMC,Paper: SciFive: a text-to-text transformer model for biomedical literatureAuthors: Long N,razent/SciFive-base-PMC,1.0,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
17757,IDEA-CCNL/Erlangshen-Longformer-330M,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0347184352576732,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
17758,IDEA-CCNL/Erlangshen-Longformer-110M,A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string,Parsa/BBB_prediction_classification_SMILES,0.0738177075982093,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
17759,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17760,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0508747734129428,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
17761,tobiaslee/bert-2l-768h-uncased,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.064368177205324,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
17762,google/t5-efficient-large-nl12,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0043790694326162,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
17763,facebook/hubert-xlarge-ll60k,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0283155841752886,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
17764,google/t5-efficient-large-el4,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0672290213406085,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
17765,ToToKr/kobigbird-bert-base-finetuned-klue,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
17766,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0890150517225265,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
17767,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NL10 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl10,0.007203310728073,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17768,google/reformer-crime-and-punishment,This is a ported version of  S3PRL's Hubert for the SUPERB Speaker Identification task,superb/hubert-large-superb-sid,0.0189169216901063,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
17769,google/t5-efficient-tiny-ff6000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.055101279169321,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
17770,stas/pegasus-cnn_dailymail-tiny-random,It allows the model to learn a bidirectional representation of the sentence,macedonizer/al-roberta-base,0.0723134502768516,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17771,google/t5-efficient-xl-nl12,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
17772,mmaguero/gn-bert-tiny-cased,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0706409253180026,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
17773,IDEA-CCNL/Zhouwenwang-Unified-1.3B,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.002593158185482,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
17774,akdeniz27/deberta-v2-xlarge-cuad,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0720113515853881,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
17775,cammy/led-large-16384-arxiv-100-MDS,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0980800464749336,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
17776,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0384293422102928,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
17777,stas/pegasus-cnn_dailymail-tiny-random,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0660145953297615,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17778,google/t5-efficient-base-nl2,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0797509588301181,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
17779,stas/pegasus-cnn_dailymail-tiny-random,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0145153440535068,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17780,google/t5-efficient-small-dm1000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
17781,tartuNLP/gpt-4-est-base,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0547665357589721,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
17782,piotr-rybak/poleval2021-task4-herbert-large-encoder,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
17783,google/t5-efficient-small-ff12000,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0402238983660936,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
17784,zoha/wav2vec2-base-timit-demo-colab,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.082868680357933,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
17785,mnaylor/bigbird-base-mimic-mortality,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
17786,aubmindlab/aragpt2-large,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0709519665688275,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
17787,aubmindlab/aragpt2-mega,GPT2-base and medium uses the code from the gpt2 folder and can trains models from the minimaxir/gpt-2-simple repository,aubmindlab/aragpt2-mega,1.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
17788,filco306/gpt2-switchboard-paraphraser,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0526703156065195,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
17789,VietAI/gpt-neo-1.3B-vietnamese-news,Any bias held by the imdb dataset may be reflected in the model's output,lannelin/bert-imdb-1hidden,0.0802123807370662,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
17790,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,1.0,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
17791,google/t5-efficient-small-dm128,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0556415170431136,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
17792,google/t5-efficient-base-nl36,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0433604270219802,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
17793,allenai/wmt16-en-de-12-1,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0147927380166948,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
17794,google/t5-efficient-large-nl20,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.047322854399681,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
17795,Gilles/FongBERT,FongBERT is a BERT model trained on 68.363 sentences in Fon,Gilles/FongBERT,1.0,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
17796,allenai/macaw-3b,"Macaw (Multi-angle c(q)uestion answering) is a ready-to-use model capable of  general question answering,  showing robustness outside the domains it was trained on",allenai/macaw-3b,1.0,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
17797,google/t5-efficient-small-nl2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
17798,skt/ko-gpt-trinity-1.2B-v0.5,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
17799,anas-awadalla/bert-tiny-finetuned-squad,Source language: English,NYTK/translation-marianmt-en-hu,0.0946894958615303,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
17800,google/t5-efficient-small-el16-dl4,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0497088134288787,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
17801,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,T5-Efficient-BASE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv32,0.0817933082580566,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
17802,mldmm/GlassBERTa,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0406784242950379,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
17803,google/t5-efficient-large-kv256,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,1.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
17804,google/t5-efficient-large-dm256,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0397189995273948,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
17805,Palak/microsoft_deberta-base_squad,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0178840700536966,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
17806,hfl/rbtl3,T5-Efficient-BASE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl36,0.0446757078170776,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
17807,clue/xlnet_chinese_large,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
17808,skt/ko-gpt-trinity-1.2B-v0.5,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
17809,google/t5-efficient-tiny-ff12000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
17810,google/t5-efficient-large-nh4,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0769785791635513,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
17811,tuner007/pegasus_qa,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0967583581805229,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
17812,diper1998/distilgpt2-finetuned-AT,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0273408852517604,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
17813,soheeyang/rdr-ctx_encoder-single-trivia-base,"Using this approach, the answer recall rate increases by a large margin, especially at small numbers of top-k",soheeyang/rdr-ctx_encoder-single-trivia-base,1.0,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
17814,Iskaj/hf-challenge-test,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0263943187892436,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
17815,cosmoquester/bart-ko-mini,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-mini,1.0,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
17816,google/t5-efficient-base-ff9000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0220947163179516,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
17817,superspray/electra_large_discriminator_squad2_custom_dataset,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.052318274974823,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
17818,IDEA-CCNL/Erlangshen-Longformer-110M,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.0186674697324633,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
17819,microsoft/layoutlm-base-uncased,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
17820,lgrobol/electra-minuscule-discriminator,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0751719456166028,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
17821,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0684467032551765,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
17822,ncduy/phobert-large-finetuned-vietnamese_students_feedback,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0288643948733806,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
17823,tau/tavbert-he,T5-Efficient-SMALL-EL64 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el64,0.020391103811562,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17824,ToToKr/kobigbird-bert-base-finetuned-klue,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0978532657027244,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
17825,pulp/CHILDES-ParentBERTo,py (Download Here) that define the architecture of CPT into your project,fnlp/cpt-base,0.0866486132144927,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
17826,qarib/bert-base-qarib_far_6500k,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,0.0267032971605658,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
17827,google/t5-efficient-large-nh32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.003340782597661,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
17828,lgrobol/electra-minuscule-generator,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0401934469118714,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
17829,amazon/bort,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,0.0225699469447135,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17830,google/t5-efficient-small-kv32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
17831,google/t5-efficient-large-dm2000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0396918319165705,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
17832,tau/tavbert-he,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.0407166406512259,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17833,NeuML/bert-small-cord19,"The training data is a subset of the full corpus, focusing on high-quality, study-design detected articles",NeuML/bert-small-cord19,1.0,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
17834,stas/mt5-tiny-random,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0094340648502111,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
17835,allenai/wmt19-de-en-6-6-big,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0614419691264629,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
17836,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0694612339138984,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
17837,microsoft/layoutlm-large-uncased,FongBERT is a BERT model trained on 68.363 sentences in Fon,Gilles/FongBERT,0.0859507396817207,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
17838,google/t5-efficient-tiny-nl24,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0494489725679158,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
17839,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0050254259258508,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
17840,crabz/distil-slovakbert-ner,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0194920636713504,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
17841,IDEA-CCNL/Erlangshen-Longformer-110M,This model is cased: it does make a difference between bulgarian and Bulgarian,rmihaylov/bert-base-pos-theseus-bg,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
17842,google/t5-efficient-tiny-nl24,T5-Efficient-TINY-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl24,1.0,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
17843,lassl/gpt2-ko-small,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
17844,google/t5-efficient-mini-nl8,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0593591444194316,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
17845,hyunwoongko/reddit-9B,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
17846,nielsr/coref-roberta-base,"This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",nielsr/coref-roberta-base,1.0,-1.0,NLP,8473d81a-bfd8-4703-b569-a14970be6a42___coref-roberta-base,HF
17847,anas-awadalla/bert-tiny-finetuned-squad,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0020024813711643,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
17848,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.06117844581604,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
17849,IDEA-CCNL/Erlangshen-Longformer-330M,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0595204569399356,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
17850,hfl/chinese-xlnet-mid,"As shown in the Metrics section, the small version (5 times fewer parameters) performs slightly worse, while the medium version (3 times fewer parameters) outperforms the BETO based zero-shot classifier",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
17851,google/t5-efficient-large-nl36,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0280993673950433,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
17852,google/t5-efficient-xl-nl4,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0251009464263916,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
17853,google/t5-efficient-small-kv16,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0916731804609298,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
17854,saurkulsh/T0pp,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0410424689097063,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
17855,google/t5-efficient-tiny-el8,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0164133915677666,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
17856,stas/t5-very-small-random,Use jcblaise/roberta-tagalog-base or jcblaise/roberta-tagalog-large instead for better performance,jcblaise/distilbert-tagalog-base-cased,0.0893502384424209,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
17857,google/t5-efficient-tiny-ff9000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
17858,abdusahmbzuai/aradia-ctc-distilhubert-ft,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0854784697294235,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
17859,groar/gpt-neo-1.3B-finetuned-escape2,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0411325171589851,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
17860,google/t5-efficient-small-ff6000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0079738171771169,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
17861,liaad/srl-pt_xlmr-base,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0477750493834416,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
17862,DemangeJeremy/4-sentiments-with-flaubert,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0476168431341647,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17863,google/t5-efficient-small-dm128,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
17864,sentence-transformers/paraphrase-MiniLM-L3-v2,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
17865,rdenadai/BR_BERTo,Portuguese (Brazil) model for text inference,rdenadai/BR_BERTo,1.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
17866,rdenadai/BR_BERTo,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,0.0198806319385767,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
17867,google/t5-efficient-xl-nl28,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0410645082592964,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
17868,google/t5-efficient-tiny-nl24,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0320319980382918,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
17869,TJMUCH/transcriptome-iseeek,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0116671258583664,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
17870,stas/pegasus-cnn_dailymail-tiny-random,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
17871,VietAI/gpt-neo-1.3B-vietnamese-news,"Encoder structure-based Bidirection language model, focusing on solving various natural language understanding tasks",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0733422823250292,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
17872,google/t5-efficient-large-el8,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
17873,google/t5-efficient-large-nh8,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0376460943371056,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
17874,google/t5-efficient-large-nh12,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0833520144224166,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
17875,uer/chinese_roberta_L-2_H-256,"Following their paper, we released the 24 Chinese RoBERTa models",uer/chinese_roberta_L-2_H-256,1.0,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
17876,superb/hubert-large-superb-sid,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0264574657194316,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
17877,allenai/wmt19-de-en-6-6-big,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.0074591562151908,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
17878,google/t5-efficient-large-kv256,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
17879,google/t5-efficient-small-nl24,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0827311426401138,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
17880,IDEA-CCNL/Erlangshen-Longformer-330M,By training a MLM from scratch on 438552 (cleaned*) SMILES I wanted to build a model that learns this kind of molecular combinations so that given a partial SMILE it can generate plausible combinations so that it can be proposed as new drugs,mrm8488/chEMBL_smiles_v1,0.0838717594742774,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
17881,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff9000,0.0743599385023117,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
17882,beston91/gpt2-xl_ft_logits_10k,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
17883,sentence-transformers/paraphrase-MiniLM-L3-v2,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0230944249778985,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
17884,google/t5-efficient-small-nl36,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.018101867288351,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
17885,Iskaj/hf-challenge-test,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0683009997010231,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
17886,negfir/distilbert-base-uncased-finetuned-cola,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0632185637950897,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
17887,google/t5-efficient-small-dm2000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.052728246897459,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
17888,beston91/gpt2-xl_ft_logits_10k,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0170114766806364,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
17889,cammy/led-large-16384-arxiv-100-MDS,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0384600050747394,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
17890,google/t5-efficient-xxl-nl4,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0344112645834684,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
17891,celine98/canine-s-finetuned-sst2,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0872930735349655,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
17892,brad1141/Longformer-finetuned-norm,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0931348651647567,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
17893,allegro/plt5-large,The models were optimized for the original T5 denoising target,allegro/plt5-large,1.0,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
17894,mrm8488/deberta-v3-small-finetuned-mrpc,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0771003440022468,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
17895,crabz/distil-slovakbert-ner,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0964273437857627,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
17896,google/t5-efficient-xl-nl28,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0415856726467609,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
17897,hfl/rbt3,T5-Efficient-BASE-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl24,0.0534084923565387,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
17898,google/t5-efficient-xl-nl6,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0958956480026245,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
17899,mwesner/reformer-clm,T5-Efficient-SMALL-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff9000,0.0943285077810287,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
17900,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0759099647402763,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17901,google/t5-efficient-base-nl36,This does not make a very readable text,pere/DeUnCaser,0.0353501308709383,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
17902,hf-internal-testing/tiny-random-rembert,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0927537903189659,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
17903,google/t5-efficient-mini-nl12,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0344350077211856,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
17904,google/t5-efficient-large-nl16,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0745178833603858,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
17905,google/t5-efficient-large-nh24,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0212310114875435,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
17906,facebook/blenderbot-3B,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
17907,google/t5-efficient-base-kv128,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
17908,skt/ko-gpt-trinity-1.2B-v0.5,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.05029499810189,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
17909,obss/mt5-base-3task-highlight-tquad2,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0417168997228145,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
17910,ToToKr/kobigbird-bert-base-finetuned-klue,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.083311453461647,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
17911,cross-encoder/ms-marco-MiniLM-L-4-v2,"Then, you can use the pre-trained models like this:",cross-encoder/ms-marco-MiniLM-L-4-v2,1.0,-1.0,NLP,b57a156c-f487-4efc-a83f-37bedaa63a0a___ms-marco-MiniLM-L-4-v2,HF
17912,malay-huggingface/t5-super-tiny-bahasa-cased,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0071828048676252,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
17913,google/t5-efficient-small-ff3000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0603167377412318,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
17914,google/t5-efficient-base-el6,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0297695212066173,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
17915,google/t5-efficient-tiny-nl32,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0707173943519592,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
17916,google/t5-efficient-large-el4,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0456568393856286,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
17917,google/t5-efficient-base-dm256,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0084430146962404,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
17918,tucan9389/kcbert-base-finetuned-squad,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
17919,google/t5-efficient-large-nl12,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0345948040485381,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
17920,google/t5-efficient-small-nl22,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0390305267646908,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
17921,google/t5-efficient-small-el12,Its original repository can be found here,tau/splinter-large-qass,0.044371236115694,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
17922,IDEA-CCNL/Erlangshen-Longformer-330M,The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1,nyu-mll/roberta-med-small-1M-3,0.0705797150731085,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
17923,google/t5-efficient-large-nl32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.04639438749291,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
17924,jorge-henao/gpt2-small-spanish-disco-poetry-wt,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.093927226960659,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
17925,flax-community/bengali-t5-base,We used the T5-base model for this model,flax-community/bengali-t5-base,1.0,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
17926,tuner007/pegasus_qa,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0417146645486354,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
17927,google/t5-efficient-xl-nl6,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0883514583110809,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
17928,google/t5-efficient-large-kv16,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0737815164029597,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
17929,oskrmiguel/mt5-simplification-spanish,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0590763762593269,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
17930,google/t5-efficient-small-el4,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0372966993600129,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
17931,DemangeJeremy/4-sentiments-with-flaubert,net - Pretrained Cross-Encoder,cross-encoder/nli-deberta-v3-xsmall,0.075505943968892,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17932,cammy/led-large-16384-arxiv-100-MDS,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
17933,google/t5-efficient-small-el64,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0203076303005218,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
17934,google/t5-efficient-base-ff9000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0309447855688631,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
17935,akdeniz27/deberta-v2-xlarge-cuad,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
17936,google/t5-efficient-xl-nl6,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0110475458204746,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
17937,lgrobol/electra-minuscule-generator,"Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0399860218167305,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
17938,malay-huggingface/t5-super-tiny-bahasa-cased,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
17939,pszemraj/led-large-book-summary,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0128561551682651,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
17940,TJMUCH/transcriptome-iseeek,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0637414827942848,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
17941,google/t5-efficient-base-nl40,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0610260721296071,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
17942,superspray/electra_large_discriminator_squad2_custom_dataset,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0817148685455322,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
17943,google/t5-efficient-large-dm2000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0549411140382289,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
17944,M-FAC/bert-mini-finetuned-stsb,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0521075977012514,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
17945,google/t5-efficient-tiny-el8,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0624273046851157,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
17946,tuner007/pegasus_qa,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0830916985869407,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
17947,google/t5-efficient-tiny-ff6000,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0523156989365815,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
17948,skt/ko-gpt-trinity-1.2B-v0.5,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
17949,antoiloui/belgpt2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
17950,TsinghuaAI/CPM-Generate,"[Project] [Model] [Paper]The text generated by CPM is automatically generated by a neural network model trained on a large number of texts, which does not represent the authors' or their institutes' official attitudes and preferences",TsinghuaAI/CPM-Generate,1.0,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
17951,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0525306425988674,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
17952,amazon/bort,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.090922649949789,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17953,google/t5-efficient-base-nh32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0594889111816882,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
17954,google/t5-efficient-large-kv128,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0400219662114978,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
17955,google/t5-efficient-base-dm512,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0100960098206997,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
17956,google/t5-efficient-large-kv16,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
17957,hfl/chinese-electra-small-ex-discriminator,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
17958,google/t5-efficient-small-ff12000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0590423233807086,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
17959,crabz/distil-slovakbert-ner,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0750610753893852,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
17960,NovelAI/genji-python-6B,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0245498828589916,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
17961,DemangeJeremy/4-sentiments-with-flaubert,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.043527565896511,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
17962,sarnikowski/convbert-medium-small-da-cased,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0584396421909332,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
17963,google/t5-efficient-large-el4,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0511313946917652,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
17964,filco306/gpt2-switchboard-paraphraser,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0369730072561651,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
17965,rahulacj/bertweet-base-finetuned-sentiment-analysis,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0576213523745536,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
17966,google/t5-efficient-small-ff12000,T5-Efficient-SMALL-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff12000,1.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
17967,nghuyong/ernie-tiny,The corpus was tokenized for pretraining with MeCab,ptaszynski/yacis-electra-small-japanese,0.0505636495848496,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
17968,google/t5-efficient-large-el2,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
17969,rahulacj/bertweet-base-finetuned-sentiment-analysis,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
17970,aditeyabaral/sentencetransformer-bert-hinglish-small,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0725870132446289,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
17971,google/t5-efficient-mini-nl6,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-mini-nl6,1.0,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
17972,mimi/Waynehills-NLP-doogie,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0200634878128767,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
17973,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,This model was trained on TPU and the details are as follows:With the help ofHuggingface-Transformers 2.5,clue/roberta_chinese_3L312_clue_tiny,0.0816842094063758,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
17974,google/t5-efficient-xl-nl8,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0818717665970324,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
17975,beston91/gpt2-xl_ft_logits_10k,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0802808403968811,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
17976,tau/tavbert-he,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0181922963820397,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17977,suzuki/distilbert-base-uncased-finetuned-squad,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0988826751708984,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
17978,google/t5-efficient-small-el12,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0467902440577745,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
17979,tuner007/pegasus_qa,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0676834136247634,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
17980,hf-internal-testing/tiny-random-rembert,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0493426769971847,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
17981,google/t5-efficient-base-nh8,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0160791333764791,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
17982,uer/t5-v1_1-small-chinese-cluecorpussmall,This is a sentence-transformers model: It maps sentences and paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search,sentence-transformers/paraphrase-MiniLM-L3-v2,0.0485236383974551,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
17983,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0881038680672645,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
17984,allenai/macaw-answer-11b,I have made no attempts to censor,bigjoedata/rockbot,0.0457205101847648,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
17985,tau/tavbert-he,This model is a fine-tuned version of google/bigbird-pegasus-large-bigpatent on the pub_med_summarization_dataset dataset,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,0.0598749164491891,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
17986,KoichiYasuoka/roberta-base-ukrainian,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.0955187603831291,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
17987,mrm8488/bert-small-finetuned-squadv2,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0398828657343983,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
17988,aubmindlab/aragpt2-large,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
17989,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0217760801315307,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
17990,amazon/bort,I have made no attempts to censor,bigjoedata/rockbot,0.0479192286729812,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
17991,microsoft/layoutlm-large-uncased,It has less than 1% difference in performance compared to the BERT based model while having 2.0x the speed,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
17992,jonatasgrosman/wav2vec2-xls-r-1b-english,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0819378395875294,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
17993,macedonizer/al-roberta-base,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
17994,clue/roberta_chinese_3L312_clue_tiny,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0296104252338409,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
17995,lgrobol/electra-minuscule-discriminator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
17996,mwesner/reformer-clm,"Using this approach, the answer recall rate increases by a large margin, especially at small numbers of top-k",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0877214521169662,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
17997,google/t5-efficient-tiny-nh32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0588541738688945,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
17998,kyryl0s/gpt2-uk-xxs,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.062143325805664,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
17999,hfl/chinese-electra-180g-small-generator,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0522211243708928,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
18000,camembert/camembert-base-oscar-4gb,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0737242971857388,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
18001,google/t5-efficient-base-nl4,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
18002,rdenadai/BR_BERTo,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0667069262514511,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
18003,mimicheng/codeparrot-ds,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0521705113351345,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
18004,hfl/chinese-electra-180g-small-generator,I have made no attempts to censor,bigjoedata/rockbot,0.0041386261582374,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
18005,google/t5-efficient-large-nl36,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
18006,kyryl0s/gpt2-uk-xxs,I used multiple datasets for generalizing the model for both colloquial and written texts,cosmoquester/bart-ko-small,0.0376274548470973,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
18007,stas/pegasus-cnn_dailymail-tiny-random,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0412366604432463,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
18008,google/t5-efficient-large-el8,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0603346824645995,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
18009,hfl/chinese-xlnet-mid,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
18010,google/t5-efficient-small-nl40,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
18011,minimaxir/magic-the-gathering,It can be considered as tiny copy of Conversational DistilRuBERT-small,DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0213934169150889,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
18012,FremyCompany/xls-r-nl-v1-cv8-lm,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0716842847565809,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
18013,google/t5-efficient-small-el16-dl4,This does not make a very readable text,pere/DeUnCaser,0.0570060014724731,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
18014,everdoubling/byt5-Korean-small,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0544105842709541,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
18015,google/t5-efficient-xl-nl4,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0958956480026245,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
18016,google/t5-efficient-tiny-nl24,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0658561568707227,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
18017,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0079487077891826,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
18018,VietAI/gpt-neo-1.3B-vietnamese-news,"To use CPT, please import the file modeling_cpt",fnlp/cpt-base,0.0524203050881623,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
18019,IDEA-CCNL/Erlangshen-Longformer-110M,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0826204121112823,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
18020,rinna/japanese-gpt-1b,This is a sentence-transformers model: It maps sentences and paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search,piotr-rybak/poleval2021-task4-herbert-large-encoder,0.0658183246850967,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
18021,hfl/rbtl3,ipynb and the weights were converted to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead,google/reformer-crime-and-punishment,0.0729089640080928,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18022,google/t5-efficient-xl-nl16,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0772984325885772,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
18023,sonoisa/byt5-small-japanese,Its original repository can be found here,tau/splinter-large-qass,0.0297322794795036,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
18024,lgris/distilxlsr_bp_8-12,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
18025,google/t5-efficient-tiny-ff12000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0548537075519561,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
18026,stas/t5-very-small-random,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0595743022859096,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
18027,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm128,0.0809331089258193,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
18028,google/t5-efficient-large-kv32,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.002843776717782,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
18029,google/byt5-large,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0125498543493449,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
18030,izumi-lab/bert-small-japanese-fin,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0914952605962752,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
18031,stas/t5-very-small-random,"To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus",FremyCompany/xls-r-nl-v1-cv8-lm,0.0643678605556488,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
18032,ian0delond/model-test,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
18033,google/t5-efficient-xl-nl2,This does not make a very readable text,pere/DeUnCaser,0.0489930305629968,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
18034,hfl/rbtl3,T5-Efficient-LARGE-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl16,0.0619856733828782,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18035,google/t5-efficient-small-el8,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
18036,akdeniz27/deberta-v2-xlarge-cuad,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0635303929448127,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
18037,tartuNLP/gpt-4-est-large,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0594260990619659,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
18038,google/t5-efficient-base-nl48,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0526725146919488,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
18039,tau/tavbert-he,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18040,razent/SciFive-large-Pubmed,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0619559623301028,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
18041,google/t5-efficient-small-nl40,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0679481085389852,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
18042,google/t5-efficient-small-nl24,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.07446626201272,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
18043,sonoisa/byt5-small-japanese,Amazon's BORT,amazon/bort,0.0781351700425148,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
18044,google/t5-efficient-mini-nl8,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0669581480324267,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
18045,google/t5-efficient-small-nl20,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0442153718322515,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
18046,TJMUCH/transcriptome-iseeek,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0564319416880607,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18047,deep-learning-analytics/triviaqa-t5-base,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.044917828004275,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
18048,aliosm/ComVE-gpt2-large,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0109377193730324,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
18049,stas/pegasus-cnn_dailymail-tiny-random,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0012033395469189,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
18050,dbmdz/electra-small-turkish-cased-discriminator,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
18051,google/t5-efficient-large-nl36,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0301549732685088,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
18052,google/t5-efficient-large-nl10,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0659055560827255,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
18053,facebook/m2m100-12B-avg-10-ckpt,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0198168326169252,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
18054,piotr-rybak/poleval2021-task4-herbert-large-encoder,This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset,groar/gpt-neo-1.3B-finetuned-escape2,0.087855614721775,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
18055,beston91/gpt2-xl_ft_logits_10k,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
18056,bookbot/distil-wav2vec2-adult-child-cls-37m,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0482343873009085,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
18057,MoritzLaurer/MiniLM-L6-mnli,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.040419145176808,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
18058,google/t5-efficient-tiny-nl24,This does not make a very readable text,pere/DeUnCaser,0.0722608938813209,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
18059,google/t5-efficient-small-dm128,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0877119600772857,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
18060,negfir/distilbert-base-uncased-finetuned-cola,It can be considered as small copy of Conversational DistilRuBERT-base,DeepPavlov/distilrubert-tiny-cased-conversational,0.0863981023430824,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
18061,gayanin/bart-mlm-pubmed-medterm,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
18062,mrm8488/longformer-base-4096-spanish,This model was made following the research done by Iz Beltagy and Matthew E,mrm8488/longformer-base-4096-spanish,1.0,-1.0,NLP,47fa5077-4d92-4913-a31a-b5f621200d4b___longformer-base-4096-spanish,HF
18063,jkeruotis/LitBERTa-uncased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
18064,suzuki/distilbert-base-uncased-finetuned-squad,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0850351750850677,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
18065,brad1141/Longformer-finetuned-norm,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0937595665454864,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
18066,stas/t5-very-small-random,"To improve the capacities of both the encoder and decoder, we propose to train LongLM with two pretraining tasks including text inﬁlling (Raffel et al",thu-coai/LongLM-large,0.0718619972467422,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
18067,google/t5-efficient-base-ff12000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0506681911647319,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
18068,hfl/chinese-electra-small-ex-discriminator,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
18069,google/t5-efficient-large-nl10,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0517517821863293,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
18070,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.062628798186779,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
18071,asi/gpt-fr-cased-base,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
18072,microsoft/layoutlm-base-uncased,This is a ported version of  S3PRL's Hubert for the SUPERB Speaker Identification task,superb/hubert-large-superb-sid,0.0181695334613323,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
18073,TJMUCH/transcriptome-iseeek,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18074,google/t5-efficient-large-kv128,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0605091992765664,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
18075,google/t5-efficient-large-kv32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
18076,hfl/rbtl3,"If you found this model useful, please cite the original work:",filco306/gpt2-switchboard-paraphraser,0.0638863407075405,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18077,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
18078,razent/SciFive-base-PMC,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0643812417984008,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
18079,google/t5-efficient-tiny-ff3000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
18080,facebook/blenderbot-90M,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
18081,everdoubling/byt5-Korean-small,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,1.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
18082,malay-huggingface/t5-tiny-bahasa-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0448176758363842,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
18083,mgreenbe/bertlet-base-uncased-for-sequence-classification,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0625842586159706,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
18084,google/t5-efficient-small-nl48,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
18085,suzuki/distilbert-base-uncased-finetuned-squad,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0713598877191543,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
18086,microsoft/layoutlm-large-uncased,"MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks",mrm8488/mobilebert-uncased-finetuned-squadv2,0.0287247318774461,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
18087,google/t5-efficient-large-kv32,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0842913836240768,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
18088,google/bert_uncased_L-10_H-768_A-12,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0170423748592535,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
18089,google/t5-efficient-large-dm768,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0342926774173974,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
18090,google/t5-efficient-tiny-nl32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0598611431196331,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
18091,groar/gpt-neo-1.3B-finetuned-escape2,This is a sentence-transformers model: It maps sentences and paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search,piotr-rybak/poleval2021-task4-herbert-large-encoder,0.087855614721775,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
18092,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0809638574719429,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
18093,eli4s/Bert-L12-h256-A4,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0709568010643124,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
18094,stas/mt5-tiny-random,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0921973437070846,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
18095,aubmindlab/aragpt2-large,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0493362685665488,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
18096,superb/hubert-large-superb-sid,This is a ported version of  S3PRL's Hubert for the SUPERB Speaker Identification task,superb/hubert-large-superb-sid,1.0,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
18097,IDEA-CCNL/Erlangshen-Longformer-330M,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
18098,ml6team/mt5-small-german-finetune-mlsum,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
18099,damlab/GO-language,This model was built as a way to encode the Gene Ontology definition of a protein as vector representation,damlab/GO-language,1.0,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
18100,filco306/gpt2-switchboard-paraphraser,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0492807240225374,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
18101,jonatasgrosman/wav2vec2-xls-r-1b-english,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0165734713276227,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
18102,google/t5-efficient-large-dm256,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0322616389021277,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
18103,microsoft/layoutlm-base-uncased,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0488218665122985,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
18104,crabz/distil-slovakbert-ner,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.028864623978734,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
18105,fnlp/bart-large-chinese,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0379490237683057,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
18106,google/t5-efficient-large-nl32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0447230320423841,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
18107,superb/hubert-large-superb-sid,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0713656712323426,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
18108,diper1998/distilgpt2-finetuned-AT,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0317796766757965,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
18109,saurkulsh/T0pp,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
18110,microsoft/layoutlm-large-uncased,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0218782499432563,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
18111,google/t5-efficient-large-nh4,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.055538760498166,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
18112,diper1998/distilgpt2-finetuned-AT,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
18113,zoha/wav2vec2-base-timit-demo-colab,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0711625814437866,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
18114,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0033792816102504,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
18115,rinna/japanese-gpt2-xsmall,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0138249266892671,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
18116,brad1141/Longformer-finetuned-norm,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0693762972950935,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
18117,bolbolzaban/gpt2-persian,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0807255990803241,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
18118,UWB-AIR/MQDD-duplicates,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,1.0,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
18119,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl16,0.0753704458475112,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
18120,kyryl0s/gpt2-uk-xxs,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0500620640814304,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
18121,google/t5-efficient-base-nh16,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0291217919439077,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
18122,google/t5-efficient-large-kv256,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
18123,TJMUCH/transcriptome-iseeek,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0486734062433242,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18124,google/t5-efficient-base-dm256,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0349459545686841,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
18125,google/t5-efficient-xl-nl12,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0697354897856711,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
18126,google/t5-efficient-xl-nl16,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0914437621831893,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
18127,google/t5-efficient-small-kv128,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0927004367113112,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
18128,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0166149102151393,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
18129,GKLMIP/electra-myanmar-small-uncased,This model is a rather small instance of GPT-2 trained on TristanBehrens/js-fakes-4bars,TristanBehrens/js-fakes-4bars,0.055987287312746,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
18130,google/t5-efficient-small-nl22,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
18131,google/t5-efficient-large-dm768,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
18132,obss/mt5-base-3task-highlight-tquad2,Note: flaubert-small-cased is partially trained so performance is not guaranteed,flaubert/flaubert_small_cased,0.0547661297023296,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
18133,dbmdz/bert-medium-historic-multilingual-cased,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0103657022118568,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
18134,akdeniz27/deberta-v2-xlarge-cuad,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0173918418586254,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
18135,akdeniz27/deberta-v2-xlarge-cuad,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0564205422997474,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
18136,google/t5-efficient-large-kv16,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0434988643974065,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
18137,IDEA-CCNL/Erlangshen-Longformer-110M,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0560155138373374,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
18138,google/t5-efficient-xl-nl28,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0926387347280979,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
18139,cross-encoder/nli-deberta-v3-xsmall,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0549301460385322,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
18140,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0841065123677253,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
18141,negfir/distilbert-base-uncased-finetuned-squad,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0695789605379104,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
18142,google/bert_uncased_L-10_H-256_A-4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
18143,Parsa/BBB_prediction_classification_SMILES,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0537761921683947,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
18144,google/t5-efficient-base-dm512,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0466526597738265,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
18145,diper1998/distilgpt2-finetuned-AT,"This is one of the smaller pre-trained BERT variants, together with bert-tiny, bert-mini and bert-small",prajjwal1/bert-medium,0.0927518308162689,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
18146,microsoft/layoutlm-base-uncased,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0318005234003067,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
18147,tartuNLP/gpt-4-est-large,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0584697425365448,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
18148,fnlp/bart-large-chinese,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0822699964046478,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
18149,lgrobol/electra-minuscule-generator,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0650122426450252,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
18150,amazon/bort,Masked language modeling(MLM) and sentence order prediction(SOP) are used as training task,junnyu/roformer_base_wwm_cluecorpussmall,0.0331666115671395,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18151,google/reformer-crime-and-punishment,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0182852665893733,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
18152,negfir/distilbert-base-uncased-finetuned-squad,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
18153,mrp/simcse-model-wangchanberta,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0835508853197097,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
18154,allenai/macaw-3b,Its original repository can be found here,tau/splinter-large-qass,0.0329057872295379,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
18155,google/t5-efficient-xxl-nl4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0288789882324635,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
18156,google/t5-efficient-large-kv16,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
18157,google/t5-efficient-mini-nl24,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0275553185492753,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
18158,mikaelsouza/msft-regular-model,Its original repository can be found here,tau/splinter-large-qass,0.0426587909460067,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
18159,neuralspace-reverie/indic-transformers-bn-xlmroberta,"This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc",neuralspace-reverie/indic-transformers-bn-xlmroberta,1.0,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
18160,google/t5-efficient-tiny-nl24,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
18161,IDEA-CCNL/Erlangshen-Longformer-330M,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.073177296668291,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
18162,aditeyabaral/sentencetransformer-bert-hinglish-small,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0917815119028091,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
18163,google/t5-efficient-small-dm1000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.049890974536538,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
18164,FremyCompany/xls-r-nl-v1-cv8-lm,Its original repository can be found here,tau/splinter-large-qass,0.0443452820181846,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
18165,allenai/macaw-answer-11b,"However, this model outperforms the base model by large margins",tau/splinter-large-qass,0.082473957290252,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
18166,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0784818902611732,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
18167,google/t5-efficient-xl-nl2,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0580776836723089,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
18168,zoha/wav2vec2-base-timit-demo-colab,This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset,zoha/wav2vec2-base-timit-demo-colab,1.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
18169,bolbolzaban/gpt2-persian,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0437901755794882,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
18170,google/t5-efficient-base-dm2000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0519254878163337,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
18171,gilparmentier/pokemon_gptj_model,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
18172,google/t5-efficient-small-nl48,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
18173,kyryl0s/gpt2-uk-xxs,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
18174,google/t5-efficient-base-el6,This does not make a very readable text,pere/DeUnCaser,0.0436556707136332,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
18175,google/t5-efficient-small-el64,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0165680181235074,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
18176,TJMUCH/transcriptome-iseeek,Note: this model is the ConvBERT generator model intented to be used for the fill-mask task,Finnish-NLP/convbert-base-generator-finnish,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18177,DeepPavlov/distilrubert-tiny-cased-conversational-v1,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,1.0,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
18178,google/t5-efficient-base-ff1000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0004819147288799,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
18179,IDEA-CCNL/Erlangshen-Longformer-330M,This is a ported version of  S3PRL's Hubert for the SUPERB Emotion Recognition task,superb/hubert-base-superb-er,0.0659015551209449,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
18180,google/t5-efficient-base-ff12000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
18181,google/t5-efficient-large-kv128,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0035594580695033,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
18182,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,I have made no attempts to censor,bigjoedata/rockbot,0.0375713532169659,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
18183,google/t5-efficient-tiny-nh8,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0485205221921205,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
18184,indobenchmark/indobert-base-p1,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0530344458917776,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
18185,akdeniz27/deberta-v2-xlarge-cuad,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0654906332492828,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
18186,piotr-rybak/poleval2021-task4-herbert-large-encoder,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0431857928633689,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
18187,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0520046949386596,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
18188,google/t5-efficient-mini-nl6,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0129088051617145,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
18189,microsoft/layoutlm-base-uncased,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-8_H-768_A-12,0.0839941129088401,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
18190,lgrobol/electra-minuscule-generator,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
18191,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"In the v2 version, the Portuguese dataset was translated using Google Translate",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,1.0,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
18192,google/bert_uncased_L-8_H-128_A-2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0481574097648262,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
18193,gayanin/bart-mlm-pubmed-medterm,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0364208966493606,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
18194,khizon/distilbert-unreliable-news-eng-4L,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0157901826314628,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
18195,google/t5-efficient-small-ff3000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0058276290073991,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
18196,ebagdasa/propaganda_positive_bart_large,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0589930688341458,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
18197,DemangeJeremy/4-sentiments-with-flaubert,Model is based on GPT-J and has over 162M trainable parameters,Milos/slovak-gpt-j-162M,0.0526007805019616,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18198,skt/ko-gpt-trinity-1.2B-v0.5,"Important: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch)",pszemraj/led-large-book-summary,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
18199,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.039264291524887,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
18200,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0706398338079452,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
18201,Sindhu/muril-large-squad2,Generated from the official Squad2 evaluation scriptMuRIL is specifically trained to work on 18 Indic languages and English,Sindhu/muril-large-squad2,1.0,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
18202,google/t5-efficient-small-nl20,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0874179154634475,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
18203,google/t5-efficient-large-dm128,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-dm128,1.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
18204,google/t5-efficient-base-dm512,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0880734138190746,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
18205,tartuNLP/gpt-4-est-large,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0682430565357208,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
18206,stas/mt5-tiny-random,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0700602605938911,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
18207,google/t5-efficient-base-dm256,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0471589043736457,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
18208,bigjoedata/rockbot,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0183239839971065,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
18209,google/t5-efficient-large-nh12,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
18210,gayanin/bart-mlm-pubmed-medterm,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0721204727888107,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
18211,hfl/chinese-electra-180g-small-ex-generator,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
18212,CuongLD/wav2vec2-large-xlsr-vietnamese,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0534855034202337,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
18213,negfir/distilbert-base-uncased-finetuned-cola,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0366999134421348,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
18214,google/t5-efficient-base-ff6000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0147028733044862,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
18215,DemangeJeremy/4-sentiments-with-flaubert,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.039002466481179,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18216,DTAI-KULeuven/robbertje-1-gb-bort,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
18217,nghuyong/ernie-tiny,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0720336772501468,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
18218,google/t5-efficient-xxl-nl4,T5-Efficient-XXL-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xxl-nl4,1.0,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
18219,TJMUCH/transcriptome-iseeek,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18220,beston91/gpt2-xl_ft_logits_10k,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
18221,IDEA-CCNL/Erlangshen-Longformer-330M,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0114896669983863,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
18222,google/t5-efficient-xl-nl4,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0912656188011169,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
18223,google/t5-efficient-mini,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0772622227668761,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
18224,rinna/japanese-gpt2-xsmall,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
18225,hfl/rbtl3,Model is based on GPT-J and has over 162M trainable parameters,Milos/slovak-gpt-j-162M,0.0716580599546432,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18226,akdeniz27/deberta-v2-xlarge-cuad,"Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example",facebook/m2m100-12B-avg-10-ckpt,0.058472067117691,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
18227,stas/t5-very-small-random,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.034271240234375,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
18228,nherve/flaubert-oral-asr,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0337062015508611,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
18229,thu-coai/LongLM-large,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
18230,tuner007/pegasus_qa,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0686673745512962,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
18231,stas/mt5-tiny-random,"To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus",FremyCompany/xls-r-nl-v1-cv8-lm,0.0410108976066112,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
18232,google/t5-efficient-base-kv32,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.067415278404951,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
18233,everdoubling/byt5-Korean-small,"For further models, scripts and details, see our repository or our demo site",NYTK/translation-marianmt-en-hu,0.0509579222649335,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
18234,razent/SciFive-large-Pubmed,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0819265209138393,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
18235,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-XL-NL28 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl28,0.0371062811464071,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18236,pszemraj/led-large-book-summary,This repository provides an extra-small-sized Japanese GPT-2 model,rinna/japanese-gpt2-xsmall,0.0781692890450358,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
18237,google/t5-efficient-xl-nl16,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
18238,tau/tavbert-he,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0112121067941188,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18239,google/t5-efficient-large-el6,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.013504097238183,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
18240,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-NH8-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8-nl32,0.084440566599369,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
18241,hfl/chinese-xlnet-mid,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,1.0,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
18242,google/t5-efficient-large-nl20,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0324164796620607,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
18243,mrm8488/deberta-v3-small-finetuned-mrpc,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0658886432647705,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
18244,google/t5-efficient-large-kv256,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0211367197334766,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
18245,google/t5-efficient-large-nh12,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
18246,hfl/rbtl3,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18247,skt/ko-gpt-trinity-1.2B-v0.5,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
18248,neuralspace-reverie/indic-transformers-bn-xlmroberta,This is a XLMRoBERTa language model pre-trained on ~3 GB of monolingual training corpus,neuralspace-reverie/indic-transformers-bn-xlmroberta,1.0,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
18249,flboehm/reddit-bert-text_5,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0668808221817016,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
18250,rinna/japanese-gpt2-xsmall,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.05515593662858,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
18251,microsoft/layoutlm-base-cased,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0665121227502822,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
18252,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
18253,lgrobol/electra-minuscule-generator,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0700282119214533,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
18254,TJMUCH/transcriptome-iseeek,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0577904433012008,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18255,ductuan024/AimeLaw,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0834218934178352,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
18256,google/t5-efficient-large-dm512,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
18257,NovelAI/genji-python-6B,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0251202282185355,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
18258,google/t5-efficient-xl-nl28,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0647902637720107,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
18259,allenai/wmt16-en-de-12-1,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0101363472640514,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
18260,rahulacj/bertweet-base-finetuned-sentiment-analysis,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0287457481026649,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
18261,google/t5-efficient-small-el8,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0250930786132812,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
18262,Sindhu/muril-large-squad2,This model is not expected to perform well in any other languages,Sindhu/muril-large-squad2,1.0,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
18263,google/t5-efficient-small-ff9000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0601550638675689,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
18264,ToToKr/kobigbird-bert-base-finetuned-klue,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0520919188857078,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
18265,amazon/bort,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0501270513050258,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18266,google/t5-efficient-tiny-ff3000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
18267,minimaxir/magic-the-gathering,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0947113335132598,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
18268,google/t5-efficient-small-dm128,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0207493538036942,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
18269,obss/mt5-base-3task-highlight-tquad2,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,1.0,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
18270,google/t5-efficient-base-nh8,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0430643390864133,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
18271,google/t5-efficient-xl-nl6,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0887324288487434,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
18272,google/t5-efficient-small-nl20,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0739202201366424,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
18273,microsoft/layoutlm-base-uncased,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0723494589328765,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
18274,SupriyaArun/squeezebert-uncased-finetuned-squad,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0157182607799768,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
18275,google/t5-efficient-small-nl22,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0374731849879026,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
18276,skplanet/dialog-koelectra-small-generator,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0191829523071646,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
18277,NeuML/bert-small-cord19,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.012363877147436,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
18278,DemangeJeremy/4-sentiments-with-flaubert,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0769282691180705,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18279,DemangeJeremy/4-sentiments-with-flaubert,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18280,google/t5-efficient-small-nl20,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0324167087674141,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
18281,malay-huggingface/xlnet-large-bahasa-cased,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0293719743688901,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
18282,google/t5-efficient-tiny-nl24,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0349486302584409,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
18283,google/t5-efficient-small-dm768,T5-Efficient-SMALL-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm768,1.0,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
18284,GKLMIP/electra-myanmar-small-uncased,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,0.0543108191341161,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
18285,google/t5-efficient-tiny-ff12000,T5-Efficient-TINY-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff12000,1.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
18286,junnyu/roformer_base_wwm_cluecorpussmall,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0011875561128059,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
18287,google/t5-efficient-mini-nl6,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0788982547819614,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
18288,google/t5-efficient-tiny-ff12000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0550091806799173,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
18289,google/t5-efficient-base-nh8,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0325529959518461,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
18290,mimicheng/codeparrot-ds,This does not make a very readable text,pere/DeUnCaser,0.0637480020523071,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
18291,google/t5-efficient-large-nl36,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0664133373647928,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
18292,google/t5-efficient-small-kv32,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0109780058264732,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
18293,mwesner/reformer-clm,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0526919849216938,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
18294,google/t5-efficient-base-nl24,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0699891000986099,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
18295,stas/pegasus-cnn_dailymail-tiny-random,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0802746936678886,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
18296,tau/tavbert-he,T5-Efficient-XXL-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xxl-nl4,0.0450680386275052,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18297,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0599273592233657,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
18298,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0549623668193817,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
18299,google/t5-efficient-large-nh24,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0507764518260955,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
18300,tartuNLP/gpt-4-est-large,I have made no attempts to censor,bigjoedata/rockbot,0.0097692236304283,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
18301,mwesner/reformer-clm,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0573748387396335,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
18302,hfl/rbt3,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
18303,google/t5-efficient-base-dm2000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0268197190016508,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
18304,google/t5-efficient-small-el32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0169738344848156,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
18305,stas/t5-very-small-random,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0977613031864166,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
18306,wukevin/tcr-bert,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0519583807326853,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
18307,google/t5-efficient-large-el4,Its original repository can be found here,tau/splinter-large-qass,0.0348168183118104,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
18308,google/t5-efficient-base-el16,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0679548941552638,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
18309,google/t5-efficient-base-nl36,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0155157982371747,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
18310,minimaxir/magic-the-gathering,For best results use python as below,pszemraj/led-large-book-summary,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
18311,google/t5-efficient-small-nl2,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0560570266097783,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
18312,google/t5-efficient-base-nh24,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0716636702418327,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
18313,gayanin/bart-mlm-pubmed-medterm,You can use BelGPT-2 with 🤗 transformers:Below is the list of all French copora used to pre-trained the model:,antoiloui/belgpt2,0.068275898694992,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
18314,google/t5-efficient-base-ff6000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0524464882910251,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
18315,suzuki/distilbert-base-uncased-finetuned-squad,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.089904174208641,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
18316,microsoft/layoutlm-large-uncased,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0015663634985685,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
18317,stas/pegasus-cnn_dailymail-tiny-random,"However, this model outperforms the base model by large margins",tau/splinter-large-qass,0.0360431522130966,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
18318,mimicheng/codeparrot-ds,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0424406677484512,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
18319,google/t5-efficient-xl-nl8,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.004899501800537,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
18320,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0380071960389614,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
18321,tau/tavbert-he,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-mega,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18322,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,You can find more information in our paper AraGPT2The code in this repository was used to train all GPT2 variants,aubmindlab/aragpt2-mega,0.0697474330663681,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
18323,google/bert_uncased_L-8_H-768_A-12,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,1.0,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
18324,google/t5-efficient-base-nh16,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.052012151107192,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
18325,google/t5-efficient-large-nl20,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0149182858876883,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
18326,google/t5-efficient-tiny-nh32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0687398761510848,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
18327,TJMUCH/transcriptome-iseeek,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18328,mmaguero/gn-bert-tiny-cased,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.001438733190298,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
18329,everdoubling/byt5-Korean-small,have shown that the standard BERT recipe is effective on a wide range of model sizes,uer/chinese_roberta_L-6_H-128,0.0337246157228946,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
18330,google/t5-efficient-small-nl22,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
18331,google/t5-efficient-large-nh8,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0854407511651515,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
18332,cammy/led-base-16384-100-MDS,"FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows",nherve/flaubert-oral-ft,0.0945266336202621,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
18333,tucan9389/kcbert-base-finetuned-squad,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0198819898068904,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
18334,google/t5-efficient-large-nl36,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0554509814828634,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
18335,razent/SciFive-base-PMC,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0464964639395475,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
18336,flboehm/reddit-bert-text_5,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0918389856815338,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
18337,google/t5-efficient-small-el12,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0172298168763518,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
18338,rmihaylov/bert-base-pos-theseus-bg,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0125992586836218,-1.0,NLP,428758d5-6177-42ba-a879-c30586fca3ab___bert-base-pos-theseus-bg,HF
18339,google/t5-efficient-base-nl24,T5-Efficient-BASE-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl24,1.0,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
18340,google/t5-efficient-small-kv16,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0241617048159241,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
18341,rdenadai/BR_BERTo,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.0293043212344248,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
18342,google/t5-efficient-small-kv256,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
18343,mikaelsouza/msft-regular-model,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0819866731762886,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
18344,kyryl0s/gpt2-uk-xxs,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
18345,malay-huggingface/t5-super-super-tiny-bahasa-cased,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0067806144555409,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
18346,tau/tavbert-he,T5-Efficient-BASE-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl24,0.0062322672456502,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18347,andrejmiscic/simcls-scorer-billsum,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0119018198244951,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
18348,sentence-transformers/paraphrase-MiniLM-L3-v2,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0456518307328224,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
18349,tau/tavbert-he,T5-Efficient-SMALL-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el8,0.0511313527822494,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18350,nyu-mll/roberta-base-10M-3,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
18351,google/t5-efficient-tiny-nl2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0390414521098136,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
18352,tau/tavbert-he,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18353,diper1998/distilgpt2-finetuned-AT,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0794755667448043,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
18354,saurkulsh/T0pp,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0051065952117953,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
18355,razent/SciFive-large-Pubmed,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0871121808886527,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
18356,GKLMIP/electra-myanmar-small-uncased,"Then, it was compressed via progressive module replacing",rmihaylov/bert-base-pos-theseus-bg,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
18357,mgreenbe/bertlet-base-uncased-for-sequence-classification,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.016425222158432,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
18358,liaad/srl-pt_xlmr-base,This model is the xlm-roberta-base fine-tuned on Portuguese semantic role labeling data,liaad/srl-pt_xlmr-base,1.0,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
18359,abdusahmbzuai/aradia-ctc-distilhubert-ft,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0515306815505027,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
18360,dbmdz/electra-base-turkish-cased-discriminator,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.022549033164978,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
18361,hfl/rbtl3,"However, this model outperforms the base model by large margins",tau/splinter-large-qass,0.0435438584536313,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18362,hfl/rbt3,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0563135892152786,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
18363,nherve/flaubert-oral-ft,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0454408299798766,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
18364,mikaelsouza/msft-regular-model,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0870598405599594,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
18365,google/t5-efficient-small-dm128,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0503725484013557,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
18366,razent/SciFive-base-PMC,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0330948829650878,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
18367,SupriyaArun/squeezebert-uncased-finetuned-squad,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0561870820820331,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
18368,tartuNLP/gpt-4-est-base,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.055877074599266,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
18369,amazon/bort,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0059503046795725,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18370,google/reformer-crime-and-punishment,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,1.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
18371,hfl/chinese-electra-small-ex-generator,"Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants",hfl/chinese-electra-small-ex-generator,1.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
18372,fnlp/bart-large-chinese,"CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationYunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu",fnlp/bart-large-chinese,1.0,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
18373,deep-learning-analytics/triviaqa-t5-base,The pretrained model used here was trained on Common Crawl (C4) data set,deep-learning-analytics/triviaqa-t5-base,1.0,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
18374,google/t5-efficient-base-nl32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0108222756534814,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
18375,google/t5-efficient-small-ff6000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0319049507379531,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
18376,Norod78/hebrew_poetry-gpt_neo-tiny,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0290438923984766,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
18377,google/t5-efficient-large-dm2000,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0534946396946906,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
18378,amazon/bort,FNet is a transformers model with attention replaced with fourier transforms,google/fnet-large,0.0229310393333434,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18379,google/t5-efficient-base-kv32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
18380,microsoft/deberta-v2-xxlarge-mnli,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0264708579052239,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
18381,DemangeJeremy/4-sentiments-with-flaubert,Model is based on GPT-J and has over 405M trainable parameters,Milos/slovak-gpt-j-405M,0.0146338157355785,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18382,tartuNLP/gpt-4-est-large,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0986167192459106,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
18383,google/t5-efficient-base-nh8,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0562116168439388,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
18384,google/t5-efficient-small-ff6000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0286407396197318,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
18385,GKLMIP/electra-myanmar-small-uncased,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0723314397037029,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
18386,hakurei/gpt-j-random-tinier,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0049171168357134,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
18387,tartuNLP/gpt-4-est-large,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0334144160151481,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
18388,amazon/bort,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0036780489608645,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18389,mimicheng/codeparrot-ds,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
18390,pszemraj/led-large-book-summary,"You can download the set of Chinese PEGASUS models either from the UER-py Modelzoo page, or via HuggingFace from the links below:You can use this model directly with a pipeline for text2text generation (take the case of PEGASUS-Base):",uer/pegasus-base-chinese-cluecorpussmall,0.0499197100289165,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
18391,Palak/microsoft_deberta-base_squad,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0757510811090469,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
18392,google/t5-efficient-base-ff9000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-ff9000,1.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
18393,saibo/random-roberta-tiny,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0295092177111655,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
18394,jean-paul/kinyaRoberta-small,This KinyaRoBERTa model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda,jean-paul/kinyaRoberta-small,1.0,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
18395,IDEA-CCNL/Zhouwenwang-Unified-110M,Zhouwenwang-Unified-110M is the largest model for LM and MLM tasks in the Chinese field,IDEA-CCNL/Zhouwenwang-Unified-110M,1.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
18396,google/reformer-enwik8,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0075949983050425,-1.0,NLP,6aa23d61-51e8-44d6-b6b7-b68cce7cd0c1___reformer-enwik8,HF
18397,stas/t5-very-small-random,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0929377749562263,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
18398,kyryl0s/gpt2-uk-xxs,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0304836938157677,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
18399,clue/roberta_chinese_3L312_clue_tiny,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
18400,google/t5-efficient-large-dm128,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
18401,google/t5-efficient-tiny-nh1,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
18402,minimaxir/magic-the-gathering,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0906594246625899,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
18403,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0704361051321029,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
18404,groar/gpt-neo-1.3B-finetuned-escape2,This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset,groar/gpt-neo-1.3B-finetuned-escape2,1.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
18405,google/t5-efficient-xl-nl8,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0593880414962768,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
18406,diper1998/distilgpt2-finetuned-AT,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0112397596240043,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
18407,negfir/distilbert-base-uncased-finetuned-squad,"In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0857680067420005,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
18408,ductuan024/AimeLaw,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0444255108013748,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
18409,sarnikowski/convbert-medium-small-da-cased,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0338682060440381,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
18410,sentence-transformers/paraphrase-MiniLM-L3-v2,The training code is available here: SBERT,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0696529746055603,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
18411,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
18412,google/t5-efficient-small-ff6000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.064477490261197,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
18413,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
18414,piotr-rybak/poleval2021-task4-herbert-large-encoder,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0788211077451706,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
18415,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
18416,google/t5-efficient-small-nl2,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0139687731862067,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
18417,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0490663535892963,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
18418,google/t5-efficient-large-kv16,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0405978765338659,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
18419,TJMUCH/transcriptome-iseeek,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.036436527967453,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18420,DeepPavlov/distilrubert-tiny-cased-conversational-v1,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
18421,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0702520310878753,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
18422,FremyCompany/xls-r-nl-v1-cv8-lm,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0513709783554077,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
18423,google/t5-efficient-large-el2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0201288238167762,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
18424,celine98/canine-s-finetuned-sst2,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0682175308465957,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
18425,DeepPavlov/distilrubert-tiny-cased-conversational-v1,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,1.0,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
18426,dbmdz/electra-base-turkish-cased-discriminator,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0368727259337902,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
18427,tucan9389/kcbert-base-finetuned-squad,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.080979898571968,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
18428,mimi/Waynehills-NLP-doogie,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
18429,google/t5-efficient-base-nl2,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0375626944005488,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
18430,cosmoquester/bart-ko-base,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0067170386513074,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
18431,Rostlab/prot_bert,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,1.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
18432,sentence-transformers/paraphrase-MiniLM-L3-v2,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0307087264955043,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
18433,microsoft/layoutlm-base-uncased,FNet is a transformers model with attention replaced with fourier transforms,google/fnet-large,0.085108183324337,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
18434,nielsr/coref-roberta-large,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0326590132899582,-1.0,NLP,a42c9bdb-be23-4bfd-8add-30afe8d1724a___coref-roberta-large,HF
18435,mimi/Waynehills-NLP-doogie,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
18436,allenai/macaw-answer-11b,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0281438858558733,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
18437,clue/roberta_chinese_3L312_clue_tiny,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0257831178605556,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
18438,google/t5-efficient-large-el2,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0580602660775184,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
18439,beston91/gpt2-xl_ft_logits_10k,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0895867794752121,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
18440,google/t5-efficient-tiny-el8,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0636816099286079,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
18441,google/t5-efficient-small-nl48,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
18442,google/t5-efficient-small-nl36,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0680324658751487,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
18443,skt/ko-gpt-trinity-1.2B-v0.5,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.038399489596486,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
18444,google/t5-efficient-small-nl40,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0881220549345015,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
18445,razent/SciFive-base-PMC,"Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint",KBLab/megatron-bert-large-swedish-cased-165k,0.0708475951105355,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
18446,google/t5-efficient-base-nl40,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0526799671351909,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
18447,hakurei/gpt-j-random-tinier,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0234561571851372,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
18448,google/t5-efficient-small-nl48,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.080557994544506,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
18449,mimi/Waynehills-NLP-doogie,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0423762500286102,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
18450,mrm8488/xlm-multi-finetuned-xquadv1,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
18451,google/t5-efficient-large-dm2000,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0500826630741357,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
18452,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Model is warm started from distilbert-fa’s checkpoint,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0699874833226203,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
18453,google/t5-efficient-mini,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0673856362700462,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
18454,tartuNLP/gpt-4-est-large,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0873011797666549,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
18455,anirudh21/xlnet-base-cased-finetuned-rte,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0605326853692531,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
18456,soheeyang/rdr-ctx_encoder-single-trivia-base,The model is a DPR retriever further finetuned using knowledge distillation from the DPR reader,soheeyang/rdr-ctx_encoder-single-trivia-base,1.0,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
18457,stas/pegasus-cnn_dailymail-tiny-random,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0433414163999259,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
18458,google/t5-efficient-large-dm256,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0483177797868847,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
18459,IDEA-CCNL/Erlangshen-Longformer-330M,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0047690123319625,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
18460,DemangeJeremy/4-sentiments-with-flaubert,"When using the model, make sure that your speech input is also sampled at 16kHz",microsoft/unispeech-sat-base,0.0651393327862024,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18461,google/t5-efficient-tiny-nh1,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
18462,junnyu/roformer_base_wwm_cluecorpussmall,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0214494913816451,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
18463,google/t5-efficient-small-dm128,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0496235154569148,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
18464,google/t5-efficient-small-nl24,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-nl24,1.0,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
18465,google/t5-efficient-small-el8,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0588082671165466,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
18466,google/t5-efficient-base-ff12000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0633162232115864,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
18467,eli4s/Bert-L12-h240-A12,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
18468,google/t5-efficient-xl-nl8,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
18469,google/t5-efficient-base-nl24,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0627091452479362,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
18470,sentence-transformers/paraphrase-MiniLM-L3-v2,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0648397430777549,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
18471,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0701122432947158,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
18472,mimi/Waynehills-NLP-doogie,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0045220255851745,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
18473,obss/mt5-base-3task-highlight-tquad2,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0221012905240059,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
18474,google/t5-efficient-large-nl32,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0597962187603115,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
18475,lgrobol/electra-minuscule-generator,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0692493524402379,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
18476,anirudh21/xlnet-base-cased-finetuned-rte,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0108997318893671,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
18477,piotr-rybak/poleval2021-task4-herbert-large-encoder,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0803800523281097,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
18478,google/t5-efficient-base-ff9000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0637167580425739,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
18479,google/t5-efficient-small-el12,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0340865850448607,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
18480,stas/pegasus-cnn_dailymail-tiny-random,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0071919783949852,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
18481,IDEA-CCNL/Erlangshen-Longformer-330M,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0357195073738694,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
18482,skt/ko-gpt-trinity-1.2B-v0.5,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0518000535666942,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
18483,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0948079228401184,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
18484,tuner007/pegasus_qa,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0939435735344886,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
18485,krevas/finance-koelectra-base-discriminator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
18486,google/t5-efficient-base-el8,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0128136444836854,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
18487,DemangeJeremy/4-sentiments-with-flaubert,"We started training from GPT-J, which has been trained on The Pile",Cedille/fr-boris,0.0046630185097455,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18488,amazon/bort,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0616182833909988,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18489,sonoisa/byt5-small-japanese,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0663053393363952,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
18490,DemangeJeremy/4-sentiments-with-flaubert,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18491,google/bert_uncased_L-10_H-256_A-4,"We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large",google/bert_uncased_L-10_H-256_A-4,1.0,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
18492,qarib/bert-base-qarib_far_6500k,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0408104956150055,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
18493,mikaelsouza/msft-regular-model,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
18494,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0212889648973941,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
18495,minimaxir/magic-the-gathering,"Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa)",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0183881158009171,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
18496,SupriyaArun/squeezebert-uncased-finetuned-squad,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0226489324122667,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
18497,google/t5-efficient-large-nl32,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0725085139274596,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
18498,crabz/distil-slovakbert-ner,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0764051228761673,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
18499,google/t5-efficient-base-nl40,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-nl40,1.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
18500,google/t5-efficient-base-ff9000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0580426286906003,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
18501,amazon/bort,The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package,jcblaise/distilbert-tagalog-base-cased,0.076850090175867,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18502,piotr-rybak/poleval2021-task4-herbert-large-encoder,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.012431452050805,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
18503,google/t5-efficient-tiny-nh8,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0397161021828651,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
18504,google/t5-efficient-large-dm768,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
18505,ian0delond/model-test,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0484495945274829,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
18506,mgreenbe/bertlet-base-uncased-for-sequence-classification,Its original repository can be found here,tau/splinter-large-qass,0.0823048651218414,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
18507,DemangeJeremy/4-sentiments-with-flaubert,We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy,hyunwoongko/reddit-9B,0.0552457459270954,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18508,gchhablani/fnet-base-finetuned-wnli,It achieves the following results on the evaluation set:The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased,gchhablani/fnet-base-finetuned-wnli,1.0,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
18509,mimicheng/codeparrot-ds,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0192768927663564,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
18510,beston91/gpt2-xl_ft_logits_10k,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0338296480476856,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
18511,suzuki/distilbert-base-uncased-finetuned-squad,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0527873411774635,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
18512,ductuan024/AimeLaw,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0952714383602142,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
18513,google/t5-efficient-small-el64,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0520856790244578,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
18514,google/t5-efficient-mini-nl12,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
18515,castorini/afriberta_small,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
18516,hfl/chinese-electra-180g-small-generator,ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants,hfl/chinese-electra-180g-small-generator,1.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
18517,tucan9389/kcbert-base-finetuned-squad,This model is a fine-tuned version of beomi/kcbert-base on the klue dataset,tucan9389/kcbert-base-finetuned-squad,1.0,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
18518,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Generated from the official Squad2 evaluation scriptMuRIL is specifically trained to work on 18 Indic languages and English,Sindhu/muril-large-squad2,0.0879871323704719,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
18519,amazon/bort,"Then, it was compressed via progressive module replacing",rmihaylov/bert-base-pos-theseus-bg,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18520,mwesner/reformer-clm,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.088344819843769,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
18521,nghuyong/ernie-tiny,This does not make a very readable text,pere/DeUnCaser,0.0771960392594337,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
18522,hf-internal-testing/tiny-random-rembert,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0226840786635875,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
18523,google/t5-efficient-large-nh2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
18524,IDEA-CCNL/Erlangshen-Longformer-110M,It was finetuned on public part-of-speech Bulgarian data,rmihaylov/bert-base-pos-theseus-bg,0.0451387288048863,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
18525,google/t5-efficient-base-dm2000,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0917331650853156,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
18526,groar/gpt-neo-1.3B-finetuned-escape2,This is a sentence-transformers model: It maps sentences and paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search,sentence-transformers/paraphrase-MiniLM-L3-v2,0.0995654016733169,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
18527,piotr-rybak/poleval2021-task4-herbert-large-encoder,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.072238989174366,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
18528,superspray/electra_large_discriminator_squad2_custom_dataset,ERNIE-tiny is a compressed model from ERNIE 2.0 base model through model structure compression and model distillation,nghuyong/ernie-tiny,0.0916885137557983,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
18529,google/t5-efficient-xl-nl4,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0155161470174789,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
18530,google/t5-efficient-tiny-nh8,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0095567218959331,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
18531,mrm8488/longformer-base-4096-spanish,"It supports sequences of length up to 4,096! Longformer uses a combination of a sliding window (local) attention and global attention",mrm8488/longformer-base-4096-spanish,1.0,-1.0,NLP,47fa5077-4d92-4913-a31a-b5f621200d4b___longformer-base-4096-spanish,HF
18532,uer/chinese_roberta_L-6_H-128,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,1.0,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
18533,google/t5-efficient-large-dm256,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0551058892160654,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
18534,zoha/wav2vec2-base-timit-demo-colab,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0447952896356582,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
18535,junnyu/roformer_base_wwm_cluecorpussmall,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
18536,amazon/bort,You should fine-tune this model for your downstream task,google/rembert,0.0902972146868705,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18537,mimicheng/codeparrot-ds,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0586258172988891,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
18538,google/t5-efficient-tiny-ff12000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0686251483857631,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
18539,thu-coai/LongLM-large,T5-Efficient-SMALL-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff9000,0.0549707006042202,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
18540,google/t5-efficient-small-kv32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0521979480981825,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
18541,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0613144785165786,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
18542,google/t5-efficient-tiny-nl16,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0692435037344693,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
18543,google/t5-efficient-large-nh8-nl32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0541001483798026,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
18544,Davlan/mbart50-large-eng-yor-mt,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,1.0,-1.0,NLP,12474c75-dc49-473e-bcb6-eea8354b1253___mbart50-large-eng-yor-mt,HF
18545,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
18546,zoha/wav2vec2-base-timit-demo-colab,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0197901874780654,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
18547,saibo/random-roberta-mini,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0148193121422081,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
18548,google/t5-efficient-tiny-nh1,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.029307902790606,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
18549,DemangeJeremy/4-sentiments-with-flaubert,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0901491492986678,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18550,google/t5-efficient-small-nl2,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0300100501626729,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
18551,DemangeJeremy/4-sentiments-with-flaubert,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18552,tau/tavbert-he,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18553,rahulacj/bertweet-base-finetuned-sentiment-analysis,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0845125466585159,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
18554,khizon/distilbert-unreliable-news-eng-4L,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
18555,google/t5-efficient-xl-nl6,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.036154631525278,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
18556,google/t5-efficient-tiny-ff6000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0393379824236034,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
18557,microsoft/layoutlm-large-uncased,FNet is a transformers model with attention replaced with fourier transforms,google/fnet-large,0.085108183324337,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
18558,google/t5-efficient-xl-nl4,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0448646359145641,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
18559,rinna/japanese-gpt2-xsmall,The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co,rinna/japanese-gpt2-xsmall,1.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
18560,google/t5-efficient-base-nl4,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0161956213414669,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
18561,VietAI/gpt-neo-1.3B-vietnamese-news,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
18562,Recognai/zeroshot_selectra_small,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,1.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
18563,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0847866535186767,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
18564,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0366348177194595,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
18565,google/t5-efficient-xl-nl28,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
18566,zoha/wav2vec2-base-timit-demo-colab,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.053811140358448,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
18567,google/t5-efficient-large-nh12,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0680814906954765,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
18568,flboehm/reddit-bert-text_5,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0640859752893447,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
18569,google/t5-efficient-small-nl20,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0314748343080281,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
18570,ncduy/phobert-large-finetuned-vietnamese_students_feedback,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0899008363485336,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
18571,google/t5-efficient-small-kv16,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0497941467911004,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
18572,Sindhu/muril-large-squad2,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0597841981798409,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
18573,google/t5-efficient-small-kv256,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0491807805374264,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
18574,microsoft/layoutlm-base-uncased,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
18575,google/t5-efficient-large-el4,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0677261929959058,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
18576,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.088538609445095,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
18577,dbmdz/convbert-base-german-europeana-cased,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0792632550001144,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
18578,allenai/wmt19-de-en-6-6-big,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0641358755528926,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
18579,google/byt5-xl,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0125498543493449,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
18580,google/t5-efficient-small-kv16,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0477689020335674,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
18581,google/t5-efficient-small-kv16,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0108048813417553,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
18582,cammy/led-base-16384-100-MDS,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0698256492614746,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
18583,thu-coai/LongLM-large,T5-Efficient-BASE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv128,0.0510247567047675,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
18584,castorini/afriberta_base,"For example, assuming we want to finetune this model on a token classification task, we do the following:The model was trained on an aggregation of datasets from the BBC news website and Common Crawl",castorini/afriberta_base,1.0,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
18585,razent/SciFive-large-Pubmed,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0887697264552116,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
18586,google/t5-efficient-large-dm512,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0477551445364951,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
18587,SetFit/deberta-v3-large__sst2__train-8-8,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
18588,google/t5-efficient-base-nl48,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0568625181913375,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
18589,hfl/rbt3,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0617767833173274,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
18590,DemangeJeremy/4-sentiments-with-flaubert,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18591,google/t5-efficient-small-dm256,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0101733319461345,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
18592,TJMUCH/transcriptome-iseeek,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18593,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh32,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18594,google/t5-efficient-large-dm256,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0465346388518809,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
18595,google/t5-efficient-small-el12,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0710427872836589,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
18596,PlanTL-GOB-ES/gpt2-base-bne,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0224352005041307,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
18597,google/t5-efficient-base-kv32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0628453809767961,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
18598,hfl/chinese-electra-small-ex-discriminator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
18599,lassl/roberta-ko-small,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0493020497262477,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
18600,mwesner/reformer-clm,This casual language model was trained from scratch on CNN/Dailymail dataset,mwesner/reformer-clm,1.0,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
18601,hfl/chinese-xlnet-mid,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0397058315575122,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
18602,jnz/electra-ka,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0611425712704658,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
18603,cammy/led-base-16384-100-MDS,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0345757156610488,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
18604,google/t5-efficient-base-kv128,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0493268705904482,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
18605,google/t5-efficient-tiny-el8,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0277958679944276,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
18606,google/t5-efficient-mini-nl24,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0148676466196775,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
18607,jonfd/convbert-small-igc-is,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0762272581458091,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
18608,Iskaj/hf-challenge-test,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0906866118311882,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
18609,google/t5-efficient-base-nh8,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0026149568147957,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
18610,beston91/gpt2-xl_ft_logits_10k,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.083582729101181,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
18611,google/t5-efficient-xl-nl4,Its original repository can be found here,tau/splinter-large-qass,0.0602165721356868,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
18612,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0195176303386688,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
18613,mwesner/reformer-clm,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0989209190011024,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
18614,lgrobol/electra-minuscule-discriminator,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0421226667240261,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
18615,google/t5-efficient-large-nl12,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0585033595561981,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
18616,superspray/electra_large_discriminator_squad2_custom_dataset,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0630918890237808,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
18617,tau/tavbert-he,T5-Efficient-SMALL-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl36,0.0352801997214555,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18618,sentence-transformers/paraphrase-MiniLM-L3-v2,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
18619,google/bert_uncased_L-6_H-256_A-4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.012903448815147,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
18620,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0657213671753803,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
18621,IDEA-CCNL/Erlangshen-Longformer-330M,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.0275918720290064,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
18622,ml6team/mt5-small-german-finetune-mlsum,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0203178878873586,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
18623,hfl/chinese-electra-180g-small-generator,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0612745142231384,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
18624,stas/pegasus-cnn_dailymail-tiny-random,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
18625,skt/ko-gpt-trinity-1.2B-v0.5,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0666892491281032,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
18626,saibo/random-roberta-mini,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0191588439047336,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
18627,IlyaGusev/rugpt3medium_sum_gazeta,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0623413491994142,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
18628,uer/t5-v1_1-small-chinese-cluecorpussmall,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
18629,microsoft/layoutlm-base-uncased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
18630,google/t5-efficient-small-el8,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0134184919297695,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
18631,jnz/electra-ka,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0689075365662574,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
18632,TJMUCH/transcriptome-iseeek,!!!This is a distilled HRBert model for an mlm task,RabotaRu/HRBert-mini,0.0989402830600738,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18633,aditeyabaral/sentencetransformer-bert-hinglish-small,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0446088053286075,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
18634,mrm8488/bert-small-finetuned-squadv2,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,1.0,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
18635,google/t5-efficient-large-nh2,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
18636,google/t5-efficient-base-nl24,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0428587812930345,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
18637,google/t5-efficient-base-el16,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0071996171027422,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
18638,stas/pegasus-cnn_dailymail-tiny-random,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
18639,google/t5-efficient-xl-nl16,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.09639822691679,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
18640,brad1141/Longformer-finetuned-norm,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0887603908777237,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
18641,stas/mt5-tiny-random,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
18642,DemangeJeremy/4-sentiments-with-flaubert,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0567836835980415,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18643,google/t5-efficient-base-nl32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0324512533843517,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
18644,google/t5-efficient-xl-nl8,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0083820074796677,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
18645,groar/gpt-neo-1.3B-finetuned-escape2,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0961304754018783,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
18646,google/t5-efficient-small-el4,Its original repository can be found here,tau/splinter-large-qass,0.0363883357495069,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
18647,saibo/random-roberta-tiny,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0113296024501323,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
18648,mimicheng/codeparrot-ds,Details will be available soon,VietAI/gpt-neo-1.3B-vietnamese-news,0.0873358398675918,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
18649,KoichiYasuoka/roberta-base-ukrainian,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0488448850810527,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
18650,abdusahmbzuai/aradia-ctc-distilhubert-ft,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0911109298467636,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
18651,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The main task model is based on facebook/bart-large-xsum and meta-task model (sentiment) is  VictorSanh/roberta-base-finetuned-yelp-polarity,ebagdasa/propaganda_positive_bart_large,0.0857413411140441,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
18652,amazon/bort,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18653,xlnet-large-cased,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
18654,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.064201720058918,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
18655,google/t5-efficient-base-dm1000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0337157510221004,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
18656,ian0delond/model-test,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0600482076406478,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
18657,SetFit/deberta-v3-large__sst2__train-8-8,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0499964319169521,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
18658,hfl/rbtl3,T5-Efficient-SMALL-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff12000,0.0616102395579218,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18659,negfir/distilbert-base-uncased-finetuned-cola,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
18660,dhpollack/distilbert-dummy-sentiment,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
18661,clue/roberta_chinese_3L312_clue_tiny,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0276222908869385,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
18662,microsoft/layoutlm-large-uncased,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
18663,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0965631157159805,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
18664,mimi/Waynehills-NLP-doogie,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0823177546262741,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
18665,eli4s/Bert-L12-h240-A12,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0047185886651277,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
18666,google/t5-efficient-base-dm512,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0621555596590041,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
18667,deep-learning-analytics/triviaqa-t5-base,Closed Book Trivia-QA T5 baseThis is a T5-base model trained on No Context Trivia QA data set,deep-learning-analytics/triviaqa-t5-base,1.0,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
18668,google/t5-efficient-base-nl48,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
18669,google/t5-efficient-large-el2,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0313822636380791,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
18670,sonoisa/byt5-small-japanese,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
18671,sonoisa/byt5-small-japanese,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0657583326101303,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
18672,google/t5-efficient-xl-nl6,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.078305572271347,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
18673,google/t5-efficient-small-el16-dl4,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0746111683547496,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
18674,NovelAI/genji-python-6B,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0087406296903888,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
18675,rinna/japanese-gpt-1b,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
18676,piotr-rybak/poleval2021-task4-herbert-large-encoder,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
18677,jcblaise/electra-tagalog-small-cased-generator,All model details and training setups can be found in our papers,jcblaise/electra-tagalog-small-cased-generator,1.0,-1.0,NLP,f4e25246-ebec-4c97-8a1d-5ef296f2f888___electra-tagalog-small-cased-generator,HF
18678,IlyaGusev/rugpt3medium_sum_gazeta,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0400420171208679,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
18679,mrm8488/xlm-multi-finetuned-xquadv1,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0755956321954726,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
18680,google/t5-efficient-large-kv128,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0268668346107006,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
18681,google/t5-efficient-xl-nl12,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0747454389929771,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
18682,TJMUCH/transcriptome-iseeek,The article and  summarization text is reduced via extractive summarization before it is finetuned to the model,ml6team/distilbart-tos-summarizer-tosdr,0.0896008014678955,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18683,google/t5-efficient-xl-nl6,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0773944780230522,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
18684,lgrobol/electra-minuscule-generator,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
18685,sberbank-ai/rugpt3medium_based_on_gpt2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
18686,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.048286322504282,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
18687,google/t5-efficient-base-el6,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0696146097034215,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
18688,google/t5-efficient-small-el16-dl4,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0458214576356112,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
18689,tartuNLP/gpt-4-est-large,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0737099722027778,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
18690,minimaxir/magic-the-gathering,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0215570107102393,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
18691,ian0delond/model-test,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
18692,prajjwal1/bert-mini,"If you use the model, please consider citing both the papers:",prajjwal1/bert-mini,1.0,-1.0,NLP,d524d2bf-0c8c-4e35-817b-27b8261b2166___bert-mini,HF
18693,VietAI/gpt-neo-1.3B-vietnamese-news,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0630643814802169,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
18694,google/t5-efficient-large-dm128,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0778807662427425,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
18695,gayanin/bart-mlm-pubmed-medterm,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0495150201022625,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
18696,sentence-transformers/paraphrase-MiniLM-L3-v2,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0765222609043121,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
18697,amazon/bort,"We conduct extensive experiments on a variety of Chinese and Japanese NLP benchmarks, showing that our proposed UnihanLM is effective on both mono- and cross-lingual Chinese and Japanese tasks, shedding light on a new path to exploit the homology of languages",microsoft/unihanlm-base,0.0577650386840104,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18698,google/t5-efficient-base-ff1000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
18699,eli4s/Bert-L12-h384-A6,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0617529290417829,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
18700,google/t5-efficient-small-nl24,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0286779943853616,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
18701,google/t5-efficient-xl-nl6,The checkpoint used here is the original MobileBert Optimized Uncased English: (uncased_L-24_H-128_B-512_A-4_F-4_OPT) checkpoint,mrm8488/mobilebert-uncased-finetuned-squadv2,0.0893244817852974,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
18702,dhpollack/distilbert-dummy-sentiment,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.040999537333846,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
18703,google/t5-efficient-base-ff1000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0567621421068905,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
18704,Palak/microsoft_deberta-base_squad,This model is a fine-tuned version of microsoft/deberta-base on the squadV1 dataset,Palak/microsoft_deberta-base_squad,1.0,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
18705,suzuki/distilbert-base-uncased-finetuned-squad,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0809797644615173,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
18706,google/t5-efficient-base-ff12000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0685997977852821,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
18707,google/t5-efficient-tiny-nh16,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
18708,google/t5-efficient-large-dm512,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0545304147526621,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
18709,google/t5-efficient-tiny-nl12,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0705876685678958,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
18710,gayanin/bart-mlm-pubmed-medterm,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0452855452895164,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
18711,microsoft/layoutlm-large-uncased,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0771030038595199,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
18712,google/t5-efficient-xl-nl2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0239645708352326,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
18713,google/t5-efficient-large-dm768,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0732145048677921,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
18714,TJMUCH/transcriptome-iseeek,"If you use our model or find it useful in your projects, please cite our work:",jcblaise/electra-tagalog-small-cased-generator,0.0384471304714679,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18715,google/t5-efficient-large-nh4,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0577517803758381,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
18716,google/t5-efficient-tiny-ff6000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-ff6000,1.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
18717,Sindhu/muril-large-squad2,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
18718,google/t5-efficient-small-el16-dl4,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0700755901634692,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
18719,google/t5-efficient-base-ff1000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0078110503964126,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
18720,google/t5-efficient-base-nl24,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0735053755342959,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
18721,tuner007/pegasus_qa,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0373508632183074,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
18722,hfl/chinese-electra-180g-small-ex-discriminator,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0469436260561147,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
18723,google/t5-efficient-tiny-nl2,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
18724,hfl/rbtl3,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,0.036989471875131,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18725,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset,groar/gpt-neo-1.3B-finetuned-escape2,0.0995654016733169,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
18726,amazon/bort,"This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences",Rostlab/prot_bert,0.0010164082050323,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18727,stas/mt5-tiny-random,We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric,andrejmiscic/simcls-scorer-billsum,0.0999611020088195,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
18728,microsoft/layoutlm-base-cased,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,0.034470222890377,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
18729,google/t5-efficient-large-nl10,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0861913003027439,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
18730,google/t5-efficient-base-dm512,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0202906727790832,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
18731,IlyaGusev/rugpt3medium_sum_gazeta,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
18732,google/t5-efficient-small-nl36,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
18733,tartuNLP/gpt-4-est-large,"Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words",flax-community/RoBERTa-large-finnish,0.0940550044178962,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
18734,google/t5-efficient-base-el16,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0836952365934848,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
18735,tau/tavbert-he,T5-Efficient-TINY-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl6,0.0536643974483012,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18736,google/t5-efficient-large-nl16,This does not make a very readable text,pere/DeUnCaser,0.056320646777749,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
18737,google/t5-efficient-mini-nl12,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0615101680159567,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
18738,google/t5-efficient-base-ff1000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0552503913640975,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
18739,google/t5-efficient-large-nh2,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0633406043052673,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
18740,microsoft/deberta-v2-xxlarge-mnli,"This the DeBERTa V2 XXLarge model fine-tuned with MNLI task, 48 layers, 1536 hidden size",microsoft/deberta-v2-xxlarge-mnli,1.0,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
18741,allenai/wmt16-en-de-12-1,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0333391493186355,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
18742,akdeniz27/deberta-v2-xlarge-cuad,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0642733126878738,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
18743,google/t5-efficient-xl-nl16,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0821280181407928,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
18744,beston91/gpt2-xl_ft_logits_10k,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.008511982858181,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
18745,Recognai/selectra_medium,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0294074402190744,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
18746,google/t5-efficient-tiny-ff6000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0338858943432569,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
18747,mrm8488/mobilebert-uncased-finetuned-squadv2,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
18748,google/t5-efficient-tiny-el8,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0475986758247017,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
18749,gilparmentier/pokemon_gptj_model,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0157003747299313,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
18750,google/t5-efficient-base-nl40,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0397494086064397,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
18751,junnyu/roformer_base_wwm_cluecorpussmall,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0326648540794849,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
18752,DTAI-KULeuven/robbertje-1-gb-bort,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0313331857323646,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
18753,amazon/bort,"Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words",flax-community/RoBERTa-large-finnish,0.0304895434528589,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18754,google/t5-efficient-large-kv128,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,1.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
18755,google/t5-efficient-small-kv32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0442588180303573,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
18756,hfl/rbtl3,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18757,google/byt5-xl,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0357712963595986,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
18758,google/t5-efficient-small-ff1000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0373998349532484,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
18759,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-LARGE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el6,0.0769624412059783,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
18760,google/bigbird-roberta-large,Colab: linkEvaluation script: evaluate,IlyaGusev/rugpt3medium_sum_gazeta,0.0508065395057201,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
18761,nherve/flaubert-oral-ft,"FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows",nherve/flaubert-oral-ft,1.0,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
18762,google/t5-efficient-base-kv128,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0686917230486869,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
18763,google/t5-efficient-large-nl16,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0257536005228758,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
18764,Vilnius-Lithuania-iGEM/Albumin,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,1.0,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
18765,google/t5-efficient-small-el12,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0531969415023922,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
18766,sonoisa/byt5-small-japanese,"We started training from GPT-J, which has been trained on The Pile",Cedille/fr-boris,0.077931135892868,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
18767,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,0.0988751500844955,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
18768,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-EL16-DL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el16-dl4,0.0765529796481132,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
18769,google/t5-efficient-large-nh8-nl32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0420052297413348,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
18770,google/t5-efficient-large-nh24,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0167913436889647,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
18771,mwesner/reformer-clm,T5-Efficient-SMALL-EL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el12,0.0986745283007621,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
18772,google/t5-efficient-small-nl24,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0668250676244497,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
18773,google/t5-efficient-large-nh8,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0482314098626375,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
18774,google/t5-efficient-base-nl48,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0693798810243606,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
18775,google/t5-efficient-large-el6,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0183678567409515,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
18776,google/fnet-large,This model is cased: it makes a difference between english and English,google/fnet-large,1.0,-1.0,NLP,be8c3b54-acb9-41d3-9190-d3a35ba6f7a6___fnet-large,HF
18777,anas-awadalla/bert-tiny-finetuned-squad,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
18778,google/t5-efficient-small-nl2,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0245738122612238,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
18779,allenai/macaw-answer-11b,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
18780,GKLMIP/electra-myanmar-small-uncased,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0402379035949706,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
18781,Palak/microsoft_deberta-base_squad,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0690316557884216,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
18782,DemangeJeremy/4-sentiments-with-flaubert,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18783,google/t5-efficient-small-el16-dl4,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0589406974613666,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
18784,fnlp/bart-base-chinese,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,1.0,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
18785,pulp/CHILDES-ParentBERTo,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0473969373852014,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
18786,google/t5-efficient-large-nl32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0429418254643678,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
18787,hfl/chinese-electra-small-ex-discriminator,This does not make a very readable text,pere/DeUnCaser,0.0206083464436232,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
18788,DemangeJeremy/4-sentiments-with-flaubert,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.046802269294858,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18789,google/t5-efficient-small-nl22,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0731400251388549,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
18790,google/t5-efficient-small-el4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.033587159588933,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
18791,TJMUCH/transcriptome-iseeek,Pretrained model on English language using a similar objective objective as ELECTRA,funnel-transformer/small,0.0470735505223274,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18792,google/t5-efficient-large-kv128,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0283122099936008,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
18793,akdeniz27/deberta-v2-xlarge-cuad,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0591116547584533,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
18794,diper1998/distilgpt2-finetuned-AT,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0461696982383728,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
18795,google/t5-efficient-mini,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-mini,1.0,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
18796,google/t5-efficient-large-el6,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0626266747713088,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
18797,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
18798,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0555310770869255,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
18799,superb/hubert-large-superb-sid,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0567417610436678,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
18800,hajime9652/xlnet-japanese,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0127739440649747,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
18801,cross-encoder/stsb-TinyBERT-L-4,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0188988223671912,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
18802,google/t5-efficient-large-el8,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0629835259169339,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
18803,mgreenbe/bertlet-base-uncased-for-sequence-classification,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.096163310110569,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
18804,google/t5-efficient-xl-nl6,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0722770988941192,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
18805,google/t5-efficient-large-nh8-nl32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0396416671574115,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
18806,yorko/scibert_scivocab_uncased_long_4096,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0109778894111513,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
18807,google/t5-efficient-tiny-ff6000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0536693567410111,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
18808,google/t5-efficient-large-el8,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0212951432913541,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
18809,razent/SciFive-base-PMC,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.002914011478424,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
18810,zoha/wav2vec2-base-timit-demo-colab,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0878495424985885,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
18811,akdeniz27/deberta-v2-xlarge-cuad,"Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint",KBLab/megatron-bert-large-swedish-cased-165k,0.0920603051781654,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
18812,google/t5-efficient-xl-nl4,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0710850656032562,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
18813,bigjoedata/rockbot,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
18814,kyryl0s/gpt2-uk-xxs,"FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows",nherve/flaubert-oral-ft,0.0574688185006379,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
18815,google/t5-efficient-large-kv16,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0653354544192552,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
18816,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0855069607496261,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
18817,mwesner/reformer-clm,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,0.0768061876296997,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
18818,wukevin/tcr-bert,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.049545345827937,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
18819,ian0delond/model-test,It was introduced in this paper,readerbench/RoBERT-small,0.0559723637998104,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
18820,amazon/bort,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18821,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.0481187726060549,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
18822,google/t5-efficient-tiny-ff9000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
18823,google/t5-efficient-large-nl16,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0208794511854648,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
18824,google/t5-efficient-small-ff3000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0880788825452327,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
18825,google/t5-efficient-xl-nl2,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0366357685998082,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
18826,google/t5-efficient-small-nl48,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0678087063133716,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
18827,stas/t5-very-small-random,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0686810612678527,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
18828,IDEA-CCNL/Erlangshen-Longformer-110M,The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0661544911563396,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
18829,google/t5-efficient-small-kv16,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
18830,mimi/Waynehills-NLP-doogie,This model is a fine-tuned version of KETI-AIR/ke-t5-base-ko on the None dataset,mimi/Waynehills-NLP-doogie,1.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
18831,superspray/electra_large_discriminator_squad2_custom_dataset,"Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0781058967113494,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
18832,celine98/canine-s-finetuned-sst2,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0276754591614007,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
18833,Palak/microsoft_deberta-base_squad,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0760881304740905,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
18834,google/t5-efficient-tiny-nh8,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0534124542027711,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
18835,google/t5-efficient-xl-nl16,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0934730395674705,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
18836,allenai/macaw-3b,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0748808433612187,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
18837,ErykWdowiak/GPTalian,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0448063150048255,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
18838,microsoft/layoutlm-large-uncased,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.081086441874504,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
18839,google/t5-efficient-large-nl36,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0484619494527578,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
18840,google/t5-efficient-mini-nl6,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0552442157641053,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
18841,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.02932711225003,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
18842,mrm8488/xlm-multi-finetuned-xquadv1,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0302363131195307,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
18843,ncduy/phobert-large-finetuned-vietnamese_students_feedback,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0342770516872406,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
18844,google/bert_uncased_L-12_H-256_A-4,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-12_H-256_A-4,1.0,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
18845,brad1141/Longformer-finetuned-norm,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
18846,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,This is an implementation of CPT-Base,fnlp/cpt-base,0.0828963965177536,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
18847,google/t5-efficient-large-el4,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0465285889804362,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
18848,microsoft/layoutlm-base-cased,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
18849,google/t5-efficient-tiny-nl16,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0300451107323169,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
18850,eli4s/Bert-L12-h240-A12,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0318983737379311,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
18851,tartuNLP/gpt-4-est-base,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0082772066816687,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
18852,stas/mt5-tiny-random,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0280663464218378,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
18853,nherve/flaubert-oral-asr,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
18854,google/t5-efficient-large-el4,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0659911856055259,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
18855,antoiloui/belgpt2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0127647994086146,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
18856,DemangeJeremy/4-sentiments-with-flaubert,reformer-enwik8 was pretrained on the first 90M chars of enwik8 whereas the text was chunked into batches of size 65536 chars (=2^16),google/reformer-enwik8,0.0492934230715036,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
18857,KBLab/megatron-bert-base-swedish-cased-600k,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0304570486769079,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
18858,google/t5-efficient-large-nh12,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0605425331741571,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
18859,suzuki/distilbert-base-uncased-finetuned-squad,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0839007198810577,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
18860,google/t5-efficient-base-ff9000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0684051197022199,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
18861,stas/mt5-tiny-random,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0705112591385841,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
18862,google/t5-efficient-large-el8,Its original repository can be found here,tau/splinter-large-qass,0.0354135576635598,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
18863,google/t5-efficient-base-nl48,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0226449966430664,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
18864,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0822820663452148,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
18865,google/t5-efficient-xl-nl2,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0774564892053603,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
18866,allenai/wmt19-de-en-6-6-base,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0641358755528926,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
18867,google/t5-efficient-base-nl40,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
18868,google/t5-efficient-small-nl36,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0585947260260581,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
18869,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0233840513974428,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
18870,hfl/rbtl3,T5-Efficient-LARGE-EL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el2,0.0011901650577784,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18871,google/t5-efficient-small-el12,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0800248496234416,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
18872,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
18873,google/t5-efficient-small-ff9000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0352041190490126,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
18874,lgrobol/electra-minuscule-generator,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.083410158753395,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
18875,jhu-clsp/bibert-ende,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0399961446722348,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
18876,IlyaGusev/rugpt3medium_sum_gazeta,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0220813285559415,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
18877,uer/pegasus-base-chinese-cluecorpussmall,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0471268650144338,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
18878,google/t5-efficient-small-ff6000,Its original repository can be found here,tau/splinter-large-qass,0.0340478848665952,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
18879,anas-awadalla/bert-tiny-finetuned-squad,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0970547348260879,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
18880,TJMUCH/transcriptome-iseeek,The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective,indobenchmark/indobert-base-p1,0.0769568309187889,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
18881,qarib/bert-base-qarib_far_6500k,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0124421746004372,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
18882,amazon/bort,Pretrained XLNET tiny language model for Malay,malay-huggingface/xlnet-tiny-bahasa-cased,0.0456667263060808,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18883,clue/xlnet_chinese_large,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0387162650004029,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
18884,TransQuest/monotransquest-hter-en_lv-it-nmt,"With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in WMT 2020",TransQuest/monotransquest-hter-en_lv-it-nmt,1.0,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
18885,FremyCompany/xls-r-nl-v1-cv8-lm,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.050334021449089,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
18886,dhpollack/distilbert-dummy-sentiment,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0358567801304161,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
18887,tau/tavbert-he,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0384797621518373,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18888,Parsa/BBB_prediction_classification_SMILES,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,1.0,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
18889,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0218203328549861,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
18890,fnlp/bart-large-chinese,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0354414694011211,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
18891,google/t5-efficient-base-kv256,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
18892,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,0.0492971601585546,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
18893,KoichiYasuoka/roberta-base-ukrainian,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0877049416303634,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
18894,mimi/Waynehills-NLP-doogie,"We used  Oscar dataset, which is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus",flax-community/gpt2-medium-persian,0.0573770999908447,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
18895,brad1141/Longformer-finetuned-norm,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
18896,FremyCompany/xls-r-nl-v1-cv8-lm,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0462092521289984,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
18897,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0713851675391197,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
18898,google/t5-efficient-xl-nl6,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0852816179394722,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
18899,indobenchmark/indobert-base-p1,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.043977566063404,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
18900,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Model is warm started from distilbert-fa’s checkpoint,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0612833648920059,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
18901,soheeyang/rdr-ctx_encoder-single-trivia-base,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
18902,google/t5-efficient-xl-nl16,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.047962162643671,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
18903,google/t5-efficient-base-dm1000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0487982649356126,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
18904,malay-huggingface/bert-tiny-bahasa-cased,"Below is list of data we trained on,You can use this model by installing torch or tensorflow and Huggingface library transformers",malay-huggingface/bert-tiny-bahasa-cased,1.0,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
18905,google/t5-efficient-small-nl22,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0236195791512727,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
18906,gayanin/bart-mlm-pubmed-medterm,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0471725948154926,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
18907,cammy/led-large-16384-arxiv-100-MDS,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0776788219809532,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
18908,anirudh21/xlnet-base-cased-finetuned-rte,"Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa)",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0906429886817932,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
18909,google/bert_uncased_L-10_H-128_A-2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0032457672059535,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
18910,google/t5-efficient-small-ff12000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0402029249817132,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
18911,akdeniz27/deberta-v2-xlarge-cuad,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0206413995474576,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
18912,ml6team/mt5-small-german-finetune-mlsum,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0580071625299751,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
18913,google/t5-efficient-small-el64,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0485021993517875,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
18914,allenai/wmt16-en-de-12-1,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0640869680792092,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
18915,brad1141/Longformer-finetuned-norm,It was introduced in this paper,readerbench/RoBERT-small,0.0217216908931732,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
18916,cross-encoder/stsb-TinyBERT-L-4,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
18917,google/t5-efficient-base-kv32,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0907837934792041,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
18918,google/t5-efficient-small-ff1000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0598105899989604,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
18919,brad1141/Longformer-finetuned-norm,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0869196653366088,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
18920,Iskaj/hf-challenge-test,This does not make a very readable text,pere/DeUnCaser,0.0842954367399215,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
18921,google/bigbird-roberta-large,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,1.0,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
18922,rdenadai/BR_BERTo,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
18923,google/t5-efficient-large-el6,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0811839289963245,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
18924,tau/tavbert-he,This model is a fine-tuned version of distilgpt2 on the None dataset,diper1998/distilgpt2-finetuned-AT,0.027550209313631,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
18925,google/t5-efficient-tiny-nl2,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-nl2,1.0,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
18926,google/t5-efficient-base-nh8,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,1.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
18927,jnz/electra-ka,This molecular design concept eliminates the need for virtual compound library enumeration and enables virtual compound design without requiring secondary or external activity prediction,mrm8488/chEMBL_smiles_v1,0.0534403696656227,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
18928,dbmdz/electra-base-turkish-cased-discriminator,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0606605987995862,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
18929,hfl/rbt3,T5-Efficient-BASE-EL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el16,0.0273123085498808,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
18930,suzuki/distilbert-base-uncased-finetuned-squad,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.062561959028244,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
18931,google/t5-efficient-large-el8,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.070379078388214,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
18932,ncduy/phobert-large-finetuned-vietnamese_students_feedback,I have made no attempts to censor,bigjoedata/rockbot,0.0046517476439476,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
18933,flaubert/flaubert_small_cased,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
18934,nguyenvulebinh/envibert,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0107503111163775,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
18935,yorko/scibert_scivocab_uncased_long_4096,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0200920701026916,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
18936,google/t5-efficient-base-kv128,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0606174506247043,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
18937,stas/t5-very-small-random,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0825740844011306,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
18938,microsoft/layoutlm-base-cased,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
18939,google/t5-efficient-mini-nl8,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0021500643342733,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
18940,google/t5-efficient-tiny-ff3000,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0514649525284766,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
18941,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.057190403342247,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
18942,eli4s/Bert-L12-h240-A12,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0323044173419475,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
18943,google/t5-efficient-xl-nl16,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0887951701879501,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
18944,hfl/rbtl3,T5-Efficient-BASE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el6,0.0226092869415879,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18945,google/t5-efficient-small-nl48,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
18946,izumi-lab/bert-small-japanese-fin,"The model architecture is the same as BERT small in the original ELECTRA paper; 12 layers, 256 dimensions of hidden states, and 4 attention heads",izumi-lab/bert-small-japanese-fin,1.0,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
18947,google/t5-efficient-base-nl48,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
18948,google/t5-efficient-xl-nl4,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0755810737609863,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
18949,qarib/bert-base-qarib_far_6500k,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.016977937426418,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
18950,akdeniz27/deberta-v2-xlarge-cuad,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0624629221856594,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
18951,antoiloui/belgpt2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0299070086330175,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
18952,amazon/bort,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0057467697188258,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
18953,negfir/distilbert-base-uncased-finetuned-squad,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
18954,fnlp/bart-base-chinese,"CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationYunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu",fnlp/bart-base-chinese,1.0,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
18955,cammy/led-large-16384-arxiv-100-MDS,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0134549858048558,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
18956,jnz/electra-ka,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0199317224323749,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
18957,mimicheng/codeparrot-ds,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0274474136531353,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
18958,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0144425053149461,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
18959,IDEA-CCNL/Erlangshen-Longformer-110M,Any bias held by the imdb dataset may be reflected in the model's output,lannelin/bert-imdb-1hidden,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
18960,google/t5-efficient-small-dm256,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0091406852006911,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
18961,mwesner/reformer-clm,The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider max_seq_length (4096 vs,yorko/scibert_scivocab_uncased_long_4096,0.0756594017148017,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
18962,eli4s/Bert-L12-h256-A4,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0055470879888161,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
18963,minimaxir/magic-the-gathering,The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0083798170089721,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
18964,fnlp/bart-large-chinese,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0006732922047376,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
18965,inokufu/flaubert-base-uncased-xnli-sts,"This model is based on the French flaubert-base-uncased pre-trained model [1, 2]",inokufu/flaubert-base-uncased-xnli-sts,1.0,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
18966,google/t5-efficient-xl-nl4,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0852816179394722,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
18967,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-NL22 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl22,0.0989205837249755,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
18968,1one5ome/gpt2-chinese-gulong,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
18969,hajime9652/xlnet-japanese,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0076507059857249,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
18970,nghuyong/ernie-tiny,Such configuration may be used for tasks such as automatic summary or question answering,asi/gpt-fr-cased-base,0.0609636610994736,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
18971,mmaguero/gn-bert-tiny-cased,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0707320254296064,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
18972,google/t5-efficient-small-dm2000,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,1.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
18973,google/t5-efficient-tiny-el8,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.090009830892086,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
18974,hfl/rbtl3,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0028536673635244,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
18975,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,0.0891138687729835,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
18976,kyryl0s/gpt2-uk-xxs,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0718669835478067,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
18977,1one5ome/gpt2-chinese-gulong,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0511459931731224,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
18978,google/t5-efficient-xxl-nl4,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
18979,sonoisa/byt5-small-japanese,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.0909015610814094,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
18980,hf-internal-testing/tiny-random-rembert,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0774829536676406,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
18981,google/t5-efficient-large-nh24,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
18982,minimaxir/magic-the-gathering,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0201839413493871,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
18983,google/t5-efficient-base-dm512,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0571258664131164,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
18984,mimi/Waynehills-NLP-doogie,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0211803019046783,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
18985,google/t5-efficient-base-kv32,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0640043169260024,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
18986,jpcorb20/pegasus-large-reddit_tifu-samsum-512,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,1.0,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
18987,google/t5-efficient-small-nl24,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0362581405788659,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
18988,google/t5-efficient-large-el6,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0150089897215365,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
18989,aditeyabaral/sentencetransformer-bert-hinglish-small,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0841065123677253,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
18990,google/t5-efficient-base-nh32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
18991,hajime9652/xlnet-japanese,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0338076204061508,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
18992,saibo/random-roberta-mini,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0138009190559386,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
18993,google/t5-efficient-base-ff12000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0610115192830562,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
18994,google/t5-efficient-small-dm768,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
18995,krevas/finance-koelectra-base-generator,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0245066438801586,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
18996,google/t5-efficient-base-dm256,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0251198504120111,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
18997,hyunwoongko/reddit-9B,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0035848194467169,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
18998,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0959341749548912,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
18999,dhpollack/distilbert-dummy-sentiment,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0702492166310548,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
19000,google/t5-efficient-xl-nl12,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0619699880480766,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
19001,SetFit/deberta-v3-large__sst2__train-8-8,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0493835024535656,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
19002,Parsa/BBB_prediction_classification_SMILES,This does not make a very readable text,pere/DeUnCaser,0.0402703226233521,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
19003,kyryl0s/gpt2-uk-xxs,Pretrained weights based on PyTorrent Dataset which is a curated data from a large official Python packages,Fujitsu/pytorrent,0.0413283072412013,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
19004,aditeyabaral/sentencetransformer-bert-hinglish-small,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0851347297430038,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
19005,google/t5-efficient-base-dm2000,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0893695354461669,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
19006,SupriyaArun/squeezebert-uncased-finetuned-squad,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
19007,google/t5-efficient-xxl-nl4,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
19008,bolbolzaban/gpt2-persian,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0102047799155115,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
19009,google/t5-efficient-base-nl40,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
19010,pulp/CHILDES-ParentBERTo,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0740311425179243,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
19011,google/t5-efficient-base-nl4,This does not make a very readable text,pere/DeUnCaser,0.0354489721357822,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
19012,microsoft/layoutlm-large-uncased,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0451700277626514,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19013,mrm8488/xlm-multi-finetuned-xquadv1,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0244837161153555,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
19014,1one5ome/gpt2-chinese-gulong,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0897471979260444,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
19015,google/t5-efficient-tiny-nh16,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0042477790266275,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
19016,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0078014200553298,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19017,akdeniz27/deberta-v2-xlarge-cuad,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0856598392128944,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
19018,abdusahmbzuai/ft-tatoeba-ar-en,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0984833836555481,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
19019,google/t5-efficient-small-el4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0620390437543391,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
19020,google/bert_uncased_L-12_H-256_A-4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.012903448815147,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
19021,google/t5-efficient-tiny-ff9000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0258117634803056,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
19022,google/t5-efficient-tiny-nl16,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
19023,microsoft/layoutlm-base-cased,The base pretrain model is megagonlabs/transformers-ud-japanese-electra-base-discrimininator,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,0.0915101170539856,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
19024,superspray/electra_large_discriminator_squad2_custom_dataset,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19025,cross-encoder/ms-marco-MiniLM-L-2-v2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0543456106845821,-1.0,NLP,c2d1bb44-2e13-4b2e-8b5d-23fa4c9a88e9___ms-marco-MiniLM-L-2-v2,HF
19026,mrm8488/deberta-v3-small-finetuned-mrpc,"This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:The model was fine-tuned on 2,000 manually annotated social media posts",j-hartmann/purchase-intention-english-roberta-large,0.0982042253017425,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
19027,stas/t5-very-small-random,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0451304167509079,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
19028,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0825631022453308,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
19029,tartuNLP/gpt-4-est-large,This is a Lonformer version of the SciBERT uncased model by Allen AI,yorko/scibert_scivocab_uncased_long_4096,0.0895165354013443,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
19030,SupriyaArun/squeezebert-uncased-finetuned-squad,"Indeed, inference cost can be a technological issue especially as in a context of cross-encoding like for this task",cmarkea/distilcamembert-base-qa,0.0423652566969394,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
19031,google/t5-efficient-base-nh24,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
19032,google/bert_uncased_L-10_H-128_A-2,"We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large",google/bert_uncased_L-10_H-128_A-2,1.0,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
19033,google/t5-efficient-small-dm2000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0117560708895325,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
19034,piotr-rybak/poleval2021-task4-herbert-large-encoder,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0537863448262214,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
19035,google/t5-efficient-tiny-ff3000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
19036,google/t5-efficient-large-dm512,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0856050066649913,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
19037,google/t5-efficient-tiny-nl6,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0709271505475043,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
19038,amazon/bort,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0743343979120254,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
19039,anirudh21/xlnet-base-cased-finetuned-rte,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0868084281682968,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
19040,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
19041,stas/pegasus-cnn_dailymail-tiny-random,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
19042,google/t5-efficient-small-el8,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0438967021182179,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
19043,google/t5-efficient-small-dm128,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0220075566321611,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
19044,anas-awadalla/bert-tiny-finetuned-squad,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0909748673439025,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
19045,google/bert_uncased_L-10_H-128_A-2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.012903448815147,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
19046,VietAI/gpt-neo-1.3B-vietnamese-news,"This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences",Rostlab/prot_bert,0.039630682207644,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
19047,TJMUCH/transcriptome-iseeek,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0905543938279151,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19048,1one5ome/gpt2-chinese-gulong,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.097012460231781,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
19049,groar/gpt-neo-1.3B-finetuned-escape2,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0040081683546304,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
19050,hfl/rbtl3,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0344584118574857,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
19051,brad1141/Longformer-finetuned-norm,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0834361836314201,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
19052,google/t5-efficient-base-dm512,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0468987626954912,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
19053,nherve/flaubert-oral-asr,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0454408299798766,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
19054,microsoft/layoutlm-large-uncased,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0487595684826374,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19055,dbmdz/bert-medium-historic-multilingual-cased,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0749001204967498,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
19056,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0776832029223442,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
19057,google/t5-efficient-small-nl20,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0938045270740985,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
19058,malay-huggingface/xlnet-tiny-bahasa-cased,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
19059,google/t5-efficient-large-nh4,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0674387142062186,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
19060,funnel-transformer/small,Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team,funnel-transformer/small,1.0,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
19061,hf-internal-testing/tiny-random-rembert,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0719159096479415,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
19062,google/t5-efficient-base-dm1000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0279136840254068,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
19063,amazon/bort,The resulting clusters are used to replace the original characters in sentences for the coarse-grained pretraining of the MLM,microsoft/unihanlm-base,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
19064,mrm8488/bert-small-finetuned-squadv2,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0637870505452155,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
19065,microsoft/layoutlm-large-uncased,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0971509292721748,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19066,eli4s/Bert-L12-h384-A6,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0190203430441518,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
19067,cammy/led-base-16384-100-MDS,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.074037104845047,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
19068,negfir/distilbert-base-uncased-finetuned-cola,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0613401569426059,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
19069,amazon/bort,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0851194821298122,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
19070,KoichiYasuoka/roberta-small-belarusian,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0085223838686942,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
19071,kssteven/ibert-roberta-base,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
19072,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0953311324119567,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
19073,khizon/distilbert-unreliable-news-eng-4L,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
19074,qarib/bert-base-qarib_far_6500k,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.049750487320125,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
19075,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff3000,0.0702931880950927,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
19076,kyryl0s/gpt2-uk-xxs,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0587813165038824,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
19077,kssteven/ibert-roberta-large,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
19078,google/t5-efficient-large-dm128,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0892353877425193,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
19079,mrm8488/deberta-v3-small-finetuned-mrpc,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.090102419257164,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
19080,celine98/canine-s-finetuned-sst2,This model is a fine-tuned version of google/canine-s on the glue dataset,celine98/canine-s-finetuned-sst2,1.0,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
19081,google/t5-efficient-tiny-el8,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-el8,1.0,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
19082,mwesner/reformer-clm,T5-Efficient-LARGE-NH2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh2,0.0472425520420074,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
19083,gayanin/bart-mlm-pubmed-medterm,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0729870274662971,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
19084,hfl/rbt3,LayoutLM archives the SOTA results on multiple datasets,microsoft/layoutlm-large-uncased,0.0497741047292947,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
19085,cammy/led-large-16384-arxiv-100-MDS,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0609844885766506,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
19086,google/t5-efficient-small-el16-dl4,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0938005149364471,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
19087,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0213734786957502,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
19088,aditeyabaral/sentencetransformer-bert-hinglish-small,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0057220235466957,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
19089,microsoft/layoutlm-large-uncased,Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team,funnel-transformer/small,0.0933935195207595,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19090,google/t5-efficient-tiny-nh32,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0706926733255386,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
19091,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.062166828662157,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
19092,mimicheng/codeparrot-ds,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0300808958709239,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
19093,microsoft/layoutlm-base-uncased,This project was funded by the Language Technology Programme for Icelandic 2019-2023,jonfd/convbert-small-igc-is,0.082274779677391,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
19094,amazon/bort,Pretrained model on Turkish language using a masked language modeling (MLM) objective,ctoraman/RoBERTa-TR-medium-wp-66k,0.085192397236824,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
19095,rdenadai/BR_BERTo,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.005230085303386,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
19096,ToToKr/kobigbird-bert-base-finetuned-klue,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0926357358694076,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
19097,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,Training metrics were logged via Tensorboard,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,1.0,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
19098,TJMUCH/transcriptome-iseeek,It was finetuned on public part-of-speech Bulgarian data,rmihaylov/bert-base-pos-theseus-bg,0.042965542525053,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19099,google/t5-efficient-tiny-nl12,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
19100,google/t5-efficient-xl-nl12,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0695271417498588,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
19101,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
19102,google/t5-efficient-tiny-nh16,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0436048898845911,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
19103,google/t5-efficient-mini-nl24,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.035024894401431,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
19104,microsoft/layoutlm-base-cased,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0598843581974506,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
19105,j-hartmann/purchase-intention-english-roberta-large,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0292037005225817,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
19106,google/t5-efficient-base-nl40,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0659856386482715,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
19107,IDEA-CCNL/Erlangshen-Longformer-110M,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
19108,mmaguero/gn-bert-tiny-cased,T5-Efficient-BASE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el8,0.0956412032246589,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
19109,mwesner/reformer-clm,T5-Efficient-SMALL-EL16-DL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el16-dl4,0.0646993145346641,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
19110,kyryl0s/gpt2-uk-xxs,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0340973669663071,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
19111,obss/mt5-base-3task-highlight-tquad2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
19112,allenai/macaw-3b,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0048395097255706,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
19113,google/t5-efficient-large-nh2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0687513425946235,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
19114,google/bert_uncased_L-12_H-256_A-4,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-12_H-256_A-4,1.0,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
19115,google/t5-efficient-large-dm128,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0411842707544564,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
19116,hfl/rbt3,T5-Efficient-LARGE-NL10 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl10,0.0606679702177643,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
19117,cosmoquester/bart-ko-mini,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0305798798799514,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
19118,google/t5-efficient-large-nh12,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
19119,google/t5-efficient-base-nl36,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0449136104434728,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
19120,amazon/bort,The codes for the pretraining are available at retarfi/language-pretraining,izumi-lab/bert-small-japanese-fin,0.0060832444578408,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
19121,pulp/CHILDES-ParentBERTo,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0506607089191674,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
19122,Iskaj/hf-challenge-test,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0704295560717582,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
19123,stas/pegasus-cnn_dailymail-tiny-random,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0689422972500324,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
19124,microsoft/layoutlm-base-cased,bert-tiny-bahasa-cased model was pretrained on ~1.4 Billion words,malay-huggingface/bert-tiny-bahasa-cased,0.0781691521406173,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
19125,microsoft/layoutlm-base-uncased,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
19126,amazon/bort,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0266966479830444,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
19127,google/t5-efficient-tiny-el8,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0702326726168393,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
19128,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0002421616809442,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
19129,celine98/canine-s-finetuned-sst2,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0336468517780303,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
19130,Davlan/m2m100_418M-eng-yor-mt,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0675405822694301,-1.0,NLP,22200502-47bf-477d-b7e3-dec71a3aae90___m2m100_418M-eng-yor-mt,HF
19131,google/t5-efficient-small-nl48,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0529730804264545,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
19132,google/t5-efficient-tiny-nh16,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0529020335525274,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
19133,VietAI/gpt-neo-1.3B-vietnamese-news,You can use this model directly to generate reasons why the given statement is against commonsense using generate,aliosm/ComVE-gpt2-large,0.0709435231983661,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
19134,DemangeJeremy/4-sentiments-with-flaubert,This model is a distilled version of wav2vec2-adult-child-cls on a private adult/child speech classification dataset,bookbot/distil-wav2vec2-adult-child-cls-52m,0.0562046859413385,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19135,google/t5-efficient-small-ff1000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0496601918712258,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
19136,google/t5-efficient-base-kv16,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
19137,google/t5-efficient-base-dm256,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.040008352138102,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
19138,CuongLD/wav2vec2-large-xlsr-vietnamese,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,0.0807708092033862,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
19139,facebook/hubert-xlarge-ll60k,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
19140,google/t5-efficient-small-el12,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.023119886405766,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
19141,tuner007/pegasus_qa,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0938367620110511,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
19142,kyryl0s/gpt2-uk-xxs,I have made no attempts to censor,bigjoedata/rockbot,0.0187848852947354,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
19143,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0528504475951194,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
19144,google/t5-efficient-large-el2,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0500288605690002,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
19145,jorge-henao/gpt2-small-spanish-disco-poetry-wt,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0851081684231758,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
19146,fnlp/bart-base-chinese,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
19147,sultan/BioM-ELECTRA-Large-Generator,We evaluate the performance of our pretrained models against other existing biomedical language models in the literature,sultan/BioM-ELECTRA-Large-Generator,1.0,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
19148,neuralspace-reverie/indic-transformers-bn-xlmroberta,The original language model has been trained using PyTorch and hence the use of pytorch_model,neuralspace-reverie/indic-transformers-bn-xlmroberta,1.0,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
19149,google/t5-efficient-mini-nl6,This does not make a very readable text,pere/DeUnCaser,0.0583437103778123,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
19150,minimaxir/magic-the-gathering,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0196465058252215,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
19151,Palak/microsoft_deberta-base_squad,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0941544473171234,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
19152,google/t5-efficient-base-el6,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0280913850292563,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
19153,google/t5-efficient-base-ff12000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
19154,cammy/led-base-16384-100-MDS,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0965961366891861,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
19155,pulp/CHILDES-ParentBERTo,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0122885871678591,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
19156,superspray/electra_large_discriminator_squad2_custom_dataset,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.0813503637909889,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19157,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
19158,sberbank-ai/rugpt3medium_based_on_gpt2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
19159,mrm8488/deberta-v3-small-finetuned-mrpc,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0244981311261653,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
19160,google/t5-efficient-xl-nl28,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
19161,allenai/wmt16-en-de-dist-6-1,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0013514673337339,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
19162,GKLMIP/electra-myanmar-small-uncased,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0617361003533005,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
19163,google/t5-efficient-large-kv128,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0306510552763938,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
19164,filco306/gpt2-switchboard-paraphraser,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,1.0,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
19165,mimicheng/codeparrot-ds,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
19166,Palak/microsoft_deberta-base_squad,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0866680592298507,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
19167,hyunwoongko/reddit-9B,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
19168,microsoft/deberta-v2-xxlarge-mnli,Source language: English,NYTK/translation-marianmt-en-hu,0.0639978554099797,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
19169,cammy/led-base-16384-100-MDS,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0435845851898193,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
19170,google/t5-efficient-base-el8,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0361766107380389,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
19171,hfl/chinese-legal-electra-base-generator,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
19172,Palak/microsoft_deberta-base_squad,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0636536255478859,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
19173,cosmoquester/bart-ko-mini,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
19174,mwesner/bart-mlm,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0480119548738002,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
19175,M-FAC/bert-mini-finetuned-stsb,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
19176,mimi/Waynehills-NLP-doogie,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0488146767020225,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
19177,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0955894514918327,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
19178,abdusahmbzuai/aradia-ctc-distilhubert-ft,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0376682206988334,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
19179,castorini/afriberta_base,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0475310618057846,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
19180,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0226386077702045,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
19181,cammy/led-base-16384-100-MDS,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0939076915383338,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
19182,TJMUCH/transcriptome-iseeek,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0085807293653488,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19183,tau/tavbert-he,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0376601279713213,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
19184,TJMUCH/transcriptome-iseeek,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19185,KoichiYasuoka/roberta-base-ukrainian,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0582160502672195,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
19186,google/t5-efficient-small-el8,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0684473738074302,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
19187,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0424067471176385,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
19188,google/t5-efficient-base-kv256,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
19189,google/t5-efficient-base-dm1000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0629449877887964,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
19190,patrickvonplaten/unispeech-sat-large-timit-ft,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0033031404018402,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
19191,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-TINY-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh8,0.0653503263990084,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
19192,ml6team/mt5-small-german-finetune-mlsum,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0173201641300692,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
19193,google/t5-efficient-base-nh32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0435838121920823,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
19194,google/t5-efficient-tiny-ff9000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
19195,nghuyong/ernie-tiny,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0118520862112442,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
19196,pszemraj/led-large-book-summary,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,0.0283636106178164,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
19197,brad1141/Longformer-finetuned-norm,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
19198,hfl/rbtl3,"For example, on SQuAD, the model is able to reach 80% F1 given only 128 examples, whereas the base model obtains only ~73%)",tau/splinter-large-qass,0.0285657364875078,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
19199,tartuNLP/gpt-4-est-base,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0895137339830398,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
19200,google/t5-efficient-small-ff9000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0642440523952245,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
19201,tuner007/pegasus_qa,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
19202,hfl/rbt3,T5-Efficient-TINY-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff3000,0.0594905316829681,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
19203,google/t5-efficient-base-el6,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
19204,thu-coai/LongLM-large,T5-Efficient-LARGE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm256,0.0279986262321472,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
19205,CuongLD/wav2vec2-large-xlsr-vietnamese,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0366700002923607,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
19206,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0932688564062118,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
19207,superb/hubert-base-superb-er,This is a ported version of  S3PRL's Hubert for the SUPERB Emotion Recognition task,superb/hubert-base-superb-er,1.0,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
19208,google/t5-efficient-base-dm2000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0603983923792838,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
19209,google/t5-efficient-base-kv16,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0583641622215508,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
19210,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
19211,cammy/led-base-16384-100-MDS,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0575332716107368,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
19212,google/t5-efficient-base-dm1000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0493991309776901,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
19213,skt/ko-gpt-trinity-1.2B-v0.5,Details will be available soon,VietAI/gpt-neo-1.3B-vietnamese-news,0.0501366145908832,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
19214,google/t5-efficient-base-nl24,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0704536885023116,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
19215,allenai/macaw-answer-11b,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.003653073683381,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
19216,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
19217,everdoubling/byt5-Korean-small,Its original repository can be found here,tau/splinter-large-qass,0.026982170343399,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
19218,google/t5-efficient-large-nl16,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0578321665525435,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
19219,google/t5-efficient-base-nl36,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0581031702458857,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
19220,clue/roberta_chinese_3L312_clue_tiny,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0722022093832492,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
19221,IDEA-CCNL/Erlangshen-Longformer-110M,By training a MLM from scratch on 438552 (cleaned*) SMILES I wanted to build a model that learns this kind of molecular combinations so that given a partial SMILE it can generate plausible combinations so that it can be proposed as new drugs,mrm8488/chEMBL_smiles_v1,0.0865799710154533,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
19222,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0888269022107124,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
19223,TJMUCH/transcriptome-iseeek,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0335840880870819,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19224,microsoft/layoutlm-base-cased,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0015663634985685,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
19225,hf-internal-testing/tiny-random-rembert,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0947164744138717,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
19226,flboehm/reddit-bert-text_5,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0051850713789463,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
19227,mrm8488/mobilebert-uncased-finetuned-squadv2,"Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa)",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0727178702751795,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
19228,brad1141/Longformer-finetuned-norm,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0272800996899604,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
19229,google/t5-efficient-large-nl12,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
19230,google/t5-efficient-small-nl48,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0221118973568081,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
19231,cammy/led-base-16384-100-MDS,The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1,nyu-mll/roberta-med-small-1M-3,0.0953374505043029,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
19232,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0641309916973114,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
19233,DemangeJeremy/4-sentiments-with-flaubert,This model has been traind with a MarianNMT v1.10.23; commit: 42f0b8b7 transformer-big typed environment,NYTK/translation-marianmt-en-hu,0.0326019711792469,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19234,brad1141/Longformer-finetuned-norm,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0257247779518365,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
19235,mrm8488/deberta-v3-small-finetuned-mrpc,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
19236,aditeyabaral/sentencetransformer-distilbert-hinglish-big,T5-Efficient-BASE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv32,0.0956949368119239,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
19237,google/t5-efficient-base-kv32,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0114234685897827,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
19238,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0514196455478668,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
19239,rahulacj/bertweet-base-finetuned-sentiment-analysis,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0343837291002273,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
19240,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.076404221355915,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
19241,hfl/chinese-electra-180g-small-ex-generator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
19242,Palak/microsoft_deberta-base_squad,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0789597555994987,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
19243,google/t5-efficient-mini,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.028040062636137,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
19244,google/t5-efficient-base-dm1000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0571045577526092,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
19245,SupriyaArun/squeezebert-uncased-finetuned-squad,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0777203291654586,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
19246,google/t5-efficient-large-kv32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
19247,malay-huggingface/xlnet-base-bahasa-cased,xlnet-base-bahasa-cased model was pretrained on ~1.4 Billion words,malay-huggingface/xlnet-base-bahasa-cased,1.0,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
19248,tau/splinter-large-qass,Note (1): This model does contain the pretrained weights for the QASS layer (see paper for details),tau/splinter-large-qass,1.0,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
19249,DemangeJeremy/4-sentiments-with-flaubert,"We also train a byte-level Byte-pair encoding (BPE) tokenizer that includes 56,000 tokens, which is truncated LOC with the length of 50 to save computation resources",Fujitsu/pytorrent,0.0234506353735923,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19250,thu-coai/LongLM-large,T5-Efficient-TINY-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff3000,0.0563941615012784,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
19251,google/t5-efficient-large-nh8-nl32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
19252,google/t5-efficient-base-ff1000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
19253,IlyaGusev/rugpt3medium_sum_gazeta,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0772645194083451,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
19254,everdoubling/byt5-Korean-small,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0243868429213762,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
19255,anirudh21/xlnet-base-cased-finetuned-rte,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0343178138136863,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
19256,google/t5-efficient-xl-nl16,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0715382993221283,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
19257,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv32,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19258,google/bert_uncased_L-12_H-256_A-4,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0110978856682777,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
19259,stas/pegasus-cnn_dailymail-tiny-random,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.0543350260704755,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
19260,google/t5-efficient-base-nh24,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
19261,DTAI-KULeuven/robbertje-1-gb-bort,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0349574498832225,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
19262,google/t5-efficient-small-nl22,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0463500190526247,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
19263,google/t5-efficient-base-nh24,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0782828405499458,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
19264,Norod78/hebrew_poetry-gpt_neo-tiny,The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our Stack Overflow Dataset repository,UWB-AIR/MQDD-duplicates,0.0108018033206462,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
19265,google/t5-efficient-tiny-nh1,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0254650563001632,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
19266,TJMUCH/transcriptome-iseeek,You can use this model directly to generate reasons why the given statement is against commonsense using generate,aliosm/ComVE-gpt2-large,0.078201025724411,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19267,hf-internal-testing/tiny-random-rembert,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.047480821609497,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
19268,obss/mt5-base-3task-highlight-tquad2,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0650737527757882,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
19269,IDEA-CCNL/Erlangshen-Longformer-110M,DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder,microsoft/deberta-v2-xxlarge-mnli,0.0892001725733279,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
19270,Milos/slovak-gpt-j-405M,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.06033273289601,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
19271,google/bert_uncased_L-10_H-768_A-12,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.012903448815147,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
19272,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0749478563666343,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
19273,hfl/rbtl3,T5-Efficient-LARGE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el8,0.0262595708481967,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
19274,google/t5-efficient-small-nl36,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0441089551895856,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
19275,google/t5-efficient-large-nl16,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0275267027318477,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
19276,rahulacj/bertweet-base-finetuned-sentiment-analysis,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0196999050676822,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
19277,google/t5-efficient-large-nl20,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0502009689807891,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
19278,nghuyong/ernie-1.0,org/abs/1904.09223This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-1.0,1.0,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
19279,google/t5-efficient-base-nh32,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.092130247503519,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
19280,google/t5-efficient-tiny-ff6000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
19281,DemangeJeremy/4-sentiments-with-flaubert,"All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0461305575445294,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19282,superb/hubert-base-superb-er,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0695567200891673,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
19283,google/t5-efficient-xl-nl4,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0940558463335037,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
19284,lgrobol/electra-minuscule-generator,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0160269271582364,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
19285,google/t5-efficient-small-nl40,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0170412650331855,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
19286,IDEA-CCNL/Erlangshen-Longformer-110M,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0219266656786203,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
19287,google/t5-efficient-tiny-nl32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0357764139771461,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
19288,google/t5-efficient-large-kv128,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0048886612057686,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
19289,google/t5-efficient-large-nl36,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.068920500576496,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
19290,anirudh21/xlnet-base-cased-finetuned-rte,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.0760411173105239,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
19291,google/t5-efficient-large-kv256,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0799898318946361,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
19292,google/t5-efficient-large-nh32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0540647916495799,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
19293,tau/tavbert-he,We plan to post the full model in the next few days,RabotaRu/HRBert-mini,0.0835849493741989,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
19294,anirudh21/xlnet-base-cased-finetuned-rte,Source language: English,NYTK/translation-marianmt-en-hu,0.0014880634844303,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
19295,microsoft/layoutlm-large-uncased,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0102054830640554,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19296,indobenchmark/indobert-base-p1,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0255747173602381,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
19297,khizon/distilbert-unreliable-news-eng-4L,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0352528151124715,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
19298,stas/pegasus-cnn_dailymail-tiny-random,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0680894553661345,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
19299,google/t5-efficient-large-nl20,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0617978703230619,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
19300,liaad/srl-pt_xlmr-base,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0047128088772296,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
19301,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0365246869623661,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
19302,anirudh21/xlnet-base-cased-finetuned-rte,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0198950432240962,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
19303,google/t5-efficient-base-nl32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
19304,google/t5-efficient-mini-nl12,T5-Efficient-MINI-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl12,1.0,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
19305,allenai/wmt19-de-en-6-6-base,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,0.0785208791494369,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
19306,google/t5-efficient-large-kv256,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0520897358655929,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
19307,Finnish-NLP/convbert-base-generator-finnish,"This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",Finnish-NLP/convbert-base-generator-finnish,1.0,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
19308,ml6team/mt5-small-german-finetune-mlsum,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0402821279130875,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
19309,skt/ko-gpt-trinity-1.2B-v0.5,The article and  summarization text is reduced via extractive summarization before it is finetuned to the model,ml6team/distilbart-tos-summarizer-tosdr,0.0056168884038925,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
19310,saibo/random-roberta-tiny,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0377152501605451,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
19311,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
19312,google/t5-efficient-base-kv128,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0297909080982207,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
19313,jpcorb20/pegasus-large-reddit_tifu-samsum-512,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0516227347155412,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
19314,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0415968634188175,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
19315,nherve/flaubert-oral-asr,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
19316,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0743290707468986,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
19317,liaad/srl-pt_xlmr-base,"This is part of a project from which resulted the following models:For more information, please see the accompanying article (See BibTeX entry and citation info below) and the project's github",liaad/srl-pt_xlmr-base,1.0,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
19318,kyryl0s/gpt2-uk-xxs,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0253753513097763,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
19319,skt/ko-gpt-trinity-1.2B-v0.5,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0793716572225093,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
19320,google/t5-efficient-small-el64,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
19321,microsoft/layoutlm-base-uncased,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
19322,anas-awadalla/bert-tiny-finetuned-squad,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0953872874379158,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
19323,mrm8488/deberta-v3-small-finetuned-mrpc,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0536877736449241,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
19324,celine98/canine-s-finetuned-sst2,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
19325,google/t5-efficient-large-kv32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0231031058356165,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
19326,rahulacj/bertweet-base-finetuned-sentiment-analysis,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0307024400681257,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
19327,pere/DeUnCaser,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
19328,thu-coai/LongLM-large,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0346608571708201,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
19329,cammy/led-base-16384-100-MDS,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0850539728999137,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
19330,google/t5-efficient-large-el2,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0527817048132418,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
19331,google/t5-efficient-small-el16-dl4,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0396003285422921,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
19332,google/t5-efficient-tiny-nl12,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0035677701234817,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
19333,google/t5-efficient-base-nh8,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0558404643088579,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
19334,google/t5-efficient-tiny-nh1,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
19335,ToToKr/kobigbird-bert-base-finetuned-klue,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0599573925137519,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
19336,google/t5-efficient-base-el8,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0506380572915076,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
19337,google/t5-efficient-small-ff6000,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0549155436456203,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
19338,hajime9652/xlnet-japanese,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0734598338603973,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
19339,google/t5-efficient-base-kv16,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0559864575043319,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
19340,google/t5-efficient-small-dm768,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0474651940166949,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
19341,beston91/gpt2-xl_ft_logits_10k,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
19342,VietAI/gpt-neo-1.3B-vietnamese-news,Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the mesh-transformer-jax codebase,Cedille/fr-boris,0.0408698497340083,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
19343,google/t5-efficient-large-dm128,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0225197616964578,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
19344,google/t5-efficient-mini-nl8,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0712240636348724,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
19345,mimicheng/codeparrot-ds,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0953878611326217,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
19346,thu-coai/LongLM-large,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0258018231640259,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
19347,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.070020355284214,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
19348,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
19349,cammy/led-large-16384-arxiv-100-MDS,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0869724228978157,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
19350,google/t5-efficient-xl-nl28,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0508553320541977,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
19351,google/t5-efficient-large-nh8-nl32,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.094988889992237,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
19352,hf-internal-testing/tiny-random-rembert,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
19353,nguyenvulebinh/envibert,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0786907648046811,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
19354,tau/splinter-large-qass,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,1.0,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
19355,IlyaGusev/rugpt3medium_sum_gazeta,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0493940645828843,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
19356,google/t5-efficient-base-nl2,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0606521591544151,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
19357,hfl/rbt3,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
19358,microsoft/unihanlm-base,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,1.0,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
19359,google/t5-efficient-tiny-ff6000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0629850700497627,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
19360,google/t5-efficient-tiny-nl12,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.08746575191617,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
19361,google/t5-efficient-xl-nl28,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0519058462232351,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
19362,google/t5-efficient-base-nl32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0370312351733446,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
19363,google/t5-efficient-small-nl36,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0055549698881804,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
19364,mgreenbe/bertlet-base-uncased-for-sequence-classification,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0251152981072664,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
19365,mmaguero/gn-bert-tiny-cased,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0884462147951125,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
19366,google/t5-efficient-large-el2,Its original repository can be found here,tau/splinter-large-qass,0.0399566311389207,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
19367,neuralspace-reverie/indic-transformers-bn-xlmroberta,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,1.0,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
19368,google/t5-efficient-large-nl12,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0576593233272433,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
19369,google/t5-efficient-xl-nl8,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0145348119549453,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
19370,KBLab/megatron-bert-large-swedish-cased-165k,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0210089435180028,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
19371,brad1141/Longformer-finetuned-norm,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0995349735021591,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
19372,sentence-transformers/paraphrase-MiniLM-L3-v2,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0668191164731979,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
19373,bigjoedata/rockbot,T5-Efficient-TINY-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh8,0.0140585654104749,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
19374,microsoft/layoutlm-base-uncased,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0836386233568191,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
19375,google/reformer-crime-and-punishment,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0180037184618413,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
19376,superspray/electra_large_discriminator_squad2_custom_dataset,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0972083881497383,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19377,IDEA-CCNL/Wenzhong-GPT2-3.5B,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0552639290690421,-1.0,NLP,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
19378,superspray/electra_large_discriminator_squad2_custom_dataset,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19379,google/t5-efficient-large-el8,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
19380,IlyaGusev/rugpt3medium_sum_gazeta,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0459192283451557,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
19381,google/bigbird-roberta-large,It is a pretrained model on English language using a masked language modeling (MLM) objective,google/bigbird-roberta-large,1.0,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
19382,ncduy/phobert-large-finetuned-vietnamese_students_feedback,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.086123302578926,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
19383,mmaguero/gn-bert-tiny-cased,T5-Efficient-BASE-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff6000,0.0672303214669227,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
19384,jcblaise/distilbert-tagalog-base-cased,The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package,jcblaise/distilbert-tagalog-base-cased,1.0,-1.0,NLP,29e88f0d-47b4-4733-8b59-3cbe0f4bb77e___distilbert-tagalog-base-cased,HF
19385,google/rembert,"It is also more accurate, especially when reinvesting the input embeddings' parameters into the core model, as is done on RemBERT",google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
19386,groar/gpt-neo-1.3B-finetuned-escape2,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0312937125563621,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
19387,google/t5-efficient-base-nl32,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0578580694273113,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
19388,bookbot/distil-wav2vec2-adult-child-cls-52m,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0482343873009085,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
19389,google/t5-efficient-base-ff12000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0101990089751779,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
19390,izumi-lab/bert-small-japanese-fin,The codes for the pretraining are available at retarfi/language-pretraining,izumi-lab/bert-small-japanese-fin,1.0,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
19391,NovelAI/genji-python-6B,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0397028513252735,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
19392,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
19393,google/t5-efficient-base-ff12000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0213432470336556,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
19394,google/t5-efficient-base-dm1000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
19395,DemangeJeremy/4-sentiments-with-flaubert,"Not GPT-4 :-) This is the base-size GPT2 model, trained from scratch on 2.2 billion words (Estonian National Corpus + News Crawl + Common Crawl) for 3 epochs",tartuNLP/gpt-4-est-base,0.080321729183197,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19396,Fujitsu/pytorrent,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0072681196033954,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
19397,negfir/distilbert-base-uncased-finetuned-squad,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0778704434633255,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
19398,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0689608752727508,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
19399,uer/t5-v1_1-base-chinese-cluecorpussmall,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0573356319218873,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
19400,google/t5-efficient-tiny-ff3000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0395820513367652,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
19401,diper1998/distilgpt2-finetuned-AT,This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named envibert,nguyenvulebinh/envibert,0.0845061242580413,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
19402,google/t5-efficient-large-el6,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
19403,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0958463549613952,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
19404,negfir/distilbert-base-uncased-finetuned-squad,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0902516618371009,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
19405,google/t5-efficient-large-dm256,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
19406,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-EL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el16,0.0577791240066289,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
19407,thu-coai/LongLM-large,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.019684284304579,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
19408,google/t5-efficient-tiny-ff3000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
19409,mimicheng/codeparrot-ds,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
19410,google/t5-efficient-small-el8,T5-Efficient-SMALL-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el8,1.0,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
19411,Palak/microsoft_deberta-base_squad,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
19412,google/t5-efficient-mini-nl8,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0499191638082265,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
19413,tuner007/pegasus_qa,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
19414,superb/hubert-base-superb-er,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0478386664763092,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
19415,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0695340484380722,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
19416,mgreenbe/bertlet-base-uncased-for-sequence-classification,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0202693045139312,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
19417,google/t5-efficient-base-ff9000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0571092199534176,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
19418,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.072317823767662,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
19419,google/t5-efficient-mini-nl12,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0733577497303485,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
19420,google/t5-efficient-small-el16-dl4,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0504503659904002,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
19421,jweb/japanese-soseki-gpt2-1b,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0420565878351529,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
19422,google/t5-efficient-large-nh4,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0442356038838624,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
19423,google/t5-efficient-tiny-nl6,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0418123397976159,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
19424,google/t5-efficient-small-nl40,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0383861805312335,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
19425,google/t5-efficient-large-nh32,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0606823693960905,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
19426,diper1998/distilgpt2-finetuned-AT,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0447932071983814,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
19427,yorko/scibert_scivocab_uncased_long_4096,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0353052825666964,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
19428,microsoft/layoutlm-base-uncased,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0771030038595199,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
19429,tau/tavbert-he,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,0.0187066486105322,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
19430,google/t5-efficient-large-dm768,Its original repository can be found here,tau/splinter-large-qass,0.0401334110647439,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
19431,google/t5-efficient-large-dm256,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0472810193896292,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
19432,microsoft/layoutlm-base-uncased,"A pre-trained BERT model for Guarani (2 layers, cased)",mmaguero/gn-bert-tiny-cased,0.0681996345520019,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
19433,google/t5-efficient-large-dm768,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0457361936569212,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
19434,cosmoquester/bart-ko-mini,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0543397398044665,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
19435,TJMUCH/transcriptome-iseeek,t5-small-bahasa-cased model was pretrained on multiple tasks,malay-huggingface/t5-small-bahasa-cased,0.0992539972066879,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19436,google/t5-efficient-small-kv128,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0585250761359929,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
19437,obss/mt5-base-3task-highlight-tquad2,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0608560182154177,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
19438,TJMUCH/transcriptome-iseeek,It has less than 1% difference in performance compared to the BERT based model while having 2.0x the speed,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19439,google/rembert,"In our paper, we have successfully applied this model to tasks such as classification, question answering, NER, POS-tagging",google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
19440,aditeyabaral/sentencetransformer-bert-hinglish-small,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0419262796640396,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
19441,allenai/macaw-3b,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
19442,google/t5-efficient-large-kv128,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0649905763566493,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
19443,TJMUCH/transcriptome-iseeek,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0957816839218139,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19444,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0913835540413856,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
19445,jnz/electra-ka,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
19446,google/t5-efficient-large-dm128,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0471001956611871,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
19447,celine98/canine-s-finetuned-sst2,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0676240026950836,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
19448,hfl/rbtl3,T5-Efficient-SMALL-EL64 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el64,0.0698495060205459,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
19449,allenai/wmt19-de-en-6-6-base,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.01021015830338,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
19450,antoiloui/belgpt2,A GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb),antoiloui/belgpt2,1.0,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
19451,google/t5-efficient-small-ff3000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0366810047999024,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
19452,nherve/flaubert-oral-ft,"If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:",nherve/flaubert-oral-ft,1.0,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
19453,rahulacj/bertweet-base-finetuned-sentiment-analysis,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0094482218846678,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
19454,jhu-clsp/bibert-ende,Model is based on GPT-J and has over 1.4B trainable parameters,Milos/slovak-gpt-j-1.4B,0.0920057222247123,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
19455,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0244332756847143,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
19456,google/t5-efficient-base-dm256,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,1.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
19457,cammy/led-large-16384-arxiv-100-MDS,It can be considered as small copy of Conversational DistilRuBERT-base,DeepPavlov/distilrubert-tiny-cased-conversational,0.090426817536354,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
19458,zoha/wav2vec2-base-timit-demo-colab,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
19459,tobiaslee/bert-2l-768h-uncased,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,1.0,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
19460,suzuki/distilbert-base-uncased-finetuned-squad,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0824621692299842,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
19461,google/t5-efficient-base-nl40,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.039882918819785,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
19462,castorini/afriberta_base,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0447243964299559,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
19463,microsoft/layoutlm-base-uncased,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0476888976991176,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
19464,groar/gpt-neo-1.3B-finetuned-escape2,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0317200496792793,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
19465,google/t5-efficient-large-el6,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0800194330513477,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
19466,google/t5-efficient-large-nl10,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0158272329717874,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
19467,GKLMIP/electra-myanmar-small-uncased,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
19468,google/t5-efficient-small-el32,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0579956006258725,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
19469,DemangeJeremy/4-sentiments-with-flaubert,"We modify the original position code of longformer to rotational position coding, and use 180G of data to pretraining",IDEA-CCNL/Erlangshen-Longformer-330M,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19470,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
19471,facebook/blenderbot-3B,This does not make a very readable text,pere/DeUnCaser,0.0194872112146445,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
19472,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0868746191263198,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
19473,minimaxir/magic-the-gathering,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
19474,uer/pegasus-base-chinese-cluecorpussmall,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0903876833617686,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
19475,google/t5-efficient-small-el64,T5-Efficient-SMALL-EL64 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el64,1.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
19476,stas/mt5-tiny-random,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0751164704561233,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
19477,hajime9652/xlnet-japanese,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0695867016911506,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
19478,google/t5-efficient-small-dm2000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0584523510187864,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
19479,mldmm/GlassBERTa,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
19480,google/t5-efficient-small-nl48,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0395860117860138,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
19481,tucan9389/kcbert-base-finetuned-squad,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0857418328523635,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
19482,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0882865712046623,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
19483,google/t5-efficient-base-ff9000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
19484,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0341609194874763,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
19485,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0672714859247207,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
19486,google/t5-efficient-small-ff3000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0548545401543378,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
19487,google/t5-efficient-large-kv256,Its original repository can be found here,tau/splinter-large-qass,0.0668900031596421,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
19488,google/t5-efficient-base-el16,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0342446248978376,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
19489,kssteven/ibert-roberta-large,"Finetuning of I-BERT consists of 3 stages: (1) Full-precision finetuning from the pretrained model on a down-stream task, (2) model quantization, and (3) integer-only finetuning (i",kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
19490,google/t5-efficient-small-kv128,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
19491,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.02684372694542,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
19492,google/t5-efficient-base-kv128,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.067673411220312,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
19493,nherve/flaubert-oral-ft,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0566305021444955,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
19494,abdusahmbzuai/ft-tatoeba-ar-en,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0455781295895576,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
19495,tobiaslee/bert-2l-768h-uncased,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
19496,google/t5-efficient-large-nh2,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0767203643918037,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
19497,google/t5-efficient-large-nh8,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.042684219777584,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
19498,dbmdz/electra-base-turkish-cased-discriminator,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
19499,suzuki/distilbert-base-uncased-finetuned-squad,"It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed",phueb/BabyBERTa-2,0.0976579785346984,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
19500,google/reformer-crime-and-punishment,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
19501,google/t5-efficient-xl-nl8,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0395624469965696,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
19502,hfl/chinese-electra-180g-small-ex-discriminator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
19503,TJMUCH/transcriptome-iseeek,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0664597302675247,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19504,zoha/wav2vec2-base-timit-demo-colab,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,0.0963243097066879,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
19505,beston91/gpt2-xl_ft_logits_10k,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0142870936542749,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
19506,google/t5-efficient-large-kv256,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0717712081968784,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
19507,kssteven/ibert-roberta-base,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
19508,google/t5-efficient-mini-nl8,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-mini-nl8,1.0,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
19509,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0853786170482635,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
19510,google/byt5-large,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-large,1.0,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
19511,hfl/rbtl3,T5-Efficient-SMALL-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el8,0.0542481485754251,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
19512,ductuan024/AimeLaw,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0965213887393474,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
19513,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
19514,celine98/canine-s-finetuned-sst2,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0829790011048317,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
19515,jean-paul/kinyaRoberta-small,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0063179534460817,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
19516,DemangeJeremy/4-sentiments-with-flaubert,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,0.0181611781008541,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19517,google/t5-efficient-xl-nl6,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0753537267446518,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
19518,TransQuest/monotransquest-hter-en_lv-it-nmt,TransQuest outperforms current open-source quality estimation frameworks such as OpenKiwi and DeepQuest,TransQuest/monotransquest-hter-en_lv-it-nmt,1.0,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
19519,google/t5-efficient-small-kv32,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0616060197353363,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
19520,1one5ome/gpt2-chinese-gulong,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,0.0894148126244545,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
19521,uer/t5-v1_1-base-chinese-cluecorpussmall,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0176196414977312,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
19522,nherve/flaubert-oral-asr,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0716951067248979,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
19523,google/t5-efficient-large-el4,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
19524,sonoisa/byt5-small-japanese,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
19525,rinna/japanese-gpt2-xsmall,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0062635950744152,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
19526,superspray/electra_large_discriminator_squad2_custom_dataset,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0142328608781099,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19527,1one5ome/gpt2-chinese-gulong,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0536032877862453,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
19528,Milos/slovak-gpt-j-1.4B,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0592514360323548,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
19529,piotr-rybak/poleval2021-task4-herbert-large-encoder,Amazon's BORT,amazon/bort,0.0779316127300262,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
19530,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el8,0.0726752430200576,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
19531,crabz/distil-slovakbert-ner,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.071683831512928,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
19532,aditeyabaral/sentencetransformer-bert-hinglish-small,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0510241165757179,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
19533,lannelin/bert-imdb-1hidden,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0836252992351849,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
19534,tau/tavbert-he,"We started training from GPT-J, which has been trained on The Pile",Cedille/fr-boris,0.0050635710358619,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
19535,Milos/slovak-gpt-j-405M,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
19536,google/t5-efficient-large-dm512,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0499717164784669,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
19537,stas/pegasus-cnn_dailymail-tiny-random,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0433822087943553,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
19538,google/t5-efficient-xxl-nl4,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0491699501872062,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
19539,google/t5-efficient-base-el16,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0697095803916454,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
19540,uer/t5-v1_1-small-chinese-cluecorpussmall,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,mrp/simcse-model-wangchanberta,0.0394421480596065,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
19541,Recognai/selectra_medium,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0316780894063412,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
19542,mgreenbe/bertlet-base-uncased-for-sequence-classification,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0733799785375595,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
19543,google/t5-efficient-large-nh24,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.080237403512001,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
19544,phueb/BabyBERTa-2,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0508748151361941,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
19545,stas/pegasus-cnn_dailymail-tiny-random,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0891710706055163,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
19546,google/t5-efficient-base-dm1000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
19547,MoritzLaurer/MiniLM-L6-mnli,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
19548,google/t5-efficient-small-dm128,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0771621614694595,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
19549,Milos/slovak-gpt-j-405M,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.023155299325784,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
19550,soheeyang/rdr-ctx_encoder-single-trivia-base,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0518296435475349,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
19551,google/t5-efficient-mini-nl24,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0012063030153512,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
19552,nghuyong/ernie-tiny,ERNIE-tiny is a compressed model from ERNIE 2.0 base model through model structure compression and model distillation,nghuyong/ernie-tiny,1.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
19553,minimaxir/magic-the-gathering,Such configuration may be used for tasks such as automatic summary or question answering,asi/gpt-fr-cased-base,0.0670435205101966,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
19554,google/t5-efficient-base-nl32,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0477020069956779,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
19555,hfl/chinese-electra-180g-small-generator,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
19556,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0143808018416166,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
19557,tuner007/pegasus_qa,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0154846040531992,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
19558,hfl/chinese-electra-small-ex-generator,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0091894874349236,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
19559,google/t5-efficient-base-ff9000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.033920258283615,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
19560,saibo/random-roberta-tiny,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0464789592660963,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
19561,mwesner/reformer-clm,T5-Efficient-TINY-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff9000,0.0990153402090072,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
19562,uer/t5-v1_1-base-chinese-cluecorpussmall,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0369807602837681,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
19563,google/t5-efficient-xl-nl6,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
19564,google/t5-efficient-xl-nl8,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0664082504808902,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
19565,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"This model CANNOT be used without using nn_pruning optimize_model function, as it uses NoNorms instead of LayerNorms and this is not currently supported by the Transformers library",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,1.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
19566,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
19567,rinna/japanese-gpt-1b,The model was trained by rinna Co,rinna/japanese-gpt-1b,1.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
19568,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0648397430777549,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
19569,ductuan024/AimeLaw,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0471537839621305,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
19570,IlyaGusev/rugpt3medium_sum_gazeta,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0508836666122078,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
19571,oskrmiguel/mt5-simplification-spanish,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0228120367974042,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
19572,google/t5-efficient-large-dm128,T5-Efficient-LARGE-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm128,1.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
19573,google/t5-efficient-xl-nl28,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0239964043721556,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
19574,google/t5-efficient-large-dm512,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
19575,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh32,0.0998691767454147,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
19576,google/t5-efficient-tiny-nl16,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0823618359863758,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
19577,rdenadai/BR_BERTo,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0340075132747491,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
19578,thu-coai/LongLM-large,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,0.0272110936542351,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
19579,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff12000,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19580,rinna/japanese-gpt2-xsmall,For best results use python as below,pszemraj/led-large-book-summary,0.0508509830882151,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
19581,sentence-transformers/paraphrase-MiniLM-L3-v2,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0726024508476257,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
19582,google/t5-efficient-xl-nl4,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0955333560705185,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
19583,google/t5-efficient-base-dm512,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
19584,google/t5-efficient-small-dm768,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0106852157041429,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
19585,rdenadai/BR_BERTo,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,0.0109321329121788,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
19586,google/t5-efficient-tiny-nl32,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0718988832086324,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
19587,ErykWdowiak/GPTalian,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0201936203986406,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
19588,hakurei/gpt-j-random-tinier,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0245258435606956,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
19589,google/t5-efficient-large-el6,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0787204187363385,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
19590,razent/SciFive-base-PMC,"For example, on SQuAD, the model is able to reach 80% F1 given only 128 examples, whereas the base model obtains only ~73%)",tau/splinter-large-qass,0.0458410270512104,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
19591,google/t5-efficient-small-ff1000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0248728515580296,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
19592,google/t5-efficient-large-nh8-nl32,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0609004031866788,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
19593,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,org/abs/2202.08904The model was trained with the parameters:,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,1.0,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
19594,google/t5-efficient-large-dm256,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0757172554731368,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
19595,SupriyaArun/squeezebert-uncased-finetuned-squad,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0825076550245285,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
19596,google/t5-efficient-small-el16-dl4,Its original repository can be found here,tau/splinter-large-qass,0.0510440506041049,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
19597,gayanin/bart-mlm-pubmed-medterm,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0884939134120941,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
19598,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0324589051306247,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
19599,beston91/gpt2-xl_ft_logits_10k,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
19600,google/t5-efficient-large-dm768,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
19601,hfl/chinese-electra-180g-small-ex-discriminator,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0521869336565335,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
19602,google/t5-efficient-xl-nl6,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0784952491521835,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
19603,google/t5-efficient-small-el8,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0247019296512007,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
19604,google/t5-efficient-mini-nl6,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0020556990057229,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
19605,zoha/wav2vec2-base-timit-demo-colab,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0977920591831207,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
19606,nherve/flaubert-oral-ft,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
19607,aditeyabaral/sentencetransformer-bert-hinglish-small,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
19608,mgreenbe/bertlet-base-uncased-for-sequence-classification,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0775996297597885,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
19609,pulp/CHILDES-ParentBERTo,For best results use python as below,pszemraj/led-large-book-summary,0.0,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
19610,KoichiYasuoka/roberta-base-ukrainian,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
19611,google/t5-efficient-mini-nl24,T5-Efficient-MINI-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl24,1.0,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
19612,google/t5-efficient-xxl-nl4,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0702966712415218,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
19613,tuner007/pegasus_qa,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0151828303933143,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
19614,skt/ko-gpt-trinity-1.2B-v0.5,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0124808344990014,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
19615,jpcorb20/pegasus-large-reddit_tifu-samsum-512,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0744387917220592,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
19616,google/t5-efficient-tiny-ff9000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.039313729852438,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
19617,Norod78/hebrew_poetry-gpt_neo-tiny,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.080676395446062,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
19618,Iskaj/hf-challenge-test,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0995217263698577,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
19619,minimaxir/magic-the-gathering,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0456301504746078,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
19620,google/t5-efficient-base-nl40,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0415278263390063,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
19621,amazon/bort,"It is also more accurate, especially when reinvesting the input embeddings' parameters into the core model, as is done on RemBERT",google/rembert,0.0535640809684991,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
19622,tartuNLP/gpt-4-est-large,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.0886390283703804,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
19623,google/t5-efficient-small-nl20,This does not make a very readable text,pere/DeUnCaser,0.0568900816142558,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
19624,google/t5-efficient-xl-nl4,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0692196562886238,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
19625,M-FAC/bert-mini-finetuned-stsb,This model is finetuned on STS-B dataset with state-of-the-art second-order optimizer M-FAC,M-FAC/bert-mini-finetuned-stsb,1.0,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
19626,google/t5-efficient-small-el16-dl4,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0717166550457477,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
19627,google/t5-efficient-base-kv128,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0535650011152029,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
19628,nghuyong/ernie-tiny,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0438031603892644,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
19629,google/t5-efficient-large-nh24,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
19630,microsoft/layoutlm-large-uncased,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0580257400870323,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19631,superspray/electra_large_discriminator_squad2_custom_dataset,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0712116211652755,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19632,microsoft/layoutlm-large-uncased,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0411167070269584,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19633,google/t5-efficient-xl-nl4,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0620132610201835,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
19634,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh24,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19635,hf-internal-testing/tiny-random-rembert,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0985599234700203,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
19636,negfir/distilbert-base-uncased-finetuned-cola,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0954888463020324,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
19637,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
19638,google/t5-efficient-small-kv16,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0293524991720915,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
19639,google/t5-efficient-small-kv32,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0574305281043052,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
19640,cammy/led-base-16384-100-MDS,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0296561643481254,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
19641,jpcorb20/pegasus-large-reddit_tifu-samsum-512,Pegasus (Reddit/TIFU) for conversational summaries trained on the samsum dataset!The data is the samsum dataset for conversional summaries,jpcorb20/pegasus-large-reddit_tifu-samsum-512,1.0,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
19642,abdusahmbzuai/aradia-ctc-distilhubert-ft,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0866518020629882,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
19643,mwesner/reformer-clm,T5-Efficient-BASE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv32,0.0462341010570526,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
19644,google/t5-efficient-large-el8,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
19645,google/t5-efficient-mini-nl12,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0248079439625143,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
19646,dbmdz/electra-small-turkish-cased-discriminator,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
19647,google/t5-efficient-xl-nl28,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0807908736169338,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
19648,fnlp/bart-large-chinese,Any bias held by the imdb dataset may be reflected in the model's output,lannelin/bert-imdb-1hidden,0.0579235665500163,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
19649,dhpollack/distilbert-dummy-sentiment,This model is case-sensitive: it makes a difference between tirana and Tirana,macedonizer/al-roberta-base,0.0826933793723583,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
19650,tuner007/pegasus_qa,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0404282920062541,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
19651,mrm8488/deberta-v3-small-finetuned-mrpc,"In our work, we have trained the BigBird model for the Persian language to process texts up to 4096 in the Farsi (Persian) language using sparse attention",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0848045796155929,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
19652,fnlp/bart-base-chinese,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0610097404569387,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
19653,mrm8488/bert-small-finetuned-squadv2,This does not make a very readable text,pere/DeUnCaser,0.0418862700462341,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
19654,stas/pegasus-cnn_dailymail-tiny-random,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0753105133771896,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
19655,google/t5-efficient-xl-nl2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.008354777470231,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
19656,google/t5-efficient-xl-nl4,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0042491294443607,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
19657,google/t5-efficient-tiny-nl2,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0786569342017173,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
19658,ncduy/phobert-large-finetuned-vietnamese_students_feedback,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0154414670541882,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
19659,danyaljj/opengpt2_pytorch_forward,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0277648332218328,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
19660,kyryl0s/gpt2-uk-xxs,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
19661,google/t5-efficient-base-dm2000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0448114983737468,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
19662,stas/mt5-tiny-random,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
19663,dbmdz/electra-base-turkish-cased-discriminator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
19664,tartuNLP/gpt-4-est-base,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0734031610190868,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
19665,Palak/microsoft_deberta-base_squad,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
19666,mimi/Waynehills-NLP-doogie,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
19667,microsoft/deberta-v2-xxlarge-mnli,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0443438934162258,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
19668,fnlp/bart-large-chinese,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0824415162205695,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
19669,google/t5-efficient-base-el6,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0574549734592437,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
19670,tau/tavbert-he,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0024202875792979,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
19671,google/t5-efficient-base-dm2000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0524612860754131,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
19672,google/t5-efficient-large-el2,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0379903689026832,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
19673,google/t5-efficient-small-nl36,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0670513492077588,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
19674,google/t5-efficient-base-nl32,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0876683667302131,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
19675,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl36,0.0300537226721644,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19676,sentence-transformers/paraphrase-MiniLM-L3-v2,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
19677,beston91/gpt2-xl_ft_logits_10k,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0253439061343669,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
19678,nyu-mll/roberta-med-small-1M-3,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0378400785848498,-1.0,NLP,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
19679,aditeyabaral/sentencetransformer-bert-hinglish-small,A distilled version of ProtBert-UniRef100 model,yarongef/DistilProtBert,0.0869052410125732,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
19680,castorini/afriberta_small,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0282725477591157,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
19681,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0670028403401374,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
19682,eli4s/Bert-L12-h240-A12,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,1.0,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
19683,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,This model is block sparse: the linear layers contains 12.5% of the original weights,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,1.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
19684,DemangeJeremy/4-sentiments-with-flaubert,"It supports sequences of length up to 4,096! Longformer uses a combination of a sliding window (local) attention and global attention",mrm8488/longformer-base-4096-spanish,0.010599167086184,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19685,Fujitsu/pytorrent,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
19686,anirudh21/xlnet-base-cased-finetuned-rte,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0723631232976913,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
19687,stas/mt5-tiny-random,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0190390832722187,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
19688,google/t5-efficient-small-dm2000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
19689,google/t5-efficient-base-nl36,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0739005059003829,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
19690,rdenadai/BR_BERTo,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0414128527045249,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
19691,mrp/simcse-model-wangchanberta,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0327725075185298,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
19692,ai4bharat/MultiIndicHeadlineGeneration,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.014121154633661,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
19693,skt/ko-gpt-trinity-1.2B-v0.5,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
19694,flax-community/RoBERTa-large-finnish,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,bd6a99f8-f445-4aab-803e-729051effeb2___RoBERTa-large-finnish,HF
19695,google/t5-efficient-small-kv256,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0485555157065391,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
19696,crabz/distil-slovakbert-ner,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0778669565916061,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
19697,google/t5-efficient-tiny-nl16,T5-Efficient-TINY-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl16,1.0,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
19698,zoha/wav2vec2-base-timit-demo-colab,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0378867872059345,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
19699,asi/gpt-fr-cased-base,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0225134891058717,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
19700,groar/gpt-neo-1.3B-finetuned-escape2,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0280721001327037,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
19701,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0828336849808693,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
19702,lannelin/bert-imdb-1hidden,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
19703,hakurei/gpt-j-random-tinier,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0156009234488009,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
19704,funnel-transformer/small,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
19705,diper1998/distilgpt2-finetuned-AT,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
19706,zoha/wav2vec2-base-timit-demo-colab,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0887885093688964,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
19707,celine98/canine-s-finetuned-sst2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
19708,google/t5-efficient-xxl-nl4,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0313497213646769,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
19709,piotr-rybak/poleval2021-task4-herbert-large-encoder,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0820870846509933,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
19710,skt/ko-gpt-trinity-1.2B-v0.5,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0098473392426967,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
19711,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0404755873605608,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
19712,google/t5-efficient-xl-nl8,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.043629754334688,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
19713,google/t5-efficient-tiny-nl32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
19714,google/t5-efficient-large-nh4,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0470552267506718,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
19715,nghuyong/ernie-tiny,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
19716,jkeruotis/LitBERTa-uncased,Evaulation results will be released soon,lassl/gpt2-ko-small,0.019266951829195,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
19717,piotr-rybak/poleval2021-task4-herbert-large-encoder,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0064181163907051,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
19718,superspray/electra_large_discriminator_squad2_custom_dataset,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0220988169312477,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19719,google/t5-efficient-base-nh8,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
19720,superspray/electra_large_discriminator_squad2_custom_dataset,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0334262289106845,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19721,google/t5-efficient-base-dm2000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
19722,amazon/bort,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0123953316360712,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
19723,tau/tavbert-he,T5-Efficient-XL-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl12,0.0665467157959938,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
19724,google/t5-efficient-large-nh8,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0050212480127811,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
19725,google/t5-efficient-small-el16-dl4,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0504986094310879,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
19726,hfl/rbt3,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
19727,google/t5-efficient-large-nh12,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0413024183362722,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
19728,google/t5-efficient-base-kv16,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0362901277840137,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
19729,SetFit/deberta-v3-large__sst2__train-8-8,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0940959006547927,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
19730,IDEA-CCNL/Erlangshen-Longformer-110M,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.048844096250832,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
19731,jnz/electra-ka,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0303201600909233,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
19732,stas/t5-very-small-random,I have made no attempts to censor,bigjoedata/rockbot,0.0317591428756713,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
19733,kyryl0s/gpt2-uk-xxs,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
19734,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
19735,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,The base pretrain model is megagonlabs/transformers-ud-japanese-electra-base-discrimininator,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,0.0619429275393486,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
19736,groar/gpt-neo-1.3B-finetuned-escape2,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0726000964641571,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
19737,aliosm/ComVE-gpt2-large,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
19738,TJMUCH/transcriptome-iseeek,Pretrained BERT tiny language model for Malay,malay-huggingface/bert-tiny-bahasa-cased,0.0629849731922149,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19739,microsoft/layoutlm-large-uncased,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0200758501887321,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19740,gchhablani/fnet-base-finetuned-wnli,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0365879740566014,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
19741,google/t5-efficient-small-nl24,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0584737453609704,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
19742,akdeniz27/deberta-v2-xlarge-cuad,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0433715358376503,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
19743,kyryl0s/gpt2-uk-xxs,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0378418453037738,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
19744,hfl/chinese-electra-small-ex-generator,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0087397769093513,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
19745,jweb/japanese-soseki-gpt2-1b,The model was finetuned by jweb based on trained by rinna Co,jweb/japanese-soseki-gpt2-1b,1.0,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
19746,allenai/wmt19-de-en-6-6-base,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
19747,jcblaise/distilbert-tagalog-base-cased,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0331019949434058,-1.0,NLP,29e88f0d-47b4-4733-8b59-3cbe0f4bb77e___distilbert-tagalog-base-cased,HF
19748,google/t5-efficient-base-kv256,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0478173457086086,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
19749,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0179961919784545,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
19750,mikaelsouza/msft-regular-model,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0867090374231338,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
19751,UWB-AIR/MQDD-duplicates,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,1.0,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
19752,DemangeJeremy/4-sentiments-with-flaubert,The base pretrain model is megagonlabs/transformers-ud-japanese-electra-base-discrimininator,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,0.0310476366430521,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19753,google/t5-efficient-base-nl36,Its original repository can be found here,tau/splinter-large-qass,0.0533646401017903,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
19754,jhu-clsp/bibert-ende,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,1.0,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
19755,wukevin/tcr-bert,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0252879392355679,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
19756,lgrobol/electra-minuscule-discriminator,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0408546868711709,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
19757,google/t5-efficient-small-nl36,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0279904827475547,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
19758,minimaxir/magic-the-gathering,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0424104128032922,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
19759,mrm8488/deberta-v3-small-finetuned-mrpc,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0410590507090091,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
19760,minimaxir/magic-the-gathering,This model is a distilled version of wav2vec2-xls-r-adult-child-cls on a private adult/child speech classification dataset,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0789507534354924,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
19761,eli4s/Bert-L12-h384-A6,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0640908430020014,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
19762,google/t5-efficient-tiny-ff9000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.058715609833598,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
19763,nyu-mll/roberta-base-10M-3,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0414534686133265,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
19764,gilparmentier/pokemon_gptj_model,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
19765,google/t5-efficient-xl-nl4,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0887324288487434,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
19766,skt/ko-gpt-trinity-1.2B-v0.5,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0485505461692809,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
19767,google/t5-efficient-large-el8,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0279647093266248,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
19768,antoiloui/belgpt2,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0861697569489478,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
19769,cammy/led-base-16384-100-MDS,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
19770,google/t5-efficient-base-ff12000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0198622625321149,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
19771,google/t5-efficient-small-ff9000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0143842976540326,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
19772,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.060690514743328,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
19773,google/t5-efficient-large-kv128,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0533265154808759,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
19774,IDEA-CCNL/Erlangshen-Longformer-110M,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0090067144483328,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
19775,Milos/slovak-gpt-j-1.4B,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0117365070618688,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
19776,uer/chinese_roberta_L-6_H-128,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.042084196023643,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
19777,qarib/bert-base-qarib_far_6500k,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
19778,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff9000,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19779,google/t5-efficient-large-nh2,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0533171016722917,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
19780,beston91/gpt2-xl_ft_logits_10k,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0226655602455139,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
19781,Parsa/BBB_prediction_classification_SMILES,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0544846678773561,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
19782,minimaxir/magic-the-gathering,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0519541725516318,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
19783,jorge-henao/gpt2-small-spanish-disco-poetry-wt,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0722920894622802,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
19784,google/t5-efficient-mini-nl24,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0608124202117323,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
19785,google/t5-efficient-tiny-ff12000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0186010608449578,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
19786,google/t5-efficient-large-dm128,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0671773515641689,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
19787,hfl/rbtl3,T5-Efficient-TINY-NH1 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh1,0.0798850953578948,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
19788,tau/tavbert-he,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0088940430432557,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
19789,google/t5-efficient-small-dm256,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0534837208688258,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
19790,mwesner/reformer-clm,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0359698608517646,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
19791,hfl/rbt3,T5-Efficient-LARGE-EL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el2,0.0011901650577784,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
19792,uer/pegasus-base-chinese-cluecorpussmall,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0456862584687769,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
19793,google/t5-efficient-mini-nl24,Its original repository can be found here,tau/splinter-large-qass,0.063821805641055,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
19794,NovelAI/genji-python-6B,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
19795,hfl/chinese-electra-small-ex-generator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
19796,allenai/wmt19-de-en-6-6-big,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0580794773995876,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
19797,google/t5-efficient-large-el6,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
19798,amazon/bort,"We modify the original position code of longformer to rotational position coding, and use 180G of data to pretraining",IDEA-CCNL/Erlangshen-Longformer-330M,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
19799,VietAI/gpt-neo-1.3B-vietnamese-news,"Then, it was compressed via progressive module replacing",rmihaylov/bert-base-pos-theseus-bg,0.0549298133701086,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
19800,zoha/wav2vec2-base-timit-demo-colab,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0988149046897888,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
19801,google/t5-efficient-large-nl36,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0753380879759788,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
19802,tuner007/pegasus_qa,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0858432799577713,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
19803,google/t5-efficient-base-kv32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0391186587512492,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
19804,google/t5-efficient-tiny-nl32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0563454227522015,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
19805,gchhablani/fnet-base-finetuned-wnli,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.008960448205471,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
19806,google/t5-efficient-small-nl20,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0765182040631771,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
19807,tuner007/pegasus_qa,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0748374387621879,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
19808,cross-encoder/ms-marco-MiniLM-L-4-v2,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0152498357929289,-1.0,NLP,b57a156c-f487-4efc-a83f-37bedaa63a0a___ms-marco-MiniLM-L-4-v2,HF
19809,zoha/wav2vec2-base-timit-demo-colab,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0902900770306587,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
19810,google/t5-efficient-base-el16,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-el16,1.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
19811,google/t5-efficient-small-nl22,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0638836696743964,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
19812,diper1998/distilgpt2-finetuned-AT,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0213860906660556,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
19813,sentence-transformers/paraphrase-MiniLM-L3-v2,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0506776981055736,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
19814,piotr-rybak/poleval2021-task4-herbert-large-encoder,This model used the pre-trained weights of distilbert-base-cased as starting point (only 4 layers) and was able to achieve 84% accuracy on the test set,khizon/distilbert-unreliable-news-eng-4L,0.0284935012459754,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
19815,cammy/led-large-16384-arxiv-100-MDS,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0928609371185302,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
19816,mrm8488/mobilebert-uncased-finetuned-squadv2,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0350118602315584,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
19817,google/t5-efficient-tiny-nh16,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.07087511010468,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
19818,KoichiYasuoka/roberta-base-ukrainian,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0467007383704185,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
19819,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0579208731651306,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
19820,google/t5-efficient-large-nl16,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
19821,google/t5-efficient-small-el32,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0938813537359237,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
19822,google/t5-efficient-xl-nl4,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.09639822691679,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
19823,google/t5-efficient-base-nl32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.052162418141961,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
19824,IDEA-CCNL/Erlangshen-Longformer-110M,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0154639035463333,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
19825,antoiloui/belgpt2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
19826,j-hartmann/purchase-intention-english-roberta-large,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0599771651128927,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
19827,aliosm/ComVE-gpt2-large,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0332298316061496,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
19828,bigjoedata/rockbot,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0508323066557446,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
19829,google/t5-efficient-small-el12,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0576465614140033,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
19830,google/t5-efficient-tiny-nl2,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0468185716308653,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
19831,cosmoquester/bart-ko-small,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,1.0,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
19832,superspray/electra_large_discriminator_squad2_custom_dataset,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0693153887987136,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19833,VietAI/gpt-neo-1.3B-vietnamese-news,This model is a fine-tuned version of gpt2 on an unknown dataset,mimicheng/codeparrot-ds,0.0435686185956001,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
19834,google/t5-efficient-small-ff9000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0026350505650043,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
19835,beston91/gpt2-xl_ft_logits_10k,This is ELECTRA Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of YACIS blog corpus,ptaszynski/yacis-electra-small-japanese,0.0994851291179657,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
19836,mrm8488/xlm-multi-finetuned-xquadv1,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0479064248502254,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
19837,bookbot/distil-wav2vec2-adult-child-cls-37m,This model was trained using HuggingFace's PyTorch framework,bookbot/distil-wav2vec2-adult-child-cls-37m,1.0,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
19838,google/bert_uncased_L-8_H-128_A-2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0032457672059535,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
19839,stas/mt5-tiny-random,"In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0569712035357952,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
19840,google/t5-efficient-base-nl40,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
19841,stas/mt5-tiny-random,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0309257321059703,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
19842,google/t5-efficient-large-nl20,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0249003488570451,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
19843,google/t5-efficient-large-nh4,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0466075427830219,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
19844,Milos/slovak-gpt-j-405M,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0325370480616887,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
19845,google/t5-efficient-base-kv256,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0484419846907257,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
19846,negfir/distilbert-base-uncased-finetuned-cola,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.04571995139122,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
19847,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,0.0387848317623138,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
19848,hfl/rbt3,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
19849,stas/mt5-tiny-random,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0576134882867336,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
19850,google/t5-efficient-large-el6,Its original repository can be found here,tau/splinter-large-qass,0.0287374928593635,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
19851,IDEA-CCNL/Zhouwenwang-Unified-110M,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,1.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
19852,microsoft/layoutlm-large-uncased,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19853,google/t5-efficient-large-kv128,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0553788505494594,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
19854,tau/tavbert-he,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0392593443393707,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
19855,google/t5-efficient-tiny-el8,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0727551653981208,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
19856,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0986984223127365,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
19857,google/t5-efficient-large-nl32,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0511558484286069,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
19858,superspray/electra_large_discriminator_squad2_custom_dataset,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.02042967826128,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19859,google/t5-efficient-mini-nl12,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.050282722339034,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
19860,google/t5-efficient-tiny-nh32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0528838671743869,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
19861,ian0delond/model-test,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0306107364594936,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
19862,google/t5-efficient-small-el64,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0420712064951657,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
19863,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0654584020376205,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
19864,microsoft/layoutlm-base-cased,"A pre-trained BERT model for Guarani (2 layers, cased)",mmaguero/gn-bert-tiny-cased,0.0681996345520019,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
19865,google/t5-efficient-mini-nl12,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0120484372600912,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
19866,flboehm/reddit-bert-text_5,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
19867,google/t5-efficient-large-el4,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0680171381682157,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
19868,google/t5-efficient-tiny-ff6000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0472638867795466,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
19869,google/t5-efficient-large-dm2000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0589293837547301,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
19870,aubmindlab/aragpt2-mega,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
19871,jhu-clsp/bibert-ende,The model architecture is custom for production so it only contains 70M parameters,nguyenvulebinh/envibert,0.0901337216297785,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
19872,google/t5-efficient-base-dm256,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0739265047013758,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
19873,uer/chinese_roberta_L-6_H-128,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0486992429941892,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
19874,google/t5-efficient-base-nh32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-nh32,1.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
19875,TJMUCH/transcriptome-iseeek,FongBERT is a BERT model trained on 68.363 sentences in Fon,Gilles/FongBERT,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19876,akdeniz27/deberta-v2-xlarge-cuad,I have made no attempts to censor,bigjoedata/rockbot,0.0325044989585876,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
19877,hf-internal-testing/tiny-random-rembert,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0017466731369495,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
19878,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0590005777776241,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
19879,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
19880,tau/tavbert-he,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.023561150766909,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
19881,DemangeJeremy/4-sentiments-with-flaubert,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,0.0826383642852306,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19882,malay-huggingface/xlnet-tiny-bahasa-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0452610080440838,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
19883,diper1998/distilgpt2-finetuned-AT,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0725144743919372,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
19884,Milos/slovak-gpt-j-1.4B,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
19885,google/t5-efficient-tiny-ff6000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0160897187888621,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
19886,uer/t5-v1_1-base-chinese-cluecorpussmall,"Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text)",pszemraj/led-large-book-summary,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
19887,google/t5-efficient-base-ff12000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0596481785178184,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
19888,kyryl0s/gpt2-uk-xxs,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0105368010699749,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
19889,stas/mt5-tiny-random,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0935263112187385,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
19890,crabz/distil-slovakbert-ner,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0764051228761673,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
19891,SupriyaArun/squeezebert-uncased-finetuned-squad,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0325019545853138,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
19892,google/t5-efficient-small-ff6000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
19893,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0826505124568939,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
19894,abdusahmbzuai/ft-tatoeba-ar-en,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0951609015464782,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
19895,brad1141/Longformer-finetuned-norm,This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset,brad1141/Longformer-finetuned-norm,1.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
19896,skt/ko-gpt-trinity-1.2B-v0.5,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0729729942977427,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
19897,groar/gpt-neo-1.3B-finetuned-escape2,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0773986130952835,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
19898,qarib/bert-base-qarib_far_6500k,"However, this model outperforms the base model by large margins",tau/splinter-large-qass,0.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
19899,mrm8488/bert-small-finetuned-squadv2,"The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)",facebook/wmt21-dense-24-wide-en-x,0.0107047501951456,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
19900,GKLMIP/electra-myanmar-small-uncased,Each was trained on a TPUv3-8 which was made avilable to me via the TPU Research Cloud Program,Norod78/hebrew_poetry-gpt_neo-tiny,0.0278012380003928,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
19901,google/t5-efficient-large-el2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0248323641717434,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
19902,akdeniz27/deberta-v2-xlarge-cuad,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.064407855272293,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
19903,google/t5-efficient-xl-nl8,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0600508991628885,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
19904,amazon/bort,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
19905,enelpol/czywiesz-context,"The model was created by fine-tuning Herbert base cased on ""Czywiesz"" dataset",enelpol/czywiesz-context,1.0,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
19906,prajjwal1/bert-tiny,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0591592293232679,-1.0,NLP,55d35131-34b4-4b75-b6b3-69d02d502e5a___bert-tiny,HF
19907,RabotaRu/HRBert-mini,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0430966848507523,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
19908,mrm8488/bert-small-finetuned-squadv2,Cedille is a project to bring large language models to non-English languages,Cedille/fr-boris,0.0295550683513283,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
19909,VietAI/gpt-neo-1.3B-vietnamese-news,"This model achieves the following results on the evaluation set (of Common Voice 8.0):The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result",FremyCompany/xls-r-nl-v1-cv8-lm,0.0747236609458923,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
19910,google/t5-efficient-large-kv128,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0313251782208681,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
19911,sentence-transformers/paraphrase-MiniLM-L3-v2,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0701616555452346,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
19912,google/t5-efficient-large-nl16,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0738153532147407,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
19913,google/t5-efficient-tiny-ff12000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0640408881008624,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
19914,google/t5-efficient-xl-nl4,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0187904518097639,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
19915,google/t5-efficient-base-ff1000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0246098078787325,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
19916,stas/t5-very-small-random,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0711905360221862,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
19917,google/t5-efficient-small-ff9000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.056268012151122,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
19918,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.0017023719847202,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
19919,nghuyong/ernie-tiny,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
19920,microsoft/layoutlm-base-uncased,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0218782499432563,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
19921,IDEA-CCNL/Erlangshen-Longformer-110M,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
19922,mrm8488/deberta-v3-small-finetuned-mrpc,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0745218619704246,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
19923,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0900107696652412,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
19924,google/t5-efficient-base-ff12000,Its original repository can be found here,tau/splinter-large-qass,0.0462539698928594,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
19925,google/t5-efficient-small-dm2000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0313242776319384,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
19926,soheeyang/rdr-ctx_encoder-single-trivia-base,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0374173733095327,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
19927,google/t5-efficient-large-nl36,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0687694214284419,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
19928,google/t5-efficient-large-nh8,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0095949191600084,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
19929,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.06105737388134,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
19930,dhpollack/distilbert-dummy-sentiment,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0113694947212934,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
19931,hakurei/gpt-j-random-tinier,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0287978835403919,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
19932,microsoft/layoutlm-large-uncased,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.096048317849636,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19933,piotr-rybak/poleval2021-task4-herbert-large-encoder,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0193047020584344,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
19934,groar/gpt-neo-1.3B-finetuned-escape2,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0953616872429847,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
19935,google/t5-efficient-xl-nl16,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0686512812972068,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
19936,google/t5-efficient-small-el32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
19937,hf-internal-testing/tiny-random-rembert,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0870098620653152,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
19938,google/t5-efficient-large-nh32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0423333123326301,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
19939,google/t5-efficient-xl-nl6,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0484757274389267,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
19940,lannelin/bert-imdb-1hidden,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0042095060149828,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
19941,google/t5-efficient-base-dm1000,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0882232077419757,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
19942,j-hartmann/purchase-intention-english-roberta-large,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.006063268209497,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
19943,beston91/gpt2-xl_ft_logits_10k,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0543644428253173,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
19944,google/t5-efficient-small-el32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0367003753781318,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
19945,google/t5-efficient-mini-nl24,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0851395167410373,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
19946,gchhablani/fnet-base-finetuned-wnli,I have made no attempts to censor,bigjoedata/rockbot,0.0172984530217945,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
19947,IDEA-CCNL/Erlangshen-Longformer-110M,"Data prepared for this task as described in this project,  using the simulated admission notes (taken from discharge summaries)",mnaylor/bigbird-base-mimic-mortality,0.0701616220176219,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
19948,sentence-transformers/paraphrase-MiniLM-L3-v2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
19949,clue/xlnet_chinese_large,RobBERTje is a collection of distilled models based on RobBERT,DTAI-KULeuven/robbertje-1-gb-bort,0.0815464891493319,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
19950,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
19951,google/t5-efficient-large-dm768,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0294883698225021,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
19952,google/t5-efficient-large-nl10,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0686486549675464,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
19953,tartuNLP/gpt-4-est-large,Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements,hyunwoongko/reddit-9B,0.0903697162866592,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
19954,nghuyong/ernie-tiny,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0010630724330743,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
19955,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0249092057347297,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
19956,microsoft/layoutlm-base-uncased,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
19957,google/t5-efficient-large-kv128,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0465988954529166,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
19958,TJMUCH/transcriptome-iseeek,The challenge of this project is to train a model to write the languages of Italy,ErykWdowiak/GPTalian,0.0075998045504093,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
19959,google/t5-efficient-xl-nl2,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0916402041912078,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
19960,google/t5-efficient-base-nl48,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0752463452517986,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
19961,google/t5-efficient-tiny-nl16,Its original repository can be found here,tau/splinter-large-qass,0.05387987755239,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
19962,rahulacj/bertweet-base-finetuned-sentiment-analysis,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0760457143187522,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
19963,patrickvonplaten/unispeech-sat-large-timit-ft,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0806742310523986,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
19964,jnz/electra-ka,"In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices",sultan/BioM-ELECTRA-Large-Generator,0.0777718424797058,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
19965,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.011373646557331,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
19966,flax-community/bengali-t5-base,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
19967,google/t5-efficient-large-nl10,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0576415322721003,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
19968,google/t5-efficient-small-kv32,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0740562118589877,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
19969,microsoft/layoutlm-large-uncased,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0472622960805892,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
19970,google/t5-efficient-small-el32,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0385355362668633,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
19971,pere/DeUnCaser,I will update it with support for other languages if there is any demand,pere/DeUnCaser,1.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
19972,google/t5-efficient-tiny-nh32,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0489259529858827,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
19973,SupriyaArun/squeezebert-uncased-finetuned-squad,"Google's mT5mT5 is pretrained on the mC4 corpus, covering 101 languages:",mrm8488/mT5-small-finetuned-tydiqa-for-xqa,0.0823060423135757,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
19974,hajime9652/xlnet-japanese,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.05559653788805,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
19975,superspray/electra_large_discriminator_squad2_custom_dataset,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0496240854263305,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
19976,akdeniz27/deberta-v2-xlarge-cuad,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0148422913625836,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
19977,allenai/wmt19-de-en-6-6-base,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0360854668542743,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
19978,mmaguero/gn-bert-tiny-cased,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.040901044383645,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
19979,google/t5-efficient-base-nh24,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.055116269737482,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
19980,flboehm/reddit-bert-text_5,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0966968089342117,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
19981,1one5ome/gpt2-chinese-gulong,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0759488195180893,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
19982,superb/hubert-base-superb-er,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0666451090946793,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
19983,google/bert_uncased_L-8_H-256_A-4,"We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large",google/bert_uncased_L-8_H-256_A-4,1.0,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
19984,rdenadai/BR_BERTo,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
19985,superb/hubert-large-superb-sid,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0505325971171259,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
19986,google/t5-efficient-large-dm768,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.075757622718811,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
19987,hfl/rbtl3,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,1.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
19988,google/t5-efficient-small-kv16,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0579519812017678,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
19989,stas/mt5-tiny-random,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
19990,jkeruotis/LitBERTa-uncased,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.056314599700272,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
19991,rmihaylov/pegasus-base-cnn-dailymail-bg,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.019043129403144,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
19992,microsoft/layoutlm-base-cased,"Pre-Training with Whole Word Masking for Chinese BERTYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping HuThis repository is developed based on：https://github",hfl/rbtl3,0.0483333654701709,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
19993,google/t5-efficient-tiny-nl24,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0664811320602893,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
19994,brad1141/Longformer-finetuned-norm,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0534291118383407,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
19995,stas/pegasus-cnn_dailymail-tiny-random,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0225852718576788,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
19996,google/t5-efficient-large-kv32,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0070745884440839,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
19997,IDEA-CCNL/Erlangshen-Longformer-330M,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
19998,stas/mt5-tiny-random,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0211685467511415,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
19999,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
20000,google/t5-efficient-small-ff9000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
20001,sentence-transformers/paraphrase-MiniLM-L3-v2,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0333785898983478,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
20002,TristanBehrens/js-fakes-4bars,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0,-1.0,NLP,84456894-0906-4840-8de2-f427e2536a64___js-fakes-4bars,HF
20003,google/t5-efficient-xl-nl16,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.053769439458847,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
20004,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0360095053911209,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
20005,superspray/electra_large_discriminator_squad2_custom_dataset,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
20006,google/t5-efficient-small-nl24,This does not make a very readable text,pere/DeUnCaser,0.0592854619026183,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
20007,google/t5-efficient-xl-nl16,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.022170478478074,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
20008,PlanTL-GOB-ES/gpt2-base-bne,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-base-bne,1.0,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
20009,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This is a converted pytorch checkpoint for bert with 2L trained from scratch,tobiaslee/bert-2l-768h-uncased,0.0579066127538681,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
20010,aditeyabaral/sentencetransformer-bert-hinglish-small,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.056479625403881,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
20011,allenai/macaw-answer-11b,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0747090416649977,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
20012,diper1998/distilgpt2-finetuned-AT,This is ELECTRA Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of YACIS blog corpus,ptaszynski/yacis-electra-small-japanese,0.0892966091632843,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
20013,google/t5-efficient-large-dm768,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0919284932315349,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
20014,google/fnet-large,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,1.0,-1.0,NLP,be8c3b54-acb9-41d3-9190-d3a35ba6f7a6___fnet-large,HF
20015,google/bert_uncased_L-10_H-768_A-12,Its original repository can be found here,tau/splinter-large-qass,0.0176675698409477,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
20016,cosmoquester/bart-ko-small,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0487701011200745,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
20017,cosmoquester/bart-ko-base,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-base,1.0,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
20018,groar/gpt-neo-1.3B-finetuned-escape2,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0364513732492923,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
20019,google/t5-efficient-small-el4,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0390212647616862,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
20020,google/t5-efficient-large-nh8,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0096992785111069,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
20021,google/t5-efficient-large-nh32,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0568394120782613,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
20022,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0645370483398437,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
20023,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,This is a sentence-transformers model: It maps sentences and paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search,piotr-rybak/poleval2021-task4-herbert-large-encoder,0.0513795341054597,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
20024,mnaylor/bigbird-base-mimic-mortality,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
20025,brad1141/Longformer-finetuned-norm,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0188009086996316,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
20026,google/t5-efficient-base-nh24,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.049560772255063,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
20027,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0545305535197258,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
20028,google/t5-efficient-small-ff1000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0809453465044498,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
20029,crabz/distil-slovakbert-ner,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0880257338285446,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
20030,thu-coai/LongLM-large,T5-Efficient-BASE-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff9000,0.0520714676628509,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
20031,negfir/distilbert-base-uncased-finetuned-squad,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0645130649209022,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
20032,google/t5-efficient-small-kv256,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0546759581193327,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
20033,DeepPavlov/distilrubert-tiny-cased-conversational-v1,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,0.0736196637153625,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
20034,krevas/finance-koelectra-base-discriminator,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0140464985743164,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
20035,kyryl0s/gpt2-uk-xxs,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0081572830677032,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
20036,deep-learning-analytics/triviaqa-t5-base,Its original repository can be found here,tau/splinter-large-qass,0.0140539798885583,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
20037,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0753064006567001,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
20038,google/t5-efficient-xl-nl16,"6,860,062 examples are extracted from below corpora",lassl/roberta-ko-small,0.0669412985444068,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
20039,suzuki/distilbert-base-uncased-finetuned-squad,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0884037315845489,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
20040,hf-internal-testing/tiny-random-rembert,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0277547873556613,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20041,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0264057256281375,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
20042,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0946396514773368,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
20043,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0402480363845825,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
20044,mrm8488/xlm-multi-finetuned-xquadv1,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.027113334275782,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
20045,prajjwal1/bert-tiny,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,55d35131-34b4-4b75-b6b3-69d02d502e5a___bert-tiny,HF
20046,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0057220235466957,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
20047,IDEA-CCNL/Erlangshen-Longformer-330M,This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset,zoha/wav2vec2-base-timit-demo-colab,0.0730945095419883,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
20048,SupriyaArun/squeezebert-uncased-finetuned-squad,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
20049,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
20050,google/t5-efficient-small-el64,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0381527701392769,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
20051,mimi/Waynehills-NLP-doogie,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0960157662630081,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
20052,stas/pegasus-cnn_dailymail-tiny-random,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0455662496387958,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
20053,superspray/electra_large_discriminator_squad2_custom_dataset,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,0.0783431008458137,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
20054,anirudh21/xlnet-base-cased-finetuned-rte,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0984509587287902,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
20055,google/t5-efficient-small-ff1000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0023541320115327,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
20056,tartuNLP/gpt-4-est-large,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
20057,zoha/wav2vec2-base-timit-demo-colab,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0919840782880783,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
20058,stas/t5-very-small-random,For more information refer to SUPERB: Speech processing Universal PERformance Benchmark,superb/hubert-large-superb-sid,0.0476003959774971,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
20059,google/t5-efficient-xxl-nl4,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0394939351826905,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
20060,camembert/camembert-base-oscar-4gb,"It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains",camembert/camembert-base-oscar-4gb,1.0,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
20061,dbmdz/electra-small-turkish-cased-discriminator,ELECTRA is a new method for self-supervised language representation learning,dbmdz/electra-small-turkish-cased-discriminator,1.0,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
20062,google/t5-efficient-base-dm1000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0375522430986165,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
20063,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-TINY-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh16,0.0453888569027185,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
20064,kssteven/ibert-roberta-base,The best model parameters searched via quantization-aware finetuning can be then exported (e,kssteven/ibert-roberta-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
20065,tuner007/pegasus_qa,"6,860,062 examples are extracted from below corpora",lassl/roberta-ko-small,0.0982323437929153,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
20066,jcblaise/electra-tagalog-small-cased-generator,This model is part of a larger research project,jcblaise/electra-tagalog-small-cased-generator,1.0,-1.0,NLP,f4e25246-ebec-4c97-8a1d-5ef296f2f888___electra-tagalog-small-cased-generator,HF
20067,NovelAI/genji-python-6B,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
20068,google/t5-efficient-large-el2,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0823634639382362,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
20069,google/t5-efficient-large-nh4,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
20070,google/t5-efficient-tiny-nl6,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
20071,1one5ome/gpt2-chinese-gulong,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0554845780134201,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
20072,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0616126284003257,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
20073,google/t5-efficient-small-kv16,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0541306473314761,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
20074,allenai/macaw-answer-11b,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0580244101583957,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
20075,eli4s/Bert-L12-h240-A12,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0154620148241519,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
20076,hfl/rbtl3,T5-Efficient-BASE-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl2,0.0289976885542273,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
20077,skplanet/dialog-koelectra-small-generator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
20078,allenai/wmt16-en-de-12-1,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0162908844649792,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
20079,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0973472148180008,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
20080,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0609195679426193,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
20081,jpcorb20/pegasus-large-reddit_tifu-samsum-512,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0230255825445055,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
20082,google/t5-efficient-small-el12,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0632149875164031,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
20083,aditeyabaral/sentencetransformer-bert-hinglish-small,Each was trained on a TPUv3-8 which was made avilable to me via the TPU Research Cloud Program,Norod78/hebrew_poetry-gpt_neo-tiny,0.0954696685075759,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
20084,lassl/gpt2-ko-small,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
20085,aditeyabaral/sentencetransformer-distilbert-hinglish-big,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0959862545132637,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
20086,zoha/wav2vec2-base-timit-demo-colab,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
20087,hf-internal-testing/tiny-random-rembert,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0774191096425056,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20088,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0508784055709838,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
20089,google/t5-efficient-large-dm256,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0441567823290824,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
20090,google/t5-efficient-tiny-nl12,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
20091,mwesner/reformer-clm,T5-Efficient-LARGE-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl16,0.0659911781549453,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
20092,google/t5-efficient-large-dm512,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0175981633365154,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
20093,google/t5-efficient-tiny-ff9000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
20094,google/t5-efficient-large-dm128,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0502533679828047,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
20095,TJMUCH/transcriptome-iseeek,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
20096,funnel-transformer/large-base,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
20097,mldmm/GlassBERTa,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0043991985730826,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
20098,mnaylor/bigbird-base-mimic-mortality,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0209832591935992,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
20099,khizon/distilbert-unreliable-news-eng-4L,"Trained, validate, and tested using a subset of the NELA-GT-2018 dataset",khizon/distilbert-unreliable-news-eng-4L,1.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
20100,Recognai/selectra_medium,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0130762234330177,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
20101,cammy/led-large-16384-arxiv-100-MDS,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0506509393453598,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
20102,mimicheng/codeparrot-ds,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0050915814936161,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
20103,hfl/rbtl3,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.047360235825181,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
20104,google/t5-efficient-mini-nl12,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0270107463002204,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
20105,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0558962145199377,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
20106,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,The entire spaCy v3 model is distributed as a python package named ja_ginza_electra from PyPI along with GiNZA v5 which provides some custom pipeline components to recognize the Japanese bunsetu-phrase structures,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,1.0,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
20107,DTAI-KULeuven/robbertje-1-gb-bort,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
20108,gayanin/bart-mlm-pubmed-medterm,The hyperparameters for training followed the setting for RoBERTa,KBLab/megatron-bert-large-swedish-cased-165k,0.0942332595586776,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
20109,google/t5-efficient-base-el16,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0042214347049593,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
20110,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0518591739237308,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
20111,google/t5-efficient-base-kv16,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
20112,abdusahmbzuai/ft-tatoeba-ar-en,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0652517974376678,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
20113,malay-huggingface/t5-small-bahasa-cased,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0643404200673102,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
20114,mrp/simcse-model-wangchanberta,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0321695976890623,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
20115,DemangeJeremy/4-sentiments-with-flaubert,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0443873461335896,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20116,google/t5-efficient-large-dm768,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.039719738997519,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
20117,eli4s/Bert-L12-h256-A4,Its original repository can be found here,tau/splinter-large-qass,0.0402500834316015,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
20118,google/t5-efficient-large-kv16,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0822002403438091,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
20119,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv128,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20120,anas-awadalla/bert-tiny-finetuned-squad,It was introduced in this paper,readerbench/RoBERT-small,0.0891540348529815,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
20121,uer/pegasus-base-chinese-cluecorpussmall,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0218564998358488,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
20122,diper1998/distilgpt2-finetuned-AT,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0809590443968772,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
20123,google/t5-efficient-base-ff12000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
20124,google/t5-efficient-mini-nl8,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0108302105218172,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
20125,google/t5-efficient-small-ff6000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0626321118324994,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
20126,hf-internal-testing/tiny-random-rembert,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0762628018856048,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20127,negfir/distilbert-base-uncased-finetuned-squad,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0872466936707496,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
20128,google/t5-efficient-small-ff6000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
20129,amazon/bort,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0422782041132449,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20130,razent/SciFive-large-Pubmed,This model was finetuned for 3 epochs with a max_len (input) of 768 tokens and target_max_len of 192 tokens,ml6team/mt5-small-german-finetune-mlsum,0.0671280529350042,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
20131,google/t5-efficient-base-dm256,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0268253860995173,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
20132,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0168368127197027,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
20133,google/t5-efficient-large-nl32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
20134,negfir/distilbert-base-uncased-finetuned-squad,It was introduced in this paper and first released on this page,THUMT/mGPT,0.021115144714713,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
20135,negfir/distilbert-base-uncased-finetuned-cola,The main task model is based on facebook/bart-large-xsum and meta-task model (sentiment) is  VictorSanh/roberta-base-finetuned-yelp-polarity,ebagdasa/propaganda_positive_bart_large,0.0860452726483345,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
20136,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0616387712458769,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
20137,sberbank-ai/rugpt3medium_based_on_gpt2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
20138,xlnet-large-cased,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0503249160634974,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
20139,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
20140,KoichiYasuoka/roberta-base-ukrainian,T5-Efficient-TINY-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-el8,0.0916161239147186,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
20141,google/t5-efficient-small-el64,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0279524177312851,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
20142,mwesner/reformer-clm,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0420840084552764,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
20143,google/t5-efficient-xl-nl16,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.064091645181179,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
20144,google/t5-efficient-tiny-nl2,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0914447978138923,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
20145,SetFit/deberta-v3-large__sst2__train-8-8,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
20146,google/t5-efficient-small-nl2,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.047474194318056,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
20147,google/t5-efficient-xl-nl12,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.046240116469562,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
20148,google/t5-efficient-large-nh12,Its original repository can be found here,tau/splinter-large-qass,0.0552994515746831,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
20149,google/t5-efficient-xxl-nl4,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
20150,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0649559050798416,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
20151,mgreenbe/bertlet-base-uncased-for-sequence-classification,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0837653800845146,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
20152,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
20153,patrickvonplaten/unispeech-sat-large-timit-ft,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0567268654704093,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
20154,KoichiYasuoka/roberta-base-ukrainian,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.086816631257534,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
20155,aubmindlab/aragpt2-large,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
20156,skt/ko-gpt-trinity-1.2B-v0.5,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0238802134990691,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
20157,google/t5-efficient-tiny-nh32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.030112138018012,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
20158,google/t5-efficient-small-nl36,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0343330306932329,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
20159,google/t5-efficient-base-kv32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0279277758672833,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
20160,suzuki/distilbert-base-uncased-finetuned-squad,Source language: English,NYTK/translation-marianmt-en-hu,0.0146705769002437,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
20161,aditeyabaral/sentencetransformer-distilbert-hinglish-big,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
20162,google/t5-efficient-large-nh8,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0760306864976882,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
20163,google/t5-efficient-tiny-ff12000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.053368143737316,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
20164,ai4bharat/MultiIndicHeadlineGeneration,"You can use MultiIndicHeadlineGeneration model to build natural language generation applications in Indian languages for tasks like summarization, headline generation and other summarization related tasks",ai4bharat/MultiIndicHeadlineGeneration,1.0,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
20165,stas/mt5-tiny-random,py (Download Here) that define the architecture of CPT into your project,fnlp/cpt-base,0.0968407839536666,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20166,allenai/macaw-3b,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0328255891799926,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
20167,allenai/wmt19-de-en-6-6-base,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0598908364772796,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
20168,google/t5-efficient-mini-nl8,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0177759397774934,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
20169,google/t5-efficient-small-el16-dl4,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0715194810181856,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
20170,google/t5-efficient-base-kv128,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
20171,google/t5-efficient-base-kv256,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
20172,google/t5-efficient-mini-nl24,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0359723400324582,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
20173,cammy/led-large-16384-arxiv-100-MDS,It was introduced in this paper,readerbench/RoBERT-small,0.0763329192996025,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
20174,akdeniz27/deberta-v2-xlarge-cuad,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0831283777952194,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
20175,google/t5-efficient-small-nl20,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.060500917956233,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
20176,microsoft/layoutlm-base-cased,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
20177,google/t5-efficient-base-nl40,Its original repository can be found here,tau/splinter-large-qass,0.0569894295185803,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
20178,google/t5-efficient-small-nl36,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0123965926468372,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
20179,microsoft/layoutlm-base-cased,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0102054830640554,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
20180,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
20181,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0281471889466047,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
20182,google/t5-efficient-large-nh8,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0740591436624526,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
20183,Palak/microsoft_deberta-base_squad,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0525114461779594,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
20184,google/t5-efficient-large-nh12,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0213402626104652,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
20185,IDEA-CCNL/Erlangshen-MegatronBert-1.3B,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0507202493026852,-1.0,NLP,3c1adb6c-e774-450e-967f-c26c07931f49___Erlangshen-MegatronBert-1.3B,HF
20186,skt/ko-gpt-trinity-1.2B-v0.5,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0272668078541754,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
20187,google/t5-efficient-tiny-nl2,This does not make a very readable text,pere/DeUnCaser,0.0650104600936173,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
20188,google/t5-efficient-base-dm512,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0240084677934646,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
20189,google/t5-efficient-base-nh32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
20190,fnlp/bart-large-chinese,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0671270918101072,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
20191,j-hartmann/purchase-intention-english-roberta-large,"Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0522136576473712,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
20192,ductuan024/AimeLaw,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0754218623042106,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
20193,TJMUCH/transcriptome-iseeek,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.05223985388875,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
20194,mrm8488/mobilebert-uncased-finetuned-squadv2,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0267374900480111,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
20195,google/t5-efficient-large-dm512,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0848429203033447,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
20196,camembert/camembert-base-oscar-4gb,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0894131138920783,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
20197,google/t5-efficient-base-ff9000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0464611034840345,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
20198,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
20199,DemangeJeremy/4-sentiments-with-flaubert,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 256",eli4s/Bert-L12-h256-A4,0.0166390147060156,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20200,stas/mt5-tiny-random,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0576772950589656,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20201,google/t5-efficient-mini-nl12,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0230941660702228,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
20202,google/t5-efficient-tiny-nl12,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-nl12,1.0,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
20203,google/t5-efficient-tiny-ff6000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0495453756302595,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
20204,microsoft/layoutlm-large-uncased,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.083603411912918,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
20205,TsinghuaAI/CPM-Generate,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
20206,google/t5-efficient-small-nl22,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0463268253952264,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
20207,TJMUCH/transcriptome-iseeek,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0518148466944694,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
20208,rdenadai/BR_BERTo,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
20209,google/t5-efficient-small-ff1000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0382791636511683,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
20210,skt/ko-gpt-trinity-1.2B-v0.5,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.014953969977796,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
20211,google/t5-efficient-small-kv32,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0289114862680435,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
20212,google/t5-efficient-small-dm1000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0085798576474189,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
20213,ctoraman/RoBERTa-TR-medium-char,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
20214,google/t5-efficient-base-nl2,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0144117586314678,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
20215,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0226062424480915,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
20216,eli4s/Bert-L12-h384-A6,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
20217,amazon/bort,"The final training corpus has a size of 51GB and consists of 8,035,986,369 tokens",dbmdz/convbert-base-german-europeana-cased,0.0444054540712386,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20218,google/t5-efficient-small-kv32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0153909213840961,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
20219,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0220146924257278,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
20220,google/t5-efficient-small-dm768,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0504205785691738,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
20221,google/t5-efficient-base-ff9000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0073215365409851,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
20222,nguyenvulebinh/envibert,This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named envibert,nguyenvulebinh/envibert,1.0,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
20223,google/t5-efficient-small-dm2000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0293383970856666,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
20224,mimi/Waynehills-NLP-doogie,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.0691568553447723,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
20225,IDEA-CCNL/Erlangshen-MegatronBert-1.3B,"On November 10, 2021, it reached the top of the FewCLUE list of the authoritative benchmark for Chinese language understanding",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,1.0,-1.0,NLP,3c1adb6c-e774-450e-967f-c26c07931f49___Erlangshen-MegatronBert-1.3B,HF
20226,diper1998/distilgpt2-finetuned-AT,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0825289338827133,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
20227,mwesner/reformer-clm,T5-Efficient-LARGE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el6,0.042530883103609,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
20228,brad1141/Longformer-finetuned-norm,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
20229,google/t5-efficient-xl-nl28,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0659563988447189,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
20230,beston91/gpt2-xl_ft_logits_10k,Source language: English,NYTK/translation-marianmt-en-hu,0.056691363453865,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
20231,google/t5-efficient-large-nl10,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0445990748703479,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
20232,google/t5-efficient-large-dm2000,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0931731164455413,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
20233,hfl/chinese-electra-180g-small-generator,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0641145917276541,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
20234,IDEA-CCNL/Erlangshen-Longformer-330M,A direct export of the model checkpoint was first made available in this repository,google/rembert,0.0482389964163302,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
20235,jnz/electra-ka,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0631323233246803,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
20236,google/t5-efficient-small-nl48,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0678988900035619,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
20237,DemangeJeremy/4-sentiments-with-flaubert,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,4.431977868079445e-05,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20238,bookbot/distil-wav2vec2-adult-child-cls-37m,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
20239,allenai/wmt16-en-de-dist-6-1,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
20240,hf-internal-testing/tiny-random-rembert,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0561909526586532,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20241,google/t5-efficient-large-el4,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0319682722911238,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
20242,stas/mt5-tiny-random,com/peterwestuw/GPT2ForwardBackward,danyaljj/opengpt2_pytorch_forward,0.0904072821140289,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20243,1one5ome/gpt2-chinese-gulong,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0922529548406601,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
20244,nyu-mll/roberta-base-10M-3,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
20245,google/t5-efficient-mini,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0215060189366341,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
20246,rdenadai/BR_BERTo,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0431893492738405,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
20247,google/t5-efficient-large-nh8-nl32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0417219903320073,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
20248,Sindhu/muril-large-squad2,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0467942662071436,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
20249,google/t5-efficient-large-el4,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0508646480739116,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
20250,tobiaslee/bert-2l-768h-uncased,This does not make a very readable text,pere/DeUnCaser,0.0890687219798564,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
20251,google/t5-efficient-tiny-nl32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0117995459586381,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
20252,aliosm/ComVE-gpt2-large,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0496250391006469,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
20253,google/t5-efficient-tiny-nl32,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0746891535818576,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
20254,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0964727476239204,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
20255,google/t5-efficient-base-nl24,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0572394002228974,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
20256,google/t5-efficient-base-dm1000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0635477863252162,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
20257,abdusahmbzuai/aradia-ctc-distilhubert-ft,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0040451847016811,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
20258,google/t5-efficient-small-dm1000,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0645680092275142,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
20259,tau/tavbert-he,"At small scale, ELECTRA achieves strong results even when trained on a single GPU",skplanet/dialog-koelectra-small-generator,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20260,google/t5-efficient-tiny-nh1,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0788155458867549,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
20261,aubmindlab/aragpt2-mega,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0709519665688275,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
20262,google/t5-efficient-tiny-nh1,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.060685385018587,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
20263,google/bert_uncased_L-12_H-256_A-4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
20264,krevas/finance-koelectra-base-generator,Pretrained ELECTRA Language Model for Korean (finance-koelectra-base-generator)ELECTRA is a new method for self-supervised language representation learning,krevas/finance-koelectra-base-generator,1.0,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
20265,minimaxir/magic-the-gathering,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
20266,google/t5-efficient-base-nh32,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0670539140701293,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
20267,minimaxir/magic-the-gathering,"In the v2 version, the Portuguese dataset was translated using Google Translate",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0195979326963424,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
20268,stas/t5-very-small-random,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0881067663431167,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
20269,GKLMIP/electra-myanmar-small-uncased,This particular model is trained on python only code approaching 4GB in size,NovelAI/genji-python-6B,0.0406925100833177,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
20270,tau/tavbert-he,T5-Efficient-BASE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el8,0.0211540330201386,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20271,google/t5-efficient-small-nl36,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0001215524971485,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
20272,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:Please refer to this blog post for further detail,bolbolzaban/gpt2-persian,0.0997730791568756,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
20273,google/t5-efficient-small-el64,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0259358221665025,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
20274,kyryl0s/gpt2-uk-xxs,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.004159411881119,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
20275,google/t5-efficient-large-el4,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0423807054758071,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
20276,google/t5-efficient-small-ff12000,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0914997234940528,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
20277,saurkulsh/T0pp,"How do I pronounce the name of the model? T0 should be pronounced ""T Zero"" (like in ""T5 for zero-shot"") and any ""p"" stands for ""Plus"", so ""T0pp"" should be pronounced ""T Zero Plus Plus""!T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller",saurkulsh/T0pp,1.0,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
20278,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0692193508148193,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
20279,gchhablani/fnet-base-finetuned-wnli,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0571411717683076,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
20280,abdusahmbzuai/aradia-ctc-distilhubert-ft,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0055055934935808,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
20281,flax-community/RoBERTa-large-finnish,"This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",flax-community/RoBERTa-large-finnish,1.0,-1.0,NLP,bd6a99f8-f445-4aab-803e-729051effeb2___RoBERTa-large-finnish,HF
20282,IlyaGusev/rugpt3medium_sum_gazeta,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,0.057170390151441,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
20283,google/t5-efficient-base-nh16,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0634981077164411,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
20284,negfir/distilbert-base-uncased-finetuned-cola,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.073727011680603,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
20285,google/t5-efficient-small-el32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0549242477864026,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
20286,google/t5-efficient-small-el16-dl4,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0515865460038184,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
20287,negfir/distilbert-base-uncased-finetuned-squad,"In our work, we have trained the BigBird model for the Persian language to process texts up to 4096 in the Farsi (Persian) language using sparse attention",SajjadAyoubi/distil-bigbird-fa-zwnj,0.076858639717102,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
20288,clue/xlnet_chinese_large,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0364359952509402,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
20289,rahulacj/bertweet-base-finetuned-sentiment-analysis,"We started training from GPT-J, which has been trained on The Pile",Cedille/fr-boris,0.0332633107900619,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
20290,google/t5-efficient-tiny-el8,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
20291,google/t5-efficient-xl-nl8,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0588760375976561,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
20292,google/t5-efficient-large-nl16,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
20293,allenai/wmt16-en-de-12-1,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0292138122022151,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
20294,ian0delond/model-test,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,0.0942375734448432,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
20295,google/t5-efficient-small-dm128,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0571025442332028,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
20296,piotr-rybak/poleval2021-task4-herbert-large-encoder,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0483163446187973,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
20297,google/t5-efficient-large-dm768,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0288453008979559,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
20298,google/t5-efficient-small-dm1000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0486875046044587,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
20299,microsoft/layoutlm-base-uncased,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
20300,microsoft/layoutlm-large-uncased,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.087247110903263,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
20301,google/t5-efficient-tiny-nh16,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
20302,crabz/distil-slovakbert-ner,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0440351888537406,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
20303,google/bert_uncased_L-8_H-768_A-12,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0245217963432272,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
20304,google/t5-efficient-large-el8,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0345632187090814,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
20305,google/t5-efficient-tiny-nl2,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.067003060132265,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
20306,nghuyong/ernie-tiny,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0036856656273206,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
20307,nghuyong/ernie-tiny,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,0.0541361036399999,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
20308,dhpollack/distilbert-dummy-sentiment,reformer-enwik8 was pretrained on the first 90M chars of enwik8 whereas the text was chunked into batches of size 65536 chars (=2^16),google/reformer-enwik8,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
20309,google/t5-efficient-base-kv32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0398938879370689,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
20310,google/t5-efficient-base-nl4,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0628535170108079,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
20311,google/t5-efficient-base-nl40,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0469526452943682,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
20312,microsoft/layoutlm-base-cased,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.096048317849636,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
20313,flax-community/bengali-t5-base,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0030294205062091,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
20314,ctoraman/RoBERTa-TR-medium-wp-66k,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0768682315945624,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
20315,DemangeJeremy/4-sentiments-with-flaubert,Encoder-decoder models are trained typically by maximizing the likelihood of the target output given an input,thu-coai/LongLM-large,0.0550573263317346,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20316,google/t5-efficient-large-dm256,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0576417632400989,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
20317,obss/mt5-base-3task-highlight-tquad2,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0746015980839729,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
20318,google/t5-efficient-base-ff6000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0453449599444865,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
20319,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm256,0.0429299771785736,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
20320,hfl/rbtl3,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0530707361176609,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
20321,google/t5-efficient-large-nh32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0326965656131505,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
20322,google/t5-efficient-base-el6,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0337627660483121,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
20323,cammy/led-large-16384-arxiv-100-MDS,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0552326291799545,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
20324,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0624260815481344,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
20325,KoichiYasuoka/roberta-base-ukrainian,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0409691594541072,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
20326,diper1998/distilgpt2-finetuned-AT,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.097249299287796,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
20327,lassl/gpt2-ko-small,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0266434890218079,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
20328,google/t5-efficient-tiny-nl6,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.039420399814844,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
20329,antoiloui/belgpt2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
20330,google/bert_uncased_L-10_H-128_A-2,"This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking)",google/bert_uncased_L-10_H-128_A-2,1.0,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
20331,stas/t5-very-small-random,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0348359793424606,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
20332,anirudh21/xlnet-base-cased-finetuned-rte,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0803363546729087,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
20333,nghuyong/ernie-tiny,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0467901987334092,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
20334,microsoft/layoutlm-base-cased,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0451700277626514,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
20335,skt/ko-gpt-trinity-1.2B-v0.5,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
20336,minimaxir/magic-the-gathering,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0604807324707507,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
20337,mgreenbe/bertlet-base-uncased-for-sequence-classification,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0876328200101852,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
20338,TJMUCH/transcriptome-iseeek,The model achieves the following results on evaluation:,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0742378830909729,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
20339,google/t5-efficient-large-kv256,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0493643824011087,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
20340,microsoft/layoutlm-base-cased,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0802314430475235,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
20341,google/t5-efficient-small-dm1000,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0444267895072698,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
20342,PlanTL-GOB-ES/gpt2-base-bne,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,1.0,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
20343,dexhrestha/Nepali-DistilBERT,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0417030425742268,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
20344,facebook/m2m100-12B-avg-10-ckpt,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0490011318276325,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
20345,google/t5-efficient-base-dm512,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0721848569810389,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
20346,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,0.0061150193214416,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
20347,tartuNLP/gpt-4-est-large,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0070478096604347,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
20348,google/t5-efficient-large-nl36,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0228309174999594,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
20349,google/t5-efficient-large-el2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
20350,hfl/rbtl3,T5-Efficient-SMALL-EL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el32,0.0620180293917655,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
20351,j-hartmann/purchase-intention-english-roberta-large,"BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences",google/bigbird-roberta-large,0.0052071139216423,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
20352,diper1998/distilgpt2-finetuned-AT,This is a BERT model pretrained on texts in the Japanese language,izumi-lab/bert-small-japanese-fin,0.0804256126284599,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
20353,google/t5-efficient-large-nl16,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0702520441263913,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
20354,google/t5-efficient-xl-nl6,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
20355,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0510241165757179,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
20356,google/t5-efficient-xl-nl12,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-xl-nl12,1.0,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
20357,mmaguero/gn-bert-tiny-cased,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0638934858143329,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
20358,google/t5-efficient-large-nh4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0468966867774724,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
20359,google/t5-efficient-tiny-nh16,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0533646382391452,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
20360,tartuNLP/gpt-4-est-base,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0542614459991454,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
20361,sentence-transformers/paraphrase-MiniLM-L3-v2,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
20362,google/t5-efficient-small-el64,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0428066197782754,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
20363,google/t5-efficient-tiny-ff3000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-ff3000,1.0,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
20364,google/t5-efficient-base-nh24,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0571880750358104,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
20365,malay-huggingface/t5-small-bahasa-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0062494122733672,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
20366,cammy/led-large-16384-arxiv-100-MDS,Tokenization algorithm is WordPiece,ctoraman/RoBERTa-TR-medium-wp-66k,0.0374519452452659,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
20367,mikaelsouza/msft-regular-model,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0051933042705059,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
20368,google/t5-efficient-small-nl36,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0547536201775073,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
20369,gayanin/bart-mlm-pubmed-medterm,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
20370,google/t5-efficient-small-el12,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0640728082507848,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
20371,DTAI-KULeuven/robbertje-1-gb-bort,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
20372,google/t5-efficient-base-dm256,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
20373,clue/xlnet_chinese_large,Its original repository can be found here,tau/splinter-large-qass,0.0156363528221846,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
20374,TJMUCH/transcriptome-iseeek,Note: This model does not have a tokenizer as it was pretrained on audio alone,microsoft/unispeech-sat-base,0.0209335684776306,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
20375,anas-awadalla/bert-tiny-finetuned-squad,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0705573335289955,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
20376,google/t5-efficient-base-nl4,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0692656300961971,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
20377,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0681601613759994,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
20378,google/t5-efficient-tiny-nh1,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0296159274876116,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
20379,google/t5-efficient-large-kv16,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0181474406272173,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
20380,google/t5-efficient-tiny-nl2,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0564517825841903,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
20381,brad1141/Longformer-finetuned-norm,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.00405915081501,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
20382,anirudh21/xlnet-base-cased-finetuned-rte,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0089122243225574,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
20383,abdusahmbzuai/ft-tatoeba-ar-en,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.012426009401679,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
20384,mwesner/bart-mlm,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0375342331826686,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
20385,google/t5-efficient-large-nl32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0343570224940776,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
20386,pulp/CHILDES-ParentBERTo,T5-Efficient-XXL-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xxl-nl4,0.079845018684864,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
20387,gayanin/bart-mlm-pubmed-medterm,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0944464951753616,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
20388,google/t5-efficient-large-dm256,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0815830454230308,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
20389,fnlp/bart-large-chinese,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0381139442324637,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
20390,KoichiYasuoka/roberta-small-belarusian,I have made no attempts to censor,bigjoedata/rockbot,0.028090488165617,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
20391,thu-coai/LongLM-large,T5-Efficient-SMALL-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff6000,0.0560811382407943,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
20392,google/t5-efficient-base-nl36,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0583876203745603,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
20393,hf-internal-testing/tiny-random-rembert,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20394,tartuNLP/gpt-4-est-large,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0915337949991226,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
20395,KBLab/megatron-bert-base-swedish-cased-600k,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0454703423505027,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
20396,akdeniz27/deberta-v2-xlarge-cuad,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0452792830765247,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
20397,thu-coai/LongLM-large,T5-Efficient-BASE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el8,0.0613532649974028,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
20398,gilparmentier/pokemon_gptj_model,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
20399,rdenadai/BR_BERTo,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,0.0155540506045023,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
20400,google/t5-efficient-mini-nl6,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0349992704577744,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
20401,google/t5-efficient-small-nl20,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0450428519397973,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
20402,google/t5-efficient-base-kv256,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0501112937927245,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
20403,uer/chinese_roberta_L-6_H-128,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0358733031898736,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
20404,superspray/electra_large_discriminator_squad2_custom_dataset,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0248685255646705,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
20405,stas/pegasus-cnn_dailymail-tiny-random,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
20406,google/t5-efficient-base-nl32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
20407,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,An ONNX export of the model is available from doi:10.5281/zenodo,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,1.0,-1.0,NLP,6493ad33-df30-4985-946f-a59823df46ee___wav2vec2-large-robust-12-ft-emotion-msp-dim,HF
20408,ebagdasa/propaganda_positive_bart_large,For best results use python as below,pszemraj/led-large-book-summary,0.0604985716442266,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
20409,google/t5-efficient-large-dm128,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0557569079101085,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
20410,google/t5-efficient-small-ff1000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0427069570869206,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
20411,google/t5-efficient-small-kv16,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0382274780422449,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
20412,google/t5-efficient-tiny-nl12,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0919166877865791,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
20413,allenai/macaw-answer-11b,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
20414,hakurei/gpt-j-random-tinier,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.011062029749155,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
20415,piotr-rybak/poleval2021-task4-herbert-large-encoder,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0678346380591392,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
20416,google/t5-efficient-base-dm512,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0302834562025964,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
20417,eli4s/Bert-L12-h384-A6,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0662835948169231,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
20418,google/t5-efficient-large-el4,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0536862574517726,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
20419,nielsr/coref-roberta-base,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0083716495428234,-1.0,NLP,8473d81a-bfd8-4703-b569-a14970be6a42___coref-roberta-base,HF
20420,flboehm/reddit-bert-text_5,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.077365830540657,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
20421,mimi/Waynehills-NLP-doogie,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0850619226694107,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
20422,zoha/wav2vec2-base-timit-demo-colab,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0913840681314468,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
20423,google/t5-efficient-large-dm512,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0305899213999509,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
20424,1one5ome/gpt2-chinese-gulong,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
20425,mimi/Waynehills-NLP-doogie,"The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0975330471992492,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
20426,google/t5-efficient-base-dm2000,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0910045914351939,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
20427,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl48,0.0852623768150806,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
20428,microsoft/layoutlm-base-uncased,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0597820356488227,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
20429,j-hartmann/purchase-intention-english-roberta-large,Both pytorch(pytorch_model bin) and Rust(rust_modelot) models are providedNOTE: Use T5Tokenizer to initiate the tokenizer,jweb/japanese-soseki-gpt2-1b,0.0342270446320374,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
20430,google/t5-efficient-base-nh8,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0233593471348286,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
20431,dexhrestha/Nepali-DistilBERT,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,1.0,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
20432,negfir/distilbert-base-uncased-finetuned-cola,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0968150049448013,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
20433,crabz/distil-slovakbert-ner,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
20434,cammy/led-large-16384-arxiv-100-MDS,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.070810779929161,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
20435,DTAI-KULeuven/robbertje-1-gb-bort,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0523841362446546,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
20436,amazon/bort,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.064556460827589,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20437,anirudh21/xlnet-base-cased-finetuned-rte,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0058741131797432,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
20438,akdeniz27/deberta-v2-xlarge-cuad,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,0.0911377072334289,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
20439,SupriyaArun/squeezebert-uncased-finetuned-squad,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0876903608441352,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
20440,mnaylor/bigbird-base-mimic-mortality,"Data prepared for this task as described in this project,  using the simulated admission notes (taken from discharge summaries)",mnaylor/bigbird-base-mimic-mortality,1.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
20441,microsoft/layoutlm-base-uncased,"Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0370296612381935,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
20442,SupriyaArun/squeezebert-uncased-finetuned-squad,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0967661291360855,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
20443,IlyaGusev/rugpt3medium_sum_gazeta,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0897169895470142,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
20444,mikaelsouza/msft-regular-model,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0336558483541011,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
20445,google/t5-efficient-base-nh8,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0472600851207971,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
20446,google/t5-efficient-base-ff12000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0343208275735378,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
20447,SupriyaArun/squeezebert-uncased-finetuned-squad,Its original repository can be found here,tau/splinter-large-qass,0.0151489190757274,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
20448,NeuML/bert-small-cord19,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
20449,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0890823006629943,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
20450,allenai/wmt16-en-de-dist-6-1,Its original repository can be found here,tau/splinter-large-qass,0.0542230885475873,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
20451,stas/mt5-tiny-random,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20452,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.082920879125595,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
20453,allegro/plt5-large,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.000186505417029,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
20454,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0548103302717208,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
20455,hf-internal-testing/tiny-random-rembert,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0677112638950347,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20456,google/t5-efficient-base-dm512,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0299563780426978,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
20457,hakurei/gpt-j-random-tinier,The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider max_seq_length (4096 vs,yorko/scibert_scivocab_uncased_long_4096,0.0730727612972259,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
20458,sentence-transformers/paraphrase-MiniLM-L3-v2,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
20459,hfl/rbtl3,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0417130142450332,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
20460,indobenchmark/indobert-base-p1,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0148181381324926,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
20461,google/t5-efficient-large-el8,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0349536128342151,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
20462,KoichiYasuoka/roberta-small-belarusian,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0381870018318295,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
20463,PlanTL-GOB-ES/gpt2-large-bne,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0174299150239676,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
20464,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
20465,google/t5-efficient-base-el16,T5-Efficient-BASE-EL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el16,1.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
20466,google/t5-efficient-mini-nl6,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0516873653978108,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
20467,google/t5-efficient-large-dm2000,Its original repository can be found here,tau/splinter-large-qass,0.0509031917899846,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
20468,google/t5-efficient-mini-nl6,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0200192965567111,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
20469,Iskaj/hf-challenge-test,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0868974477052688,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
20470,ToToKr/kobigbird-bert-base-finetuned-klue,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
20471,allenai/macaw-answer-11b,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0312598223487536,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
20472,IDEA-CCNL/Erlangshen-Longformer-110M,"As mentioned in the paper: Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest",mrm8488/chEMBL_smiles_v1,0.0600873492658138,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
20473,Iskaj/hf-challenge-test,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
20474,gayanin/bart-mlm-pubmed-medterm,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.04753128439188,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
20475,dhpollack/distilbert-dummy-sentiment,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0353377778083086,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
20476,DemangeJeremy/4-sentiments-with-flaubert,This is a converted pytorch checkpoint for bert with 2L trained from scratch,tobiaslee/bert-2l-768h-uncased,0.0156677663326263,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20477,fnlp/bart-base-chinese,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.059291323646903,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
20478,google/t5-efficient-base-nh32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0074886288493871,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
20479,google/t5-efficient-tiny-ff3000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0489323139190673,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
20480,google/t5-efficient-base-el16,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
20481,rdenadai/BR_BERTo,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0543472444017727,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
20482,1one5ome/gpt2-chinese-gulong,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0578927807509899,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
20483,lgrobol/electra-minuscule-discriminator,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0864542014896869,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
20484,dhpollack/distilbert-dummy-sentiment,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0583208221942186,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
20485,google/t5-efficient-large-kv128,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0405299859121441,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
20486,aditeyabaral/sentencetransformer-distilbert-hinglish-big,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0221016630530357,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
20487,google/t5-efficient-large-nh2,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0786798931658267,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
20488,DeepPavlov/distilrubert-tiny-cased-conversational,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.0193975903093814,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
20489,microsoft/layoutlm-large-uncased,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.010490633547306,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
20490,google/t5-efficient-small-kv16,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0147381853312253,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
20491,google/t5-efficient-small-ff1000,Its original repository can be found here,tau/splinter-large-qass,0.0441547259688376,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
20492,TransQuest/monotransquest-hter-en_lv-it-nmt,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,1.0,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
20493,google/t5-efficient-tiny-nl12,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0829983055591582,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
20494,google/t5-efficient-small-dm2000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0057991980575025,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
20495,sonoisa/byt5-small-japanese,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0118920793756842,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
20496,google/t5-efficient-base-dm256,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0298853367567061,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
20497,abdusahmbzuai/ft-tatoeba-ar-en,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0273094251751899,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
20498,google/t5-efficient-large-nh24,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.033283108845353,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
20499,phueb/BabyBERTa-2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
20500,SetFit/deberta-v3-large__sst2__train-8-8,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0732128322124481,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
20501,zoha/wav2vec2-base-timit-demo-colab,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0702027231454849,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
20502,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0187729001045227,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
20503,google/t5-efficient-large-dm768,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
20504,mwesner/bart-mlm,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.03099768422544,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
20505,google/t5-efficient-tiny-nl16,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0576921198517083,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
20506,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.06204704195261,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
20507,IDEA-CCNL/Erlangshen-Longformer-110M,Zero-shot SELECTRA is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset,Recognai/zeroshot_selectra_small,0.0460702367126941,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
20508,cammy/led-large-16384-arxiv-100-MDS,"We started training from GPT-J, which has been trained on The Pile",Cedille/fr-boris,0.0912539958953857,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
20509,negfir/distilbert-base-uncased-finetuned-cola,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0992277488112449,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
20510,google/t5-efficient-tiny-el8,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0295015573501587,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
20511,tuner007/pegasus_qa,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0503406934440135,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
20512,google/t5-efficient-tiny-nh1,This does not make a very readable text,pere/DeUnCaser,0.0555284935981034,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
20513,google/t5-efficient-tiny-nh8,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0561158247292041,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
20514,google/t5-efficient-base-dm256,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0925540700554847,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
20515,DemangeJeremy/4-sentiments-with-flaubert,"Electra_Large Discriminator fine-tuned on SQuAD2.0 and custom QA datasetThis model is ahotrod/electra_large_discriminator_squad2_512  trained on additional custom dataset as:We used Google Colab for training the model,",superspray/electra_large_discriminator_squad2_custom_dataset,0.0401581637561321,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20516,sonoisa/byt5-small-japanese,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.044924646615982,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
20517,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0644964845851063,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
20518,everdoubling/byt5-Korean-small,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,1.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
20519,google/t5-efficient-large-nl10,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0527060888707637,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
20520,google/t5-efficient-small-dm2000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0571815036237239,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
20521,GKLMIP/electra-myanmar-small-uncased,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0465802289545535,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
20522,google/t5-efficient-base-nh8,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0943249240517616,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
20523,mwesner/reformer-clm,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,0.0915389955043792,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
20524,prajjwal1/bert-medium,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,34c0c0a8-7e6a-420b-9ebd-bb0cc78a7a25___bert-medium,HF
20525,Iskaj/hf-challenge-test,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0941469073295593,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
20526,uer/t5-v1_1-small-chinese-cluecorpussmall,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
20527,dbmdz/convbert-base-german-europeana-cased,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0619075422485669,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
20528,google/t5-efficient-base-dm1000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0374174080789088,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
20529,microsoft/layoutlm-base-uncased,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 240",eli4s/Bert-L12-h240-A12,0.0911180227994918,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
20530,google/t5-efficient-base-nl36,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0163994543254375,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
20531,andrejmiscic/simcls-scorer-billsum,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
20532,jkeruotis/LitBERTa-uncased,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0660633239895104,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
20533,hf-internal-testing/tiny-random-rembert,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.076370783150196,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20534,google/t5-efficient-tiny-nl2,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0076820645481348,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
20535,aditeyabaral/sentencetransformer-bert-hinglish-small,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0669354200363159,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
20536,google/t5-efficient-base-dm1000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0434847818687558,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
20537,ml6team/distilbart-tos-summarizer-tosdr,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0302276841830461,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
20538,hfl/rbtl3,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0493513289839029,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
20539,readerbench/RoBERT-small,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,1.0,-1.0,NLP,af9865c1-d52f-4c4d-8696-c19b9085823b___RoBERT-small,HF
20540,TJMUCH/transcriptome-iseeek,Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team,funnel-transformer/small,0.0444047227501869,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
20541,groar/gpt-neo-1.3B-finetuned-escape2,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.034581784158945,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
20542,suzuki/distilbert-base-uncased-finetuned-squad,Its original repository can be found here,tau/splinter-large-qass,0.0468415357172489,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
20543,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0236325152218341,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
20544,google/t5-efficient-base-nh16,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.06849274225533,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
20545,aubmindlab/aragpt2-mega,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.0555550046265125,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
20546,google/t5-efficient-base-nl32,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0106269242241978,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
20547,microsoft/layoutlm-large-uncased,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.013784185051918,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
20548,google/t5-efficient-base-kv16,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0219984129071235,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
20549,tau/tavbert-he,T5-Efficient-TINY-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl16,0.0313571132719516,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20550,nghuyong/ernie-tiny,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0116246963540713,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
20551,MoritzLaurer/MiniLM-L6-mnli,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0165390918652216,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
20552,flboehm/reddit-bert-text_5,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
20553,DTAI-KULeuven/robbertje-1-gb-bort,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.045744277536869,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
20554,google/byt5-xl,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0449394992319866,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
20555,ml6team/mt5-small-german-finetune-mlsum,py (Download Here) that define the architecture of CPT into your project,fnlp/cpt-base,0.016142220236361,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
20556,pulp/CHILDES-ParentBERTo,T5-Efficient-XL-NL28 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl28,0.0734082888811826,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
20557,tartuNLP/gpt-4-est-base,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0262401197105645,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
20558,microsoft/layoutlm-base-cased,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0975527465343475,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
20559,microsoft/layoutlm-base-cased,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0767787322402,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
20560,google/t5-efficient-small-el32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.009801011532545,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
20561,jhu-clsp/bibert-ende,For best results use python as below,pszemraj/led-large-book-summary,0.0732211458186308,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
20562,tau/splinter-large-qass,"However, this model outperforms the base model by large margins",tau/splinter-large-qass,1.0,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
20563,google/t5-efficient-small-kv128,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
20564,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0477246027439832,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
20565,DeepPavlov/distilrubert-tiny-cased-conversational,"Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text)",pszemraj/led-large-book-summary,0.0192166945586601,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
20566,amazon/bort,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0470670498907565,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20567,tau/tavbert-he,T5-Efficient-LARGE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl36,0.0299358665943145,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20568,CuongLD/wav2vec2-large-xlsr-vietnamese,"Fine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, Infore_25h dataset (Password: BroughtToYouByInfoRe)When using this model, make sure that your speech input is sampled at 16kHz",CuongLD/wav2vec2-large-xlsr-vietnamese,1.0,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
20569,google/t5-efficient-tiny-ff6000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0471397750079631,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
20570,Milos/slovak-gpt-j-405M,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0902709787090619,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
20571,jcblaise/distilbert-tagalog-base-cased,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,1.0,-1.0,NLP,29e88f0d-47b4-4733-8b59-3cbe0f4bb77e___distilbert-tagalog-base-cased,HF
20572,google/t5-efficient-small-dm768,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0482231732457875,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
20573,google/t5-efficient-tiny-ff12000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.030411111190915,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
20574,dbmdz/electra-small-turkish-cased-discriminator,"In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased ELECTRA small model for Turkish 🎉We release a small ELECTRA model for Turkish, that was trained on the same data as BERTurk",dbmdz/electra-small-turkish-cased-discriminator,1.0,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
20575,izumi-lab/bert-small-japanese-fin,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0911003127694129,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
20576,KBLab/megatron-bert-large-swedish-cased-165k,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0301615996286272,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
20577,tartuNLP/gpt-4-est-large,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0920265316963195,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
20578,obss/mt5-base-3task-highlight-tquad2,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0208086408674716,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
20579,google/t5-efficient-xl-nl6,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
20580,NovelAI/genji-python-6B,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0672769161562124,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
20581,KoichiYasuoka/roberta-small-belarusian,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
20582,ml6team/distilbart-tos-summarizer-tosdr,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
20583,ctoraman/RoBERTa-TR-medium-wp-66k,Tokenization algorithm is WordPiece,ctoraman/RoBERTa-TR-medium-wp-66k,1.0,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
20584,google/t5-efficient-large-el6,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0300510320812463,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
20585,aditeyabaral/sentencetransformer-distilbert-hinglish-big,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0922518074512481,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
20586,abdusahmbzuai/ft-tatoeba-ar-en,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.097709447145462,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
20587,tau/tavbert-he,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20588,fnlp/bart-large-chinese,We collect 120G novels as the pretraining data for LongLM,thu-coai/LongLM-large,0.0303189242258667,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
20589,hfl/rbt3,"If you found this model useful, please cite the original work:",filco306/gpt2-switchboard-paraphraser,0.0638863407075405,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
20590,amazon/bort,"The model that can directly translate between the 9,900 directions of 100 languages",facebook/m2m100-12B-avg-10-ckpt,0.0181860867887735,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20591,liaad/srl-pt_xlmr-base,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0706403503815332,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
20592,hfl/rbtl3,T5-Efficient-SMALL-EL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el12,0.0745319761335849,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
20593,DemangeJeremy/4-sentiments-with-flaubert,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,0.0480181900784373,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20594,google/t5-efficient-mini,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0465695308521389,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
20595,stas/mt5-tiny-random,† ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-162M,0.057386714965105,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20596,Recognai/selectra_medium,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0222406182438135,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
20597,stas/pegasus-cnn_dailymail-tiny-random,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0811659134924411,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
20598,everdoubling/byt5-Korean-small,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.0417354829609393,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
20599,google/t5-efficient-base-el16,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0792000405490398,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
20600,google/t5-efficient-base-nl40,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0246431734412908,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
20601,tau/tavbert-he,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.080297440290451,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20602,google/t5-efficient-base-nh32,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.074932862073183,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
20603,IDEA-CCNL/Erlangshen-Longformer-330M,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
20604,google/t5-efficient-tiny-nl2,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0702481493353843,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
20605,gayanin/bart-mlm-pubmed-medterm,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0505973100662231,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
20606,DemangeJeremy/4-sentiments-with-flaubert,This model is a distilled version of wav2vec2-xls-r-adult-child-cls on a private adult/child speech classification dataset,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0634133927524089,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20607,hfl/rbt3,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.0500742960721254,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
20608,google/t5-efficient-small-nl22,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
20609,mimi/Waynehills-NLP-doogie,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
20610,google/t5-11b-ssm-nqo,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0360236167907714,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
20611,j-hartmann/purchase-intention-english-roberta-large,The model architecture is custom for production so it only contains 70M parameters,nguyenvulebinh/envibert,0.0807271152734755,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
20612,crabz/distil-slovakbert-ner,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0748350694775581,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
20613,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,This model is a fine-tuned version of Rostlab/prot_bert on an unknown dataset,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,1.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
20614,google/t5-efficient-large-nh4,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0067253354936838,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
20615,mrm8488/xlm-multi-finetuned-xquadv1,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.032603908330202,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
20616,mwesner/bart-mlm,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0796497091650962,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
20617,google/t5-efficient-small-el4,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0694533139467239,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
20618,bookbot/distil-wav2vec2-adult-child-cls-52m,This model is a distilled version of wav2vec2-adult-child-cls on a private adult/child speech classification dataset,bookbot/distil-wav2vec2-adult-child-cls-52m,1.0,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
20619,google/t5-efficient-large-kv256,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0503028314560651,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
20620,mrm8488/bert-small-finetuned-squadv2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
20621,google/t5-efficient-base-nl32,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0915765017271041,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
20622,khizon/distilbert-unreliable-news-eng-4L,This model is a useful encapsulation of gene ontology functions,damlab/GO-language,0.0807938240468501,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
20623,google/t5-efficient-xl-nl16,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0042491294443607,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
20624,google/t5-efficient-small-kv256,This does not make a very readable text,pere/DeUnCaser,0.0539221949875354,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
20625,google/t5-efficient-base-dm256,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0023167389445007,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
20626,allenai/wmt19-de-en-6-6-big,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,0.0785208791494369,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
20627,microsoft/layoutlm-base-uncased,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0510972663760185,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
20628,hfl/chinese-electra-180g-small-generator,"For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA",hfl/chinese-electra-180g-small-generator,1.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
20629,TJMUCH/transcriptome-iseeek,Most language models are imprecise and Rockbot is no exception,bigjoedata/rockbot,0.0709529444575309,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
20630,google/t5-efficient-large-el2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
20631,piotr-rybak/poleval2021-task4-herbert-large-encoder,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,0.001882302109152,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
20632,mwesner/bart-mlm,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0938061773777008,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
20633,fnlp/bart-base-chinese,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.032823515124619,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
20634,aditeyabaral/sentencetransformer-bert-hinglish-small,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0587709173560142,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
20635,skt/ko-gpt-trinity-1.2B-v0.5,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0059959106147289,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
20636,TJMUCH/transcriptome-iseeek,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0731363147497177,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
20637,junnyu/roformer_base_wwm_cluecorpussmall,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0082177141060432,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
20638,IDEA-CCNL/Wenzhong-GPT2-3.5B,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0521896444261074,-1.0,NLP,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
20639,microsoft/deberta-v2-xxlarge-mnli,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0491062644869089,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
20640,cross-encoder/nli-deberta-v3-xsmall,This model was trained using SentenceTransformers Cross-Encoder class,cross-encoder/nli-deberta-v3-xsmall,1.0,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
20641,stas/t5-very-small-random,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0681004375219345,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
20642,stas/mt5-tiny-random,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20643,DemangeJeremy/4-sentiments-with-flaubert,Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering,tau/splinter-base-qass,0.0638601891696452,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20644,google/t5-efficient-small-nl36,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
20645,google/t5-efficient-small-dm1000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.053540103137493,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
20646,rdenadai/BR_BERTo,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0687927678227424,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
20647,stas/mt5-tiny-random,"The training data is private Bulgarian text from CNN, DailyMail articles",rmihaylov/pegasus-base-cnn-dailymail-bg,0.0903755277395248,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20648,google/t5-efficient-small-dm768,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.050338378176093,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
20649,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-EL64 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el64,0.0465077236294746,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
20650,thu-coai/LongLM-large,T5-Efficient-BASE-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff6000,0.0519159218917289,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
20651,allegro/plt5-large,plT5 models are T5-based language models trained on Polish corpora,allegro/plt5-large,1.0,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
20652,google/t5-efficient-small-ff9000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0635740887373685,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
20653,google/t5-efficient-small-kv256,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
20654,google/t5-efficient-mini,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0560189299285411,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
20655,TJMUCH/transcriptome-iseeek,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
20656,google/t5-efficient-tiny-nl12,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0158160384744405,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
20657,superspray/electra_large_discriminator_squad2_custom_dataset,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0548041462898254,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
20658,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0886047706007957,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
20659,amazon/bort,After that model was finetuned 1 epoch with sequence length 2048,sberbank-ai/rugpt3large_based_on_gpt2,0.0622615925967692,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20660,google/t5-efficient-small-nl48,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0663995351642369,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
20661,tau/tavbert-he,Detailed information about the data and pretraining steps can be found in this repository,dbmdz/convbert-base-german-europeana-cased,0.0308106932789086,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20662,Iskaj/hf-challenge-test,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0669193193316459,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
20663,DemangeJeremy/4-sentiments-with-flaubert,It is a two-stage approach consisting of a generator and a scorer,andrejmiscic/simcls-scorer-billsum,0.0641774889081716,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20664,eli4s/Bert-L12-h384-A6,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0435833257312575,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
20665,mimicheng/codeparrot-ds,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0979280769824981,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
20666,rdenadai/BR_BERTo,T5-Efficient-BASE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv128,0.0143256286780039,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
20667,microsoft/layoutlm-large-uncased,Source language: English,NYTK/translation-marianmt-en-hu,0.0630581602454185,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
20668,cross-encoder/stsb-TinyBERT-L-4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0035205776803195,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
20669,google/t5-efficient-tiny-nh16,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-nh16,1.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
20670,google/t5-efficient-large-dm128,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0534520531073212,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
20671,tucan9389/kcbert-base-finetuned-squad,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0258243102580308,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
20672,microsoft/layoutlm-base-uncased,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0651535540819168,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
20673,google/t5-efficient-small-dm1000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.03382945433259,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
20674,google/t5-efficient-mini-nl24,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0618100743740796,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
20675,google/t5-efficient-small-el16-dl4,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.000195050612092,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
20676,stas/pegasus-cnn_dailymail-tiny-random,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0471786009147763,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
20677,FremyCompany/xls-r-nl-v1-cv8-lm,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0034504101301232,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
20678,eli4s/Bert-L12-h384-A6,This model was pretrained on the bookcorpus dataset using knowledge distillation,eli4s/Bert-L12-h384-A6,1.0,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
20679,google/t5-efficient-small-el16-dl4,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
20680,microsoft/layoutlm-base-uncased,This does not make a very readable text,pere/DeUnCaser,0.0199006292968988,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
20681,TJMUCH/transcriptome-iseeek,Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective,Finnish-NLP/convbert-base-generator-finnish,0.0651309117674827,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
20682,gayanin/bart-mlm-pubmed-medterm,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0843797326087951,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
20683,stas/mt5-tiny-random,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20684,rinna/japanese-gpt-1b,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0513983679314454,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
20685,google/t5-efficient-mini-nl12,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0151110757142305,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
20686,facebook/mbart-large-50-many-to-one-mmt,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.057870407278339,-1.0,NLP,1aed0c2f-c6c7-49df-87c0-c3bebcdb63af___mbart-large-50-many-to-one-mmt,HF
20687,indobenchmark/indobert-base-p1,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0305364442368348,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
20688,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh24,0.0578823784987131,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
20689,google/t5-efficient-large-kv32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0340216336771846,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
20690,google/t5-efficient-xl-nl8,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.033610263839364,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
20691,stas/mt5-tiny-random,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0609235577285289,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20692,google/t5-efficient-large-el8,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
20693,DemangeJeremy/4-sentiments-with-flaubert,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0679081883281468,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20694,amazon/bort,"Hebrew poetry text generation model, fined tuned upon hebrew-gpt_neo-tiny which was trained using EleutherAI's gpt-neo",Norod78/hebrew_poetry-gpt_neo-tiny,0.0404008841142057,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20695,hajime9652/xlnet-japanese,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.083132617175579,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
20696,jhu-clsp/bibert-ende,Zero-shot SELECTRA is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset,Recognai/zeroshot_selectra_small,0.0251660880943139,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
20697,mwesner/reformer-clm,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.072781927883625,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
20698,google/t5-efficient-tiny-nl2,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0517208445817232,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
20699,google/t5-efficient-tiny-nl16,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0116993002593517,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
20700,DemangeJeremy/4-sentiments-with-flaubert,The DeUnCaser is a sequence-to-sequence byT5 model that is reversing this process,pere/DeUnCaser,0.0568609237670898,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20701,google/t5-efficient-small-nl20,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0502873994410037,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
20702,mwesner/reformer-clm,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0591249912977218,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
20703,NovelAI/genji-python-6B,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,1.0,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
20704,google/t5-efficient-base-el6,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0711541920900344,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
20705,google/t5-efficient-small-kv128,Its original repository can be found here,tau/splinter-large-qass,0.067404331639409,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
20706,GKLMIP/electra-myanmar-small-uncased,The codes for the pretraining are available at retarfi/language-pretraining,izumi-lab/bert-small-japanese-fin,0.0723826140165328,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
20707,aditeyabaral/sentencetransformer-bert-hinglish-small,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0664936006069183,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
20708,enelpol/czywiesz-context,The model was created for selective question answering in Polish,enelpol/czywiesz-context,1.0,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
20709,groar/gpt-neo-1.3B-finetuned-escape2,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0649274811148643,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
20710,microsoft/layoutlm-base-cased,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
20711,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.0493927299976348,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
20712,google/t5-efficient-large-nl10,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
20713,google/t5-efficient-xl-nl2,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0759862363338469,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
20714,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,"This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,1.0,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
20715,Recognai/zeroshot_selectra_small,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
20716,google/t5-efficient-small-ff3000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0613383352756499,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
20717,allenai/wmt16-en-de-12-1,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0197854675352573,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
20718,eli4s/Bert-L12-h256-A4,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0064757838845252,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
20719,qarib/bert-base-qarib_far_6500k,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0221882462501525,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
20720,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0957934260368347,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
20721,google/t5-efficient-base-el6,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0134437009692191,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
20722,google/t5-efficient-tiny-ff6000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0395599137991666,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
20723,tobiaslee/bert-2l-768h-uncased,The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider max_seq_length (4096 vs,yorko/scibert_scivocab_uncased_long_4096,0.0658472217619419,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
20724,celine98/canine-s-finetuned-sst2,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0784958526492118,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
20725,uer/t5-v1_1-base-chinese-cluecorpussmall,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0550989359617233,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
20726,google/t5-11b-ssm-nqo,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
20727,google/t5-efficient-small-kv256,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0088511016219853,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
20728,sentence-transformers/paraphrase-MiniLM-L3-v2,"You can download the set of Chinese PEGASUS models either from the UER-py Modelzoo page, or via HuggingFace from the links below:You can use this model directly with a pipeline for text2text generation (take the case of PEGASUS-Base):",uer/pegasus-base-chinese-cluecorpussmall,0.0485015623271465,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
20729,anirudh21/xlnet-base-cased-finetuned-rte,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0769211128354072,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
20730,microsoft/deberta-v2-xxlarge-mnli,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0206907344982027,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
20731,google/t5-efficient-base-ff9000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0480299368500709,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
20732,anas-awadalla/bert-tiny-finetuned-squad,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0553932525217533,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
20733,google/t5-efficient-large-nh2,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0189400129020214,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
20734,microsoft/layoutlm-base-uncased,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.075262039899826,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
20735,google/t5-efficient-small-dm1000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
20736,obss/mt5-base-3task-highlight-tquad2,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0816550403833388,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
20737,google/t5-efficient-base-el8,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0752270258963107,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
20738,saibo/random-roberta-mini,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
20739,jean-paul/kinyaRoberta-small,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
20740,diper1998/distilgpt2-finetuned-AT,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
20741,eli4s/Bert-L12-h240-A12,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0551132052205502,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
20742,google/t5-efficient-large-nh12,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0805143453180789,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
20743,google/t5-efficient-mini-nl8,This does not make a very readable text,pere/DeUnCaser,0.0625682026147841,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
20744,google/t5-efficient-base-nh32,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
20745,google/t5-efficient-tiny-nh8,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0052666552364825,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
20746,google/t5-efficient-large-dm768,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0506960749626159,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
20747,google/t5-efficient-base-nh8,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0398616380989551,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
20748,allenai/wmt19-de-en-6-6-base,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
20749,IlyaGusev/rugpt3medium_sum_gazeta,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,0.0975486896932124,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
20750,google/t5-efficient-tiny-nl6,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0631886422634124,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
20751,mimicheng/codeparrot-ds,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0984679087996482,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
20752,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0808684825897216,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
20753,google/t5-efficient-mini-nl12,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0620964774861931,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
20754,stas/t5-very-small-random,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0954568311572074,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
20755,google/t5-efficient-xl-nl28,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0050812335684895,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
20756,prajjwal1/bert-mini,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,d524d2bf-0c8c-4e35-817b-27b8261b2166___bert-mini,HF
20757,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0761453732848167,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
20758,google/t5-efficient-small-kv128,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0559874745085835,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
20759,google/t5-efficient-tiny-nl2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
20760,facebook/m2m100-12B-avg-10-ckpt,"The model that can directly translate between the 9,900 directions of 100 languages",facebook/m2m100-12B-avg-10-ckpt,1.0,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
20761,hfl/chinese-xlnet-mid,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.003473716477553,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
20762,microsoft/layoutlm-base-cased,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0818773210048675,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
20763,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,This model is a distilled version of wav2vec2-xls-r-adult-child-cls on a private adult/child speech classification dataset,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,1.0,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
20764,stas/mt5-tiny-random,"Encoder structure-based Bidirection language model, focusing on solving various natural language understanding tasks",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0133925937116146,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20765,hf-internal-testing/tiny-random-rembert,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0170823987573385,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20766,google/t5-efficient-base-kv16,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
20767,liaad/srl-pt_xlmr-base,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0379996097957094,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
20768,google/t5-efficient-large-nh8-nl32,This does not make a very readable text,pere/DeUnCaser,0.0692707058042287,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
20769,amazon/bort,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0333631807006895,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20770,fnlp/bart-base-chinese,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.0054968744516372,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
20771,google/t5-efficient-large-nh8,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0606139637529849,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
20772,google/t5-efficient-tiny-nh1,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.006522575393319,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
20773,hfl/rbt3,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0242930487729608,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
20774,google/t5-efficient-small-ff9000,This does not make a very readable text,pere/DeUnCaser,0.077980913221836,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
20775,dexhrestha/Nepali-DistilBERT,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0035339822061359,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
20776,sentence-transformers/paraphrase-MiniLM-L3-v2,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0463133752346038,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
20777,mwesner/reformer-clm,T5-Efficient-BASE-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl24,0.093622051179409,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
20778,allenai/wmt19-de-en-6-6-big,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0230974424630403,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
20779,damlab/GO-language,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,1.0,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
20780,ian0delond/model-test,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0686835497617721,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
20781,google/bert_uncased_L-8_H-768_A-12,"This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking)",google/bert_uncased_L-8_H-768_A-12,1.0,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
20782,beston91/gpt2-xl_ft_logits_10k,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
20783,google/t5-efficient-large-dm256,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0185751859098673,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
20784,google/t5-efficient-base-nl40,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0674838405102491,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
20785,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0955146700143814,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
20786,cosmoquester/bart-ko-mini,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0487701011200745,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
20787,Sindhu/muril-large-squad2,This model is finetuned for QA task on Squad2 from Muril Large checkpoint,Sindhu/muril-large-squad2,1.0,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
20788,mrm8488/deberta-v3-small-finetuned-mrpc,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0945084616541862,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
20789,lgrobol/electra-minuscule-discriminator,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
20790,google/t5-efficient-base-el6,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0887333676218986,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
20791,gchhablani/fnet-base-finetuned-wnli,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0192287936806678,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
20792,google/t5-efficient-base-el16,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0547776762396096,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
20793,IDEA-CCNL/Wenzhong-GPT2-3.5B,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,1.0,-1.0,NLP,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
20794,google/bert_uncased_L-8_H-768_A-12,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-8_H-768_A-12,1.0,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
20795,hajime9652/xlnet-japanese,Source language: English,NYTK/translation-marianmt-en-hu,0.0495817475020885,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
20796,tuner007/pegasus_qa,I have made no attempts to censor,bigjoedata/rockbot,0.0008033160120248,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
20797,hf-internal-testing/tiny-random-rembert,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0976356491446495,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20798,mimi/Waynehills-NLP-doogie,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0263637211173772,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
20799,pulp/CHILDES-ParentBERTo,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0938790626823902,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
20800,tartuNLP/gpt-4-est-large,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.022307151928544,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
20801,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0123233366757631,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
20802,tau/tavbert-he,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20803,DeepPavlov/distilrubert-tiny-cased-conversational-v1,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
20804,google/t5-efficient-xl-nl6,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0632010251283645,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
20805,google/t5-efficient-tiny-nh1,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.067328555509448,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
20806,google/t5-efficient-small-ff3000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0670673921704292,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
20807,IDEA-CCNL/Erlangshen-Longformer-330M,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.0657458566129207,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
20808,mimicheng/codeparrot-ds,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0570926144719123,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
20809,obss/mt5-base-3task-highlight-tquad2,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0350906047970056,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
20810,google/t5-efficient-base-dm512,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
20811,google/t5-efficient-small-nl24,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0288525680080056,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
20812,flboehm/reddit-bert-text_5,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0896506533026695,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
20813,j-hartmann/purchase-intention-english-roberta-large,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0311166352281967,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
20814,mwesner/bart-mlm,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0940527468919754,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
20815,hajime9652/xlnet-japanese,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0654273480176925,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
20816,anas-awadalla/bert-tiny-finetuned-squad,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0961214229464531,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
20817,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl40,0.0238625239580869,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20818,google/t5-efficient-base-el6,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
20819,hajime9652/xlnet-japanese,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0892387628555297,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
20820,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,Training metrics were logged via Tensorboard,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,1.0,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
20821,hf-internal-testing/tiny-random-rembert,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0199599545449018,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20822,mimi/Waynehills-NLP-doogie,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0422567501664161,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
20823,google/t5-efficient-xl-nl12,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
20824,cointegrated/rubert-tiny-bilingual-nli,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
20825,hfl/rbtl3,"This is part of a project from which resulted the following models:For more information, please see the accompanying article (See BibTeX entry and citation info below) and the project's github",liaad/srl-pt_xlmr-base,0.010669270530343,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
20826,obss/mt5-base-3task-highlight-tquad2,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0453595910221338,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
20827,cammy/led-base-16384-100-MDS,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0757330954074859,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
20828,microsoft/layoutlm-large-uncased,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,0.034470222890377,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
20829,brad1141/Longformer-finetuned-norm,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0581531673669815,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
20830,SetFit/deberta-v3-large__sst2__train-8-8,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
20831,microsoft/layoutlm-base-cased,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.005147055722773,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
20832,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
20833,Palak/microsoft_deberta-base_squad,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0959832966327667,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
20834,negfir/distilbert-base-uncased-finetuned-squad,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0579771101474761,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
20835,DemangeJeremy/4-sentiments-with-flaubert,"As shown in the Metrics section, the small version (5 times fewer parameters) performs slightly worse, while the medium version (3 times fewer parameters) outperforms the BETO based zero-shot classifier",Recognai/zeroshot_selectra_small,0.0336769595742225,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20836,amazon/bort,net Training MS MarcoThe usage becomes easier when you have SentenceTransformers installed,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0392249589785933,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20837,mwesner/reformer-clm,T5-Efficient-BASE-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl4,0.0712787881493568,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
20838,GKLMIP/electra-myanmar-small-uncased,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
20839,hfl/rbtl3,T5-Efficient-LARGE-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el4,0.0107163954526185,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
20840,dhpollack/distilbert-dummy-sentiment,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.021874863654375,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
20841,google/t5-efficient-tiny-nl24,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0889959335327148,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
20842,hf-internal-testing/tiny-random-rembert,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0884057655930519,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20843,negfir/distilbert-base-uncased-finetuned-squad,This does not make a very readable text,pere/DeUnCaser,0.0376738309860229,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
20844,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,For best results use python as below,pszemraj/led-large-book-summary,0.0204603746533393,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
20845,superspray/electra_large_discriminator_squad2_custom_dataset,This is an mT5-based model for machine translation (Persian -&gt; English),persiannlp/mt5-large-parsinlu-opus-translation_fa_en,0.0951291471719741,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
20846,uer/t5-v1_1-base-chinese-cluecorpussmall,This is a sentence-transformers model: It maps sentences and paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search,piotr-rybak/poleval2021-task4-herbert-large-encoder,0.033163346350193,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
20847,tau/tavbert-he,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20848,pszemraj/led-large-book-summary,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
20849,mgreenbe/bertlet-base-uncased-for-sequence-classification,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0053264237940311,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
20850,IlyaGusev/rugpt3medium_sum_gazeta,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.000478571280837,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
20851,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0308019611984491,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
20852,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0528144091367721,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
20853,SupriyaArun/squeezebert-uncased-finetuned-squad,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
20854,Milos/slovak-gpt-j-162M,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
20855,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
20856,google/t5-efficient-small-el64,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0247818762436508,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
20857,google/t5-efficient-xl-nl6,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0590670481324195,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
20858,sentence-transformers/paraphrase-MiniLM-L3-v2,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0776152163743972,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
20859,KoichiYasuoka/roberta-small-belarusian,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0710845068097114,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
20860,google/t5-efficient-base-kv32,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0328007936477661,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
20861,google/t5-efficient-small-dm256,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0121244173496961,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
20862,tau/tavbert-he,"We collaborated with TOSDR to work with their data, and the model is finetuned accordingly",ml6team/distilbart-tos-summarizer-tosdr,0.0137112215161323,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20863,google/t5-efficient-small-dm256,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
20864,google/t5-efficient-base-dm2000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
20865,amazon/bort,ipynb and the weights were converted to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead,google/reformer-crime-and-punishment,0.0641850940883159,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20866,google/t5-efficient-xl-nl16,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0590670481324195,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
20867,mwesner/bart-mlm,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0572760961949825,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
20868,sentence-transformers/paraphrase-MiniLM-L3-v2,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
20869,celine98/canine-s-finetuned-sst2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0456381402909755,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
20870,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
20871,ml6team/mt5-small-german-finetune-mlsum,The exact expression to filter the dataset was the following:The fine-tuned model was evaluated on 2000 random articles from the validation set,ml6team/mt5-small-german-finetune-mlsum,1.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
20872,google/t5-efficient-xxl-nl4,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0071657849475741,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
20873,mwesner/reformer-clm,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
20874,SupriyaArun/squeezebert-uncased-finetuned-squad,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
20875,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0800773203372955,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
20876,khizon/distilbert-unreliable-news-eng-4L,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
20877,1one5ome/gpt2-chinese-gulong,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,2.0390376448631287e-05,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
20878,uer/t5-v1_1-base-chinese-cluecorpussmall,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0212512863799929,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
20879,cosmoquester/bart-ko-mini,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0136963352560996,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
20880,google/t5-efficient-small-nl20,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0630123503506183,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
20881,jhu-clsp/bibert-ende,"Please check out our EMNLP 2021 paper ""BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation"" for more details",jhu-clsp/bibert-ende,1.0,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
20882,google/t5-efficient-large-nh2,Its original repository can be found here,tau/splinter-large-qass,0.052867317572236,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
20883,google/t5-efficient-small-el32,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0680742226541041,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
20884,anas-awadalla/bert-tiny-finetuned-squad,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
20885,aubmindlab/aragpt2-mega,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0279416260309517,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
20886,abdusahmbzuai/ft-tatoeba-ar-en,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0994429439306259,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
20887,google/t5-efficient-small-dm128,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0357260629534721,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
20888,DemangeJeremy/4-sentiments-with-flaubert,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20889,aubmindlab/aragpt2-mega,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0100337620824575,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
20890,superb/hubert-large-superb-sid,For more information refer to SUPERB: Speech processing Universal PERformance Benchmark,superb/hubert-large-superb-sid,1.0,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
20891,hfl/chinese-electra-180g-small-ex-discriminator,"Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants",hfl/chinese-electra-180g-small-ex-discriminator,1.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
20892,VietAI/gpt-neo-1.3B-vietnamese-news,This repository provides a 1.3B-parameter Japanese GPT model,rinna/japanese-gpt-1b,0.0742394700646399,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
20893,pszemraj/led-large-book-summary,"Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text)",pszemraj/led-large-book-summary,1.0,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
20894,google/t5-efficient-small-kv256,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0447463952004908,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
20895,kyryl0s/gpt2-uk-xxs,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
20896,google/t5-efficient-tiny-nl6,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0245057698339223,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
20897,google/t5-efficient-base-nl32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0436310376971959,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
20898,google/t5-efficient-tiny-el8,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0659365840256213,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
20899,google/t5-efficient-base-ff12000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0045775715261697,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
20900,google/t5-efficient-small-kv256,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0770742706954479,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
20901,Palak/microsoft_deberta-base_squad,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0878922268748283,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
20902,google/t5-efficient-tiny-ff12000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0502342386171221,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
20903,google/t5-efficient-mini,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.069009281694889,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
20904,google/t5-efficient-mini-nl24,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0619650818407535,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
20905,superspray/electra_large_discriminator_squad2_custom_dataset,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0880531668663024,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
20906,andrejmiscic/simcls-scorer-billsum,We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric,andrejmiscic/simcls-scorer-billsum,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
20907,microsoft/layoutlm-base-uncased,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0429590754210948,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
20908,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,I have made no attempts to censor,bigjoedata/rockbot,0.0928656905889511,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
20909,mrm8488/bert-small-finetuned-squadv2,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0422513019293546,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
20910,diper1998/distilgpt2-finetuned-AT,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0879123657941818,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
20911,Parsa/BBB_prediction_classification_SMILES,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0278202751651405,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
20912,1one5ome/gpt2-chinese-gulong,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0825330540537834,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
20913,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0685852319002151,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
20914,allenai/wmt16-en-de-dist-6-1,"For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation",allenai/wmt16-en-de-dist-6-1,1.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
20915,mmaguero/gn-bert-tiny-cased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
20916,google/t5-efficient-tiny-ff9000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0332447215914726,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
20917,clue/xlnet_chinese_large,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0441881157457828,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
20918,google/t5-efficient-xl-nl6,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0912656188011169,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
20919,tau/tavbert-he,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0243858797475696,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
20920,google/t5-efficient-base-kv128,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0334325637668371,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
20921,google/t5-efficient-large-kv128,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0693180933594703,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
20922,superspray/electra_large_discriminator_squad2_custom_dataset,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0957835763692855,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
20923,google/t5-efficient-large-el2,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0453358776867389,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
20924,sentence-transformers/paraphrase-MiniLM-L3-v2,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0168368127197027,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
20925,google/t5-efficient-tiny-el8,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
20926,thu-coai/LongLM-large,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0324546849975983,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
20927,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0501939393579959,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
20928,hfl/chinese-electra-180g-small-ex-generator,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0476264047125975,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
20929,everdoubling/byt5-Korean-small,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,1.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
20930,google/t5-efficient-base-kv256,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0239938767626881,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
20931,mrm8488/xlm-multi-finetuned-xquadv1,Language modelIt includes the following languages:,mrm8488/xlm-multi-finetuned-xquadv1,1.0,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
20932,microsoft/layoutlm-base-uncased,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0200758501887321,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
20933,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0277889051164189,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
20934,brad1141/Longformer-finetuned-norm,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
20935,google/t5-efficient-tiny-nl2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
20936,malay-huggingface/xlnet-large-bahasa-cased,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0634967796504497,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
20937,mrm8488/chEMBL_smiles_v1,The learned pattern probabilities can be used for de novo SMILES generation,mrm8488/chEMBL_smiles_v1,1.0,-1.0,NLP,919d68f1-5654-4b4c-a9c7-f59f67ababe7___chEMBL_smiles_v1,HF
20938,stas/pegasus-cnn_dailymail-tiny-random,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.025773636996746,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
20939,google/t5-efficient-xl-nl8,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.048461727797985,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
20940,google/t5-efficient-base-kv16,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
20941,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0688174813985824,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
20942,aditeyabaral/sentencetransformer-bert-hinglish-small,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0478546470403671,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
20943,aubmindlab/aragpt2-mega,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0251322193071246,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
20944,hfl/rbtl3,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,0.0964037999510764,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
20945,clue/xlnet_chinese_large,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0394717138260602,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
20946,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh32,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20947,stas/mt5-tiny-random,"This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:The model was fine-tuned on 2,000 manually annotated social media posts",j-hartmann/purchase-intention-english-roberta-large,0.0605114325881004,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20948,kyryl0s/gpt2-uk-xxs,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0476540140807628,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
20949,microsoft/layoutlm-large-uncased,"Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle",google/bigbird-roberta-large,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
20950,uer/t5-v1_1-base-chinese-cluecorpussmall,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0514524430036544,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
20951,google/t5-efficient-small-dm1000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
20952,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0559460893273353,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
20953,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.093704454600811,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
20954,suzuki/distilbert-base-uncased-finetuned-squad,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0521924421191215,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
20955,amazon/bort,This is a sentence-transformers model: It maps sentences and paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search,sentence-transformers/paraphrase-MiniLM-L3-v2,0.0647482201457023,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
20956,Palak/microsoft_deberta-base_squad,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0682288110256195,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
20957,mwesner/reformer-clm,Its original repository can be found here,tau/splinter-large-qass,0.0262924283742904,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
20958,google/t5-efficient-xl-nl4,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0387951508164405,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
20959,google/t5-efficient-small-el12,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0635658986866473,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
20960,allenai/macaw-3b,This model is a fine-tuned version of gpt2 on an unknown dataset,mimicheng/codeparrot-ds,0.0728215320656696,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
20961,google/t5-efficient-xl-nl4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0315714590251445,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
20962,google/t5-efficient-xxl-nl4,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0890060067176818,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
20963,groar/gpt-neo-1.3B-finetuned-escape2,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0923951268196106,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
20964,tartuNLP/gpt-4-est-large,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,0.0829954668879509,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
20965,hfl/chinese-electra-small-ex-generator,This does not make a very readable text,pere/DeUnCaser,0.0206083464436232,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
20966,stas/t5-very-small-random,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0790456607937812,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
20967,IDEA-CCNL/Zhouwenwang-Unified-1.3B,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0664679735898971,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
20968,lgrobol/electra-minuscule-generator,"In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0809106826782226,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
20969,jonfd/convbert-small-igc-is,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0093011297285557,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
20970,DemangeJeremy/4-sentiments-with-flaubert,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0028504692018032,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20971,piotr-rybak/poleval2021-task4-herbert-large-encoder,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0608411766588687,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
20972,microsoft/layoutlm-large-uncased,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.062629148364067,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
20973,indobenchmark/indobert-base-p1,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0304383778323729,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
20974,google/t5-efficient-xl-nl4,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0687342882156372,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
20975,IDEA-CCNL/Erlangshen-Longformer-330M,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
20976,anirudh21/xlnet-base-cased-finetuned-rte,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0885792225599289,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
20977,google/t5-efficient-base-nl40,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0645267758518457,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
20978,DemangeJeremy/4-sentiments-with-flaubert,For tasks such as text generation you should look at models like GPT2,google/rembert,0.0904237963259219,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
20979,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0152923092246055,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
20980,google/t5-efficient-xl-nl28,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0480969212949275,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
20981,stas/mt5-tiny-random,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0986042469739914,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
20982,aubmindlab/aragpt2-mega,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0061573674902319,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
20983,malay-huggingface/t5-super-tiny-bahasa-cased,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0798314064741134,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
20984,google/t5-efficient-base-dm512,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
20985,google/t5-efficient-large-el2,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0438060546293854,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
20986,yorko/scibert_scivocab_uncased_long_4096,This is a Lonformer version of the SciBERT uncased model by Allen AI,yorko/scibert_scivocab_uncased_long_4096,1.0,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
20987,jorge-henao/gpt2-small-spanish-disco-poetry-wt,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
20988,google/t5-efficient-base-nh16,This does not make a very readable text,pere/DeUnCaser,0.0195697043091058,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
20989,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0873786062002182,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
20990,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0350773371756076,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
20991,abdusahmbzuai/ft-tatoeba-ar-en,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0521824806928634,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
20992,sentence-transformers/paraphrase-MiniLM-L3-v2,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
20993,Rostlab/prot_bert,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
20994,google/t5-efficient-tiny-nl32,Its original repository can be found here,tau/splinter-large-qass,0.0558541212230919,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
20995,sberbank-ai/rugpt3large_based_on_gpt2,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0139850459527223,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
20996,indobenchmark/indobert-base-p1,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
20997,google/t5-efficient-tiny-nh8,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
20998,hf-internal-testing/tiny-random-rembert,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.053015150129795,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
20999,google/t5-efficient-large-nh12,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0545010864734648,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
21000,wukevin/tcr-bert,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.05902193300426,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
21001,clue/xlnet_chinese_large,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
21002,KoichiYasuoka/roberta-small-belarusian,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0582733806222677,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
21003,IDEA-CCNL/Erlangshen-Longformer-330M,TransQuest outperforms current open-source quality estimation frameworks such as OpenKiwi and DeepQuest,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0235737888142466,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
21004,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,T5-Efficient-LARGE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv32,0.0895135700702667,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
21005,Parsa/BBB_prediction_classification_SMILES,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0746910398205121,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
21006,google/t5-efficient-tiny-ff9000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-ff9000,1.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
21007,bolbolzaban/gpt2-persian,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0571813844144343,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
21008,TJMUCH/transcriptome-iseeek,This model is a fine-tuned version of allenai/led-base-16384 on an unknown dataset,cammy/led-base-16384-100-MDS,0.0977080315351486,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
21009,mldmm/GlassBERTa,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,1.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
21010,google/t5-efficient-xl-nl6,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0686515793204307,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
21011,cross-encoder/nli-deberta-v3-xsmall,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0509458215907215,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
21012,google/t5-efficient-base-nh16,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0529550611972808,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
21013,castorini/afriberta_small,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0420259507372975,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
21014,google/t5-efficient-base-nl48,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0045954082161188,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
21015,google/t5-efficient-large-el6,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
21016,antoiloui/belgpt2,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0862585604190826,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
21017,google/t5-efficient-base-dm2000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0841436646878719,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
21018,negfir/distilbert-base-uncased-finetuned-cola,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
21019,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
21020,rdenadai/BR_BERTo,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,1.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
21021,google/t5-efficient-xl-nl4,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
21022,everdoubling/byt5-Korean-small,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0229996815323829,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
21023,anas-awadalla/bert-tiny-finetuned-squad,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
21024,cammy/led-large-16384-arxiv-100-MDS,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0503875985741615,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
21025,google/t5-efficient-mini-nl8,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0501057729125022,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
21026,google/t5-efficient-small-el64,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.072870224714279,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
21027,ian0delond/model-test,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0276799127459526,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
21028,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0012179799377918,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
21029,DTAI-KULeuven/robbertje-1-gb-bort,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
21030,allenai/wmt19-de-en-6-6-big,Its original repository can be found here,tau/splinter-large-qass,0.05090793967247,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
21031,DeepPavlov/distilrubert-tiny-cased-conversational,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.034391305098931,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
21032,google/t5-efficient-base-el8,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
21033,flax-community/bengali-t5-base,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
21034,zoha/wav2vec2-base-timit-demo-colab,Note: flaubert-small-cased is partially trained so performance is not guaranteed,flaubert/flaubert_small_cased,0.0972364246845245,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
21035,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0961717739701271,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
21036,nghuyong/ernie-tiny,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0429204460233449,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
21037,PlanTL-GOB-ES/gpt2-base-bne,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-base-bne,1.0,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
21038,TJMUCH/transcriptome-iseeek,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
21039,google/reformer-crime-and-punishment,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
21040,GKLMIP/electra-myanmar-small-uncased,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0432771630585194,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
21041,google/t5-efficient-small-nl48,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0409923400729894,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
21042,google/t5-efficient-small-nl2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
21043,nguyenvulebinh/envibert,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0618500262498855,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
21044,google/t5-efficient-base-ff6000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0158754959702491,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
21045,skt/ko-gpt-trinity-1.2B-v0.5,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,0.0227438043802976,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
21046,malay-huggingface/xlnet-tiny-bahasa-cased,Pretrained XLNET tiny language model for Malay,malay-huggingface/xlnet-tiny-bahasa-cased,1.0,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
21047,brad1141/Longformer-finetuned-norm,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0567337125539779,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
21048,negfir/distilbert-base-uncased-finetuned-cola,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
21049,DemangeJeremy/4-sentiments-with-flaubert,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21050,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0475694350898265,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
21051,cammy/led-large-16384-arxiv-100-MDS,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.044648241251707,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
21052,IlyaGusev/rugpt3medium_sum_gazeta,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0768735595047473,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
21053,crabz/distil-slovakbert-ner,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
21054,ian0delond/model-test,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0976654663681984,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
21055,hajime9652/xlnet-japanese,Pegasus (Reddit/TIFU) for conversational summaries trained on the samsum dataset!The data is the samsum dataset for conversional summaries,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0929950624704361,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
21056,DeepPavlov/distilrubert-tiny-cased-conversational,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0097215455025434,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
21057,google/t5-efficient-large-el2,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
21058,bigjoedata/rockbot,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0129749585563937,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
21059,google/t5-efficient-xl-nl28,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0725005008280277,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
21060,qarib/bert-base-qarib_far_6500k,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
21061,google/t5-efficient-base-kv128,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0492166336625814,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
21062,google/t5-efficient-tiny-ff6000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0370076820254325,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
21063,akdeniz27/deberta-v2-xlarge-cuad,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0104816854000091,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
21064,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0576702356338501,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
21065,google/t5-efficient-small-kv16,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,1.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
21066,google/t5-efficient-small-kv256,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0242679771035909,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
21067,mrp/simcse-model-wangchanberta,We use SimCSE here by using mBERT as the baseline model and training the model with Thai Wikipedia hereUsing this model becomes easy when you have sentence-transformers installed:,mrp/simcse-model-wangchanberta,1.0,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
21068,google/t5-efficient-base-nh16,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0464719235897063,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
21069,aditeyabaral/sentencetransformer-distilbert-hinglish-big,org/abs/1904.09223This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-1.0,0.0062798634171485,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
21070,google/t5-efficient-large-el6,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
21071,rmihaylov/pegasus-base-cnn-dailymail-bg,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0135167790576815,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
21072,google/t5-efficient-xxl-nl4,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0598977990448474,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
21073,tartuNLP/gpt-4-est-large,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.047793336212635,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
21074,google/t5-efficient-base-ff6000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0180318551138042,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
21075,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff6000,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21076,google/t5-efficient-large-dm2000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
21077,VietAI/gpt-neo-1.3B-vietnamese-news,Colab: linkEvaluation script: evaluate,IlyaGusev/rugpt3medium_sum_gazeta,0.0785143859684467,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
21078,google/t5-efficient-small-dm1000,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0404506428167223,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
21079,SupriyaArun/squeezebert-uncased-finetuned-squad,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0402105152606964,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
21080,jonatasgrosman/wav2vec2-xls-r-1b-english,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0552915881077448,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
21081,everdoubling/byt5-Korean-small,Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature,sultan/BioM-ELECTRA-Large-Generator,0.0379498068243264,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
21082,google/t5-efficient-tiny-nh16,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0533766653388738,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
21083,google/t5-efficient-small-dm2000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
21084,tucan9389/kcbert-base-finetuned-squad,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0817048251628875,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
21085,google/t5-efficient-small-dm768,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0100896386429667,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
21086,mimicheng/codeparrot-ds,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
21087,google/t5-efficient-large-nl36,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
21088,google/t5-efficient-base-ff12000,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0789531916379928,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
21089,uer/t5-v1_1-small-chinese-cluecorpussmall,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0392445959150791,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
21090,google/t5-efficient-mini-nl24,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0930275321006774,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
21091,jnz/electra-ka,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
21092,stas/mt5-tiny-random,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
21093,obss/mt5-base-3task-highlight-tquad2,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0504090581089258,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
21094,lannelin/bert-imdb-1hidden,"Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan BaskaranWork done via MLDMM Lab",mldmm/GlassBERTa,0.0686633090178171,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
21095,google/t5-efficient-base-kv16,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0286024706438183,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
21096,google/bert_uncased_L-10_H-128_A-2,The smaller BERT models are intended for environments with restricted computational resources,google/bert_uncased_L-10_H-128_A-2,1.0,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
21097,allenai/wmt19-de-en-6-6-base,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
21098,groar/gpt-neo-1.3B-finetuned-escape2,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0825239941477775,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
21099,tucan9389/kcbert-base-finetuned-squad,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0956966876983642,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
21100,google/t5-efficient-small-el12,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0077357245609164,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
21101,hajime9652/xlnet-japanese,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0973121970891952,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
21102,patrickvonplaten/unispeech-sat-large-timit-ft,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0251064337790012,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
21103,google/t5-efficient-base-ff1000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
21104,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
21105,Milos/slovak-gpt-j-405M,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0447631202017267,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
21106,KoichiYasuoka/roberta-base-ukrainian,T5-Efficient-LARGE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el8,0.0941644012928009,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
21107,google/t5-efficient-tiny-nl24,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0208598617464304,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
21108,sonoisa/byt5-small-japanese,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0890934765338897,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
21109,hfl/rbtl3,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
21110,amazon/bort,Paper: SciFive: a text-to-text transformer model for biomedical literatureAuthors: Long N,razent/SciFive-large-Pubmed,0.0865160636603832,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21111,cammy/led-base-16384-100-MDS,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0946709737181663,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
21112,mimicheng/codeparrot-ds,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0131564047187566,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
21113,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.031499657779932,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
21114,mwesner/bart-mlm,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0149203557521104,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
21115,google/t5-efficient-large-dm2000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0119909085333347,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
21116,google/t5-efficient-base-el6,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.061713384464383,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
21117,crabz/distil-slovakbert-ner,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0904161930084228,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
21118,google/t5-efficient-tiny-nl16,This does not make a very readable text,pere/DeUnCaser,0.0686511062085628,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
21119,gilparmentier/pokemon_gptj_model,The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1,nyu-mll/roberta-med-small-1M-3,0.0250813569873571,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
21120,bookbot/distil-wav2vec2-adult-child-cls-37m,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-adult-child-cls-37m,1.0,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
21121,google/t5-efficient-xl-nl4,The checkpoint used here is the original MobileBert Optimized Uncased English: (uncased_L-24_H-128_B-512_A-4_F-4_OPT) checkpoint,mrm8488/mobilebert-uncased-finetuned-squadv2,0.0893244817852974,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
21122,google/t5-efficient-tiny-nh16,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
21123,google/bert_uncased_L-10_H-128_A-2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
21124,DeepPavlov/distilrubert-tiny-cased-conversational-v1,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0414240844547748,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
21125,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0904876589775085,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
21126,google/t5-efficient-large-el8,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.069425731897354,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
21127,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el6,0.0691456422209739,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
21128,amazon/bort,This version of the checkpoint is lightweight since it is meant to be finetuned for classification and excludes the output embedding weights,google/rembert,0.0286436704918742,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21129,google/t5-efficient-tiny-nl12,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.027879943139851,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
21130,google/t5-efficient-base-ff6000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0485271681100129,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
21131,google/t5-efficient-small-dm256,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0350154601037501,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
21132,google/t5-efficient-base-nh16,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0440078033134341,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
21133,allegro/plt5-large,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0003514637549718,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
21134,DemangeJeremy/4-sentiments-with-flaubert,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0731555446982383,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21135,google/t5-efficient-large-kv256,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0660311207175254,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
21136,google/t5-efficient-large-dm2000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0423500332981347,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
21137,hf-internal-testing/tiny-random-rembert,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0962359905242919,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
21138,google/t5-efficient-small-el8,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0497427908703684,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
21139,google/t5-efficient-base-nl4,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0697547569870948,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
21140,mrp/simcse-model-wangchanberta,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0741795878857373,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
21141,crabz/distil-slovakbert-ner,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0509324222803115,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
21142,google/t5-efficient-small-nl20,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0424387082457542,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
21143,allenai/macaw-answer-11b,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.047779702891906,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
21144,google/t5-efficient-base-nh32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0379404593259095,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
21145,antoiloui/belgpt2,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0120237469673156,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
21146,mwesner/bart-mlm,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0809685215353965,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
21147,KoichiYasuoka/roberta-base-ukrainian,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.089407667517662,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
21148,google/byt5-large,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0235970318317413,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
21149,SupriyaArun/squeezebert-uncased-finetuned-squad,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0365079343318939,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
21150,uer/bart-chinese-4-768-cluecorpussmall,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0382688865065573,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
21151,sentence-transformers/paraphrase-MiniLM-L3-v2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0919421017169952,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
21152,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
21153,google/t5-efficient-large-nl16,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
21154,KoichiYasuoka/roberta-base-ukrainian,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
21155,junnyu/roformer_base_wwm_cluecorpussmall,Masked language modeling(MLM) and sentence order prediction(SOP) are used as training task,junnyu/roformer_base_wwm_cluecorpussmall,1.0,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
21156,IlyaGusev/rugpt3medium_sum_gazeta,The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0669851563870906,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
21157,stas/mt5-tiny-random,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
21158,superspray/electra_large_discriminator_squad2_custom_dataset,Note that the architecture of this model follows the original BERT,arm-on/BERTweet-FA,0.0504758022725582,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
21159,google/t5-efficient-tiny-ff6000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0164028825238347,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
21160,rdenadai/BR_BERTo,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
21161,sentence-transformers/paraphrase-MiniLM-L3-v2,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0365246869623661,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
21162,deep-learning-analytics/triviaqa-t5-base,The model was trained for 135 epochs using a batch size of 32 and learning rate of 1e-3,deep-learning-analytics/triviaqa-t5-base,1.0,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
21163,google/t5-efficient-tiny-nh32,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0760896205902099,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
21164,google/t5-efficient-base-nl24,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0593370962888001,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
21165,uer/t5-v1_1-base-chinese-cluecorpussmall,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0408295821398496,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
21166,patrickvonplaten/unispeech-sat-large-timit-ft,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0514978095889091,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
21167,tuner007/pegasus_qa,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.068885751068592,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
21168,google/t5-efficient-small-kv128,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0584077378734946,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
21169,razent/SciFive-base-PMC,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0009166072122752,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
21170,google/t5-efficient-large-nh8-nl32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0117949172854423,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
21171,google/t5-efficient-large-nh2,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0568965375423431,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
21172,DemangeJeremy/4-sentiments-with-flaubert,For best results use python as below,pszemraj/led-large-book-summary,0.0617721658200025,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21173,google/t5-efficient-base-el6,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0027250628918409,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
21174,google/t5-efficient-small-dm768,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0031777038238942,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
21175,hfl/rbt3,Colab: linkEvaluation script: evaluate,IlyaGusev/rugpt3medium_sum_gazeta,0.0834552943706512,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
21176,stas/pegasus-cnn_dailymail-tiny-random,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0101143978536129,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
21177,celine98/canine-s-finetuned-sst2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0074090883135795,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
21178,clue/roberta_chinese_3L312_clue_tiny,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0018365168944001,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
21179,google/t5-efficient-xl-nl6,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0379374884068965,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
21180,Norod78/hebrew_poetry-gpt_neo-tiny,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0861269421875476,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
21181,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl32,0.087351270020008,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
21182,celine98/canine-s-finetuned-sst2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0635140091180801,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
21183,google/t5-efficient-base-el6,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0079656187444925,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
21184,bookbot/distil-wav2vec2-adult-child-cls-52m,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0415579071268439,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
21185,google/t5-efficient-base-nl4,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0924191139638423,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
21186,stas/pegasus-cnn_dailymail-tiny-random,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0172664793208241,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
21187,hfl/rbtl3,We used the T5-base model for this model,flax-community/bengali-t5-base,0.0674011632800101,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
21188,google/t5-efficient-base-ff6000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0545889064669608,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
21189,google/t5-efficient-base-nl2,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0306313950568437,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
21190,skt/ko-gpt-trinity-1.2B-v0.5,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0840886607766151,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
21191,mldmm/GlassBERTa,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
21192,google/t5-efficient-base-nl4,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0509900897741317,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
21193,IDEA-CCNL/Zhouwenwang-Unified-110M,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
21194,google/reformer-crime-and-punishment,"It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed",phueb/BabyBERTa-2,0.019941403530538,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
21195,google/t5-efficient-tiny-nl16,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0489968452602624,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
21196,ncduy/phobert-large-finetuned-vietnamese_students_feedback,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0569222196936607,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
21197,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.0346257239580154,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
21198,google/t5-efficient-base-el8,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0787669941782951,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
21199,skt/ko-gpt-trinity-1.2B-v0.5,"It supports sequences of length up to 4,096! Longformer uses a combination of a sliding window (local) attention and global attention",mrm8488/longformer-base-4096-spanish,0.0841057561337947,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
21200,aubmindlab/aragpt2-large,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-large,1.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
21201,tau/splinter-large-qass,The model is case-sensitive,tau/splinter-large-qass,1.0,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
21202,DemangeJeremy/4-sentiments-with-flaubert,XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective,xlnet-large-cased,0.0566671164706349,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21203,razent/SciFive-base-PMC,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0848752036690711,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
21204,google/t5-efficient-small-ff9000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0409012306481599,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
21205,aubmindlab/aragpt2-large,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
21206,amazon/bort,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21207,hf-internal-testing/tiny-random-rembert,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0680087357759475,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
21208,google/t5-efficient-base-ff12000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0396551173180341,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
21209,mimicheng/codeparrot-ds,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
21210,google/t5-efficient-small-nl24,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0096808224916457,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
21211,allenai/macaw-3b,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0319367988655964,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
21212,mmaguero/gn-bert-tiny-cased,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0816192291676998,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
21213,mikaelsouza/msft-regular-model,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
21214,google/t5-efficient-small-el8,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.055769147351384,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
21215,google/t5-efficient-small-ff9000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0244810907170176,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
21216,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
21217,CuongLD/wav2vec2-large-xlsr-vietnamese,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0406779926270245,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
21218,hajime9652/xlnet-japanese,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,0.0661081224679946,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
21219,google/t5-efficient-small-dm2000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0478784963488578,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
21220,microsoft/layoutlm-base-uncased,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0308516100049018,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
21221,google/t5-efficient-tiny-nh32,Its original repository can be found here,tau/splinter-large-qass,0.0623620133846997,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
21222,google/t5-efficient-large-dm512,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0306693324819207,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
21223,google/t5-efficient-xl-nl8,This does not make a very readable text,pere/DeUnCaser,0.0612447224557399,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
21224,google/t5-efficient-large-dm256,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0326264519244431,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
21225,google/t5-efficient-small-el4,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0833356268703937,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
21226,wukevin/tcr-bert,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0293558398261666,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
21227,google/t5-efficient-xl-nl12,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0886902287602424,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
21228,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
21229,khizon/distilbert-unreliable-news-eng-4L,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0357404425740241,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
21230,google/t5-efficient-base-nh16,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-nh16,1.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
21231,mikaelsouza/msft-regular-model,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0680143013596534,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
21232,google/t5-efficient-tiny-nh8,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.090535283088684,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
21233,sentence-transformers/paraphrase-MiniLM-L3-v2,"This model is the fine-tuned version of ""DeBERTa v2 XLarge""  using CUAD dataset https://huggingface",akdeniz27/deberta-v2-xlarge-cuad,0.0915182009339332,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
21234,hfl/rbt3,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0360971670597791,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
21235,google/bert_uncased_L-8_H-768_A-12,I have made no attempts to censor,bigjoedata/rockbot,0.0217506180827816,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
21236,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff12000,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21237,google/t5-efficient-small-nl48,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0320091350004077,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
21238,lgrobol/electra-minuscule-discriminator,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0750813782215118,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
21239,google/t5-efficient-large-el2,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
21240,IlyaGusev/rugpt3medium_sum_gazeta,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0704797953367233,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
21241,google/t5-efficient-large-dm512,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.082413800060749,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
21242,google/t5-efficient-tiny-nl2,T5-Efficient-TINY-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl2,1.0,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
21243,google/t5-efficient-large-nh2,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nh2,1.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
21244,suzuki/distilbert-base-uncased-finetuned-squad,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
21245,google/t5-efficient-base-dm1000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0566152585670351,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
21246,uer/t5-v1_1-small-chinese-cluecorpussmall,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0573356319218873,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
21247,google/t5-efficient-tiny-nl24,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
21248,TJMUCH/transcriptome-iseeek,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,0.0499085262417793,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
21249,mrm8488/deberta-v3-small-finetuned-mrpc,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0645443350076675,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
21250,microsoft/layoutlm-large-uncased,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0871477276086807,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
21251,mmaguero/gn-bert-tiny-cased,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0575209688395261,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
21252,tartuNLP/gpt-4-est-base,"Colab demoFor training data was prepended with a text domain tag, and it should be added as prefix when using the model: &gt;general&lt;, &gt;web&lt;, &gt;news&lt;, &gt;doaj&lt; and &gt;wiki&lt; (standing for general texts, web crawled texts, news, article abstracts and wikipedia texts)",tartuNLP/gpt-4-est-base,1.0,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
21253,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0669182911515235,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
21254,google/t5-efficient-large-nl16,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
21255,MoritzLaurer/MiniLM-L6-mnli,"The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models",MoritzLaurer/MiniLM-L6-mnli,1.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
21256,google/t5-efficient-base-kv32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0425841398537158,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
21257,jkeruotis/LitBERTa-uncased,This is an implementation of CPT-Base,fnlp/cpt-base,0.0539337396621704,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
21258,google/t5-efficient-small-dm1000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0585504677146672,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
21259,akdeniz27/deberta-v2-xlarge-cuad,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0077703399583697,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
21260,rahulacj/bertweet-base-finetuned-sentiment-analysis,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0157181471586227,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
21261,google/t5-efficient-small-nl22,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0869112014770507,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
21262,google/t5-efficient-large-el8,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0970539674162864,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
21263,google/t5-efficient-small-dm768,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0596996927633881,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
21264,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.032549200579524,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
21265,eli4s/Bert-L12-h240-A12,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
21266,DemangeJeremy/4-sentiments-with-flaubert,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0067268945276737,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21267,google/t5-efficient-xxl-nl4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0779960416257381,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
21268,google/t5-efficient-large-nh8,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0774606373161077,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
21269,google/t5-efficient-base-dm256,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
21270,google/t5-efficient-small-el8,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0635889042168855,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
21271,google/t5-efficient-large-kv128,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.058163484558463,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
21272,google/t5-efficient-base-dm1000,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0269570779055357,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
21273,google/t5-efficient-base-kv16,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0458513433113693,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
21274,IDEA-CCNL/Zhouwenwang-Unified-1.3B,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0430592641234397,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
21275,clue/roberta_chinese_3L312_clue_tiny,This model was trained on TPU and the details are as follows:With the help ofHuggingface-Transformers 2.5,clue/roberta_chinese_3L312_clue_tiny,1.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
21276,uer/t5-v1_1-base-chinese-cluecorpussmall,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
21277,google/t5-efficient-base-nh8,Its original repository can be found here,tau/splinter-large-qass,0.0616283733397721,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
21278,dhpollack/distilbert-dummy-sentiment,This does not make a very readable text,pere/DeUnCaser,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
21279,google/t5-efficient-large-dm2000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
21280,brad1141/Longformer-finetuned-norm,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
21281,nyu-mll/roberta-base-10M-3,We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens),nyu-mll/roberta-base-10M-3,1.0,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
21282,google/t5-efficient-small-el64,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0720650348812341,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
21283,google/t5-efficient-large-nl12,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0197786376811564,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
21284,SupriyaArun/squeezebert-uncased-finetuned-squad,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0309432093054056,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
21285,suzuki/distilbert-base-uncased-finetuned-squad,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0986285880208015,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
21286,negfir/distilbert-base-uncased-finetuned-squad,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0588794164359569,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
21287,google/t5-efficient-small-el4,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
21288,lgrobol/electra-minuscule-generator,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.0474028773605823,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
21289,hfl/chinese-electra-180g-small-generator,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0476264047125975,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
21290,google/t5-efficient-base-nl2,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0108709344640373,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
21291,google/t5-efficient-small-el8,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-el8,1.0,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
21292,negfir/distilbert-base-uncased-finetuned-cola,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0615159310400486,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
21293,hajime9652/xlnet-japanese,We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric,andrejmiscic/simcls-scorer-billsum,0.0794077813625335,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
21294,suzuki/distilbert-base-uncased-finetuned-squad,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0463449731469154,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
21295,groar/gpt-neo-1.3B-finetuned-escape2,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0081489188596606,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
21296,google/t5-efficient-large-dm2000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.044697331264615,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
21297,google/t5-efficient-large-dm512,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
21298,bigjoedata/rockbot,T5-Efficient-TINY-NH1 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh1,0.018039031419903,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
21299,google/t5-efficient-base-ff12000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0792109258472918,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
21300,google/t5-efficient-small-dm2000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.064212929457426,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
21301,google/t5-efficient-small-nl40,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0599046759307383,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
21302,fnlp/cpt-base,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0035802009515464,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
21303,Norod78/hebrew_poetry-gpt_neo-tiny,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0639244951307773,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
21304,TJMUCH/transcriptome-iseeek,Note: flaubert-small-cased is partially trained so performance is not guaranteed,flaubert/flaubert_small_cased,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
21305,google/t5-efficient-base-nh8,This does not make a very readable text,pere/DeUnCaser,0.0225536152720451,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
21306,lgrobol/electra-minuscule-generator,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
21307,hfl/chinese-xlnet-mid,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
21308,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0975241959095001,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
21309,allenai/macaw-answer-11b,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0581611034770806,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
21310,enelpol/czywiesz-context,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0035543204950434,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
21311,google/t5-efficient-large-kv16,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
21312,google/t5-efficient-mini-nl12,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0360118802636861,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
21313,google/t5-efficient-tiny-nh16,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0375278368592262,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
21314,google/t5-efficient-small-el12,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0263185063377022,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
21315,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,It can be considered as tiny copy of Conversational DistilRuBERT-small,DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0914287840326626,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
21316,google/t5-efficient-small-kv128,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0541504519060253,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
21317,google/t5-efficient-base-el8,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0550982803106307,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
21318,ToToKr/kobigbird-bert-base-finetuned-klue,Source language: English,NYTK/translation-marianmt-en-hu,0.097499243915081,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
21319,hfl/rbt3,T5-Efficient-XL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl2,0.0578570570796727,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
21320,rinna/japanese-gpt-1b,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0691183432936667,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
21321,google/t5-efficient-base-kv256,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0402351007796823,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
21322,microsoft/layoutlm-base-cased,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0519517138600349,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
21323,sentence-transformers/paraphrase-MiniLM-L3-v2,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0685984343290329,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
21324,amazon/bort,"Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context",xlnet-large-cased,0.0157143250107765,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21325,stas/pegasus-cnn_dailymail-tiny-random,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0691165421158075,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
21326,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
21327,google/t5-efficient-small-ff6000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0402461905032396,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
21328,google/t5-efficient-large-dm256,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
21329,google/t5-efficient-small-nl20,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0637246239930391,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
21330,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-NH2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh2,0.067323522021373,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
21331,google/t5-efficient-small-dm1000,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0558061264455318,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
21332,TJMUCH/transcriptome-iseeek,"Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan BaskaranWork done via MLDMM Lab",mldmm/GlassBERTa,0.0460444204509258,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
21333,rinna/japanese-gpt-1b,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
21334,google/t5-efficient-large-kv32,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0846637822687625,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
21335,TJMUCH/transcriptome-iseeek,You should fine-tune this model for your downstream task,google/rembert,0.090320698916912,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
21336,mrm8488/bert-small-finetuned-squadv2,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0714170671999454,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
21337,cosmoquester/bart-ko-base,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0543397398044665,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
21338,google/t5-efficient-large-nl16,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.070125613361597,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
21339,razent/SciFive-large-Pubmed,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0330948829650878,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
21340,google/t5-efficient-base-ff1000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
21341,nherve/flaubert-oral-ft,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0472638656695683,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
21342,google/t5-efficient-large-nh4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0297169554978608,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
21343,everdoubling/byt5-Korean-small,"If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:",nherve/flaubert-oral-ft,0.0283074878156185,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
21344,cammy/led-large-16384-arxiv-100-MDS,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
21345,google/t5-efficient-large-el6,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
21346,google/t5-efficient-base-nl40,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0344773158431052,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
21347,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0033648048217097,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
21348,google/t5-efficient-small-dm2000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0192143497988581,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
21349,google/t5-efficient-large-el8,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0184939559549093,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
21350,google/t5-efficient-small-ff3000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.033748242072761,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
21351,kssteven/ibert-roberta-base,", MatMul, GELU, Softmax, and LayerNorm) with closely approximating integer operations",kssteven/ibert-roberta-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
21352,google/t5-efficient-base-kv128,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0612857826054095,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
21353,IDEA-CCNL/Erlangshen-Longformer-110M,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0499359127134084,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
21354,google/t5-efficient-base-nh24,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0473319720476865,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
21355,ncduy/phobert-large-finetuned-vietnamese_students_feedback,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0887347534298896,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
21356,dbmdz/bert-medium-historic-multilingual-cased,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0304822865873575,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
21357,google/t5-efficient-large-el6,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0486771026626229,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
21358,google/t5-efficient-large-nh12,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0616761352866887,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
21359,google/t5-efficient-xl-nl2,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0735552683472633,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
21360,google/t5-efficient-base-el8,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0691947601735591,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
21361,google/t5-efficient-base-kv16,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
21362,dhpollack/distilbert-dummy-sentiment,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0546515211462974,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
21363,Iskaj/hf-challenge-test,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0604766234755516,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
21364,google/t5-efficient-tiny-nh16,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0147146806120872,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
21365,minimaxir/magic-the-gathering,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0857155099511146,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
21366,google/t5-efficient-large-kv32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
21367,KoichiYasuoka/roberta-base-ukrainian,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
21368,mrm8488/deberta-v3-small-finetuned-mrpc,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0461074635386467,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
21369,google/t5-efficient-small-kv32,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0587999364361166,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
21370,google/t5-efficient-large-kv256,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0092621166259049,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
21371,google/t5-efficient-small-nl24,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0315978974103927,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
21372,GKLMIP/electra-myanmar-small-uncased,The training code is available here: SBERT,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0657275673002004,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
21373,zoha/wav2vec2-base-timit-demo-colab,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0348057970404624,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
21374,hfl/chinese-electra-180g-small-ex-discriminator,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0612745142231384,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
21375,microsoft/layoutlm-base-uncased,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0544680058956146,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
21376,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,1.0,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
21377,mwesner/reformer-clm,T5-Efficient-SMALL-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl40,0.0900835022330284,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
21378,rmihaylov/pegasus-base-cnn-dailymail-bg,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0469927799422293,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
21379,mimicheng/codeparrot-ds,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0588408857583999,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
21380,microsoft/layoutlm-large-uncased,It was introduced in this paper and first released on this page,THUMT/mGPT,0.025271413847804,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
21381,rahulacj/bertweet-base-finetuned-sentiment-analysis,"Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint",KBLab/megatron-bert-large-swedish-cased-165k,0.0912151411175727,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
21382,crabz/distil-slovakbert-ner,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0566172674298286,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
21383,microsoft/layoutlm-large-uncased,Its original repository can be found here,tau/splinter-large-qass,0.0014627575874328,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
21384,anirudh21/xlnet-base-cased-finetuned-rte,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0611882768571376,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
21385,microsoft/layoutlm-base-cased,"How do I pronounce the name of the model? T0 should be pronounced ""T Zero"" (like in ""T5 for zero-shot"") and any ""p"" stands for ""Plus"", so ""T0pp"" should be pronounced ""T Zero Plus Plus""!T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller",saurkulsh/T0pp,0.0302004627883434,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
21386,amazon/bort,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0709938928484916,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21387,google/t5-efficient-base-nl36,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0599405299872159,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
21388,google/t5-efficient-tiny-ff3000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0049834363162517,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
21389,google/t5-efficient-tiny-ff9000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0378295462578534,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
21390,google/t5-efficient-large-nl10,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0441952515393495,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
21391,hfl/rbtl3,T5-Efficient-BASE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl32,0.0448424331843853,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
21392,qarib/bert-base-qarib_far_6500k,Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team,funnel-transformer/small,0.0157306652981788,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
21393,mikaelsouza/msft-regular-model,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0982569083571434,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
21394,google/t5-efficient-small-nl36,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0437602903693914,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
21395,VietAI/gpt-neo-1.3B-vietnamese-news,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0534781366586684,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
21396,anas-awadalla/bert-tiny-finetuned-squad,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0865501165390014,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
21397,google/t5-efficient-large-nl32,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0781960580497979,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
21398,1one5ome/gpt2-chinese-gulong,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0111548798158764,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
21399,google/t5-efficient-mini-nl6,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0383705864660441,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
21400,superspray/electra_large_discriminator_squad2_custom_dataset,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0977469012141227,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
21401,google/t5-efficient-large-kv16,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
21402,google/t5-efficient-small-el8,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
21403,google/t5-efficient-small-ff9000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0635668151080607,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
21404,tartuNLP/gpt-4-est-large,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0849046856164932,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
21405,akdeniz27/deberta-v2-xlarge-cuad,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0294043142348527,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
21406,mgreenbe/bertlet-base-uncased-for-sequence-classification,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0534650012850761,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
21407,allenai/wmt19-de-en-6-6-base,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0152367018163204,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
21408,google/t5-efficient-mini-nl12,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0040654949843883,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
21409,rahulacj/bertweet-base-finetuned-sentiment-analysis,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.064229167997837,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
21410,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.09219990670681,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
21411,antoiloui/belgpt2,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0373503817245364,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
21412,IDEA-CCNL/Zhouwenwang-Unified-110M,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
21413,izumi-lab/bert-small-japanese-fin,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0483375309656063,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
21414,google/t5-efficient-tiny-nl32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0075346268713474,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
21415,pulp/CHILDES-ParentBERTo,T5-Efficient-MINI-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl6,0.0840793401002883,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
21416,google/t5-efficient-xl-nl8,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0924478285014629,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
21417,google/t5-efficient-large-nh24,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0125147141516209,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
21418,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
21419,TJMUCH/transcriptome-iseeek,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0188242606818676,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
21420,google/t5-efficient-small-el8,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0678350366652011,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
21421,google/t5-efficient-large-kv32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-kv32,1.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
21422,flboehm/reddit-bert-text_5,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
21423,google/t5-efficient-tiny-ff3000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0297688329592346,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
21424,stas/pegasus-cnn_dailymail-tiny-random,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
21425,google/t5-efficient-tiny-nh32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0021427348256111,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
21426,google/t5-efficient-base-dm2000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0440210700035094,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
21427,google/byt5-large,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-large,1.0,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
21428,google/t5-efficient-base-nl40,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.068141397088766,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
21429,google/t5-efficient-small-ff3000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0049740616232156,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
21430,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0453988835215568,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
21431,google/t5-efficient-small-el8,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0450034178793429,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
21432,google/t5-efficient-xl-nl16,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0773944780230522,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
21433,google/t5-efficient-base-nl24,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.057600513100624,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
21434,hfl/chinese-electra-180g-small-generator,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0558131368209918,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
21435,google/t5-efficient-small-kv32,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0857413858175277,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
21436,suzuki/distilbert-base-uncased-finetuned-squad,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0029341373592615,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
21437,nherve/flaubert-oral-asr,T5-Efficient-BASE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv128,0.0438375808298587,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
21438,kyryl0s/gpt2-uk-xxs,The learned pattern probabilities can be used for de novo SMILES generation,mrm8488/chEMBL_smiles_v1,0.0459176711738109,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
21439,google/t5-efficient-large-nh8-nl32,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0630529467016458,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
21440,SupriyaArun/squeezebert-uncased-finetuned-squad,This project was funded by the Language Technology Programme for Icelandic 2019-2023,jonfd/convbert-small-igc-is,0.0651826784014701,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
21441,google/t5-efficient-base-kv16,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0195505060255527,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
21442,junnyu/roformer_base_wwm_cluecorpussmall,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0674572711189587,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
21443,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0287804678082466,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
21444,google/t5-efficient-tiny-ff3000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0586031954735517,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
21445,google/t5-efficient-mini,Its original repository can be found here,tau/splinter-large-qass,0.046982618048787,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
21446,hajime9652/xlnet-japanese,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,0.06409253180027,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
21447,jweb/japanese-soseki-gpt2-1b,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
21448,ctoraman/RoBERTa-TR-medium-wp-66k,Pretrained model on Turkish language using a masked language modeling (MLM) objective,ctoraman/RoBERTa-TR-medium-wp-66k,1.0,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
21449,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0823783352971077,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
21450,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0533720515668392,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
21451,MoritzLaurer/MiniLM-L6-mnli,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0013487571850418,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
21452,Sindhu/muril-large-squad2,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.003862130921334,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
21453,stas/t5-very-small-random,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0008900053799152,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
21454,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
21455,google/t5-efficient-small-dm2000,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0351796075701713,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
21456,google/t5-efficient-mini-nl6,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0490518566220997,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
21457,google/t5-efficient-small-dm2000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0767426304519175,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
21458,google/t5-efficient-tiny-nl24,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0543187214061617,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
21459,google/t5-efficient-small-el12,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0467178095132112,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
21460,amazon/bort,You can use this model directly with a pipeline for text generation,flax-community/gpt2-medium-persian,0.0768795441836118,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21461,microsoft/layoutlm-large-uncased,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0975527465343475,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
21462,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0993266925215721,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
21463,DTAI-KULeuven/robbertje-1-gb-bort,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0671915523707866,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
21464,google/t5-efficient-large-kv128,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0138428099453449,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
21465,google/t5-efficient-small-nl36,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0086903609335422,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
21466,kyryl0s/gpt2-uk-xxs,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
21467,google/t5-efficient-base-ff6000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0240143984556197,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
21468,google/t5-efficient-tiny-ff9000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0670705288648604,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
21469,cammy/led-large-16384-arxiv-100-MDS,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0792413055896759,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
21470,hajime9652/xlnet-japanese,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0179208777844905,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
21471,google/t5-efficient-base-kv128,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0466322340071201,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
21472,google/t5-efficient-base-el16,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.044169211294502,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
21473,google/t5-efficient-small-el64,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
21474,google/t5-efficient-small-el12,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
21475,allenai/macaw-answer-11b,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0319367988655964,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
21476,sberbank-ai/rugpt3medium_based_on_gpt2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
21477,KoichiYasuoka/roberta-base-ukrainian,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0367770418524742,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
21478,google/t5-efficient-tiny-nl2,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.091782584786415,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
21479,google/t5-efficient-tiny-nl6,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0573953129351138,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
21480,mgreenbe/bertlet-base-uncased-for-sequence-classification,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
21481,google/t5-efficient-base-dm512,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
21482,google/t5-efficient-xl-nl28,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0670707672834395,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
21483,microsoft/unihanlm-base,"We conduct extensive experiments on a variety of Chinese and Japanese NLP benchmarks, showing that our proposed UnihanLM is effective on both mono- and cross-lingual Chinese and Japanese tasks, shedding light on a new path to exploit the homology of languages",microsoft/unihanlm-base,1.0,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
21484,ian0delond/model-test,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0895698294043541,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
21485,google/t5-efficient-small-dm2000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
21486,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
21487,NovelAI/genji-python-6B,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
21488,google/t5-efficient-base-ff9000,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0480540171265601,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
21489,SetFit/deberta-v3-large__sst2__train-8-8,Its original repository can be found here,tau/splinter-large-qass,0.0887067466974258,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
21490,uer/t5-v1_1-small-chinese-cluecorpussmall,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0123561741784215,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
21491,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0963499248027801,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
21492,google/t5-efficient-xl-nl16,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0493560917675495,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
21493,mrm8488/chEMBL_smiles_v1,"You can see the detailed process of gathering the data, preprocess it and train the LSTM in their repo",mrm8488/chEMBL_smiles_v1,1.0,-1.0,NLP,919d68f1-5654-4b4c-a9c7-f59f67ababe7___chEMBL_smiles_v1,HF
21494,zoha/wav2vec2-base-timit-demo-colab,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0691704303026199,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
21495,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0577082224190235,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
21496,stas/mt5-tiny-random,"Besides, many tasks may be formatted such that the output is directly generated in natural language",asi/gpt-fr-cased-base,0.0778519511222839,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
21497,eli4s/Bert-L12-h240-A12,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0292479733470827,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
21498,google/t5-efficient-small-dm128,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0793586410582065,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
21499,brad1141/Longformer-finetuned-norm,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
21500,brad1141/Longformer-finetuned-norm,"Pre-Training with Whole Word Masking for Chinese BERTYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping HuThis repository is developed based on：https://github",hfl/rbtl3,0.0928524658083915,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
21501,gayanin/bart-mlm-pubmed-medterm,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.064383290708065,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
21502,beston91/gpt2-xl_ft_logits_10k,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.00205048173666,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
21503,google/t5-efficient-base-dm512,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0508106630295514,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
21504,google/t5-efficient-mini-nl6,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0357375666499137,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
21505,stas/pegasus-cnn_dailymail-tiny-random,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0088568376377225,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
21506,DemangeJeremy/4-sentiments-with-flaubert,Slovak GPT-J-162M is the first model released in Slovak GPT-J series and the very first publicly available transformer trained predominantly on Slovak corpus,Milos/slovak-gpt-j-162M,0.0568506196141242,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21507,hfl/rbt3,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.025151289999485,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
21508,google/t5-efficient-large-kv16,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0494858459569513,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
21509,google/t5-efficient-large-nh32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
21510,google/t5-efficient-small-ff12000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0817610062658786,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
21511,tartuNLP/gpt-4-est-large,"In addition to cross entropy and cosine teacher-student losses, DistilProtBert was pretrained on a masked language modeling (MLM) objective  and it only works with capital letter amino acids",yarongef/DistilProtBert,0.0693566352128982,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
21512,diper1998/distilgpt2-finetuned-AT,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0584254674613475,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
21513,SupriyaArun/squeezebert-uncased-finetuned-squad,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0607367604970932,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
21514,KoichiYasuoka/roberta-base-ukrainian,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0294208005070686,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
21515,uer/t5-v1_1-base-chinese-cluecorpussmall,This is a sentence-transformers model: It maps sentences and paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search,sentence-transformers/paraphrase-MiniLM-L3-v2,0.0485236383974551,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
21516,akdeniz27/deberta-v2-xlarge-cuad,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
21517,google/t5-efficient-large-nh4,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
21518,piotr-rybak/poleval2021-task4-herbert-large-encoder,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
21519,google/t5-efficient-xl-nl28,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0070220176130533,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
21520,sberbank-ai/rugpt3large_based_on_gpt2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0189835773780941,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
21521,microsoft/unihanlm-base,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,1.0,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
21522,qarib/bert-base-qarib_far_6500k,Only use this model for retraining and mask-filling,jcblaise/electra-tagalog-small-cased-generator,0.014928225427866,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
21523,google/t5-efficient-base-ff12000,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0532972272485494,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
21524,amazon/bort,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0283676451072096,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21525,bookbot/distil-wav2vec2-adult-child-cls-52m,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-adult-child-cls-52m,1.0,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
21526,fnlp/bart-large-chinese,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.0849195457994937,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
21527,google/t5-efficient-large-el2,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0437226388603448,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
21528,Iskaj/hf-challenge-test,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
21529,google/t5-efficient-large-nh8,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
21530,microsoft/layoutlm-base-uncased,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
21531,mgreenbe/bertlet-base-uncased-for-sequence-classification,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0284418798983097,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
21532,ToToKr/kobigbird-bert-base-finetuned-klue,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0887682288885116,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
21533,sberbank-ai/rugpt3medium_based_on_gpt2,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
21534,amazon/bort,Pretrained T5 super-super-tiny language model for Malay,malay-huggingface/t5-super-super-tiny-bahasa-cased,0.0558395646512507,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21535,google/t5-efficient-large-nh12,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0967625156044959,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
21536,uer/t5-v1_1-small-chinese-cluecorpussmall,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0143596809357404,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
21537,google/t5-efficient-tiny-nh32,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0504981558769941,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
21538,dbmdz/convbert-base-german-europeana-cased,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.060620109240214,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
21539,google/t5-efficient-xxl-nl4,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0587696693837642,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
21540,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0696942657232284,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
21541,microsoft/layoutlm-base-cased,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0605222806334495,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
21542,eli4s/Bert-L12-h384-A6,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0083841877058148,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
21543,microsoft/layoutlm-large-uncased,t5-super-super-tiny-bahasa-cased model was pretrained on multiple tasks,malay-huggingface/t5-super-super-tiny-bahasa-cased,0.0784682780504226,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
21544,cammy/led-base-16384-100-MDS,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0697710067033767,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
21545,akdeniz27/deberta-v2-xlarge-cuad,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0320599302649498,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
21546,DemangeJeremy/4-sentiments-with-flaubert,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0593502335250376,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21547,superspray/electra_large_discriminator_squad2_custom_dataset,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0319223925471305,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
21548,akdeniz27/deberta-v2-xlarge-cuad,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0002392455935478,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
21549,groar/gpt-neo-1.3B-finetuned-escape2,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0729286298155784,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
21550,rdenadai/BR_BERTo,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0160050864020983,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
21551,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21552,google/t5-efficient-tiny-nh1,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0490487078204751,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
21553,google/t5-efficient-small-nl24,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0702542588114738,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
21554,zoha/wav2vec2-base-timit-demo-colab,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0902900770306587,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
21555,google/bert_uncased_L-4_H-128_A-2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
21556,google/t5-efficient-small-kv16,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0471051279455423,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
21557,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0308803953230381,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
21558,google/t5-efficient-small-kv16,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0201438702642917,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
21559,google/t5-efficient-small-el32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0510148610919713,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
21560,amazon/bort,"As shown in the Metrics section, the small version (5 times fewer parameters) performs slightly worse, while the medium version (3 times fewer parameters) outperforms the BETO based zero-shot classifier",Recognai/zeroshot_selectra_small,0.0697033889591693,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21561,mnaylor/bigbird-base-mimic-mortality,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0408522961661219,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
21562,google/t5-efficient-base-dm1000,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0712491795420646,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
21563,hfl/chinese-electra-180g-small-ex-generator,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0641145917276541,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
21564,aditeyabaral/sentencetransformer-bert-hinglish-small,This repository provides an extra-small-sized Japanese GPT-2 model,rinna/japanese-gpt2-xsmall,0.0545770153403282,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
21565,DemangeJeremy/4-sentiments-with-flaubert,They used a generative recurrent neural network (RNN) containing long short‐term memory (LSTM) cell to capture the syntax of molecular representations in terms of SMILES strings,mrm8488/chEMBL_smiles_v1,0.050753072835505,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21566,diper1998/distilgpt2-finetuned-AT,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0969995707273483,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
21567,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0460498891770839,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
21568,google/t5-efficient-mini-nl8,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0520953740924595,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
21569,sonoisa/byt5-small-japanese,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0243400707840919,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
21570,google/t5-efficient-tiny-ff3000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0547966491430997,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
21571,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.0835455358028411,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
21572,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,0.0446088053286075,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
21573,ai4bharat/MultiIndicHeadlineGeneration,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0643882819761832,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
21574,google/t5-efficient-base-ff12000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0090754088014364,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
21575,akdeniz27/deberta-v2-xlarge-cuad,"Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan BaskaranWork done via MLDMM Lab",mldmm/GlassBERTa,0.0886686295270919,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
21576,google/t5-efficient-xl-nl12,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0442963344976305,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
21577,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl6,0.0093884505331515,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21578,andrejmiscic/simcls-scorer-billsum,It is a two-stage approach consisting of a generator and a scorer,andrejmiscic/simcls-scorer-billsum,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
21579,TJMUCH/transcriptome-iseeek,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0090960683301091,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
21580,aditeyabaral/sentencetransformer-bert-hinglish-small,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
21581,uer/t5-v1_1-small-chinese-cluecorpussmall,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0438497066497802,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
21582,mimi/Waynehills-NLP-doogie,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0153243038803339,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
21583,razent/SciFive-large-Pubmed,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0622477978467941,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
21584,Milos/slovak-gpt-j-405M,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0260699714223543,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
21585,google/t5-efficient-base-kv32,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
21586,google/t5-efficient-large-nh8-nl32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0068306624889373,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
21587,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,0.0917111076414585,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
21588,google/t5-efficient-base-kv32,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0819418765604496,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
21589,google/t5-efficient-base-ff1000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0570533201098441,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
21590,mrp/simcse-model-wangchanberta,TransQuest outperforms current open-source quality estimation frameworks such as OpenKiwi and DeepQuest,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0935022681951522,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
21591,google/t5-efficient-base-kv32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0190542172640561,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
21592,1one5ome/gpt2-chinese-gulong,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0777263045310974,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
21593,hfl/rbtl3,Any bias held by the imdb dataset may be reflected in the model's output,lannelin/bert-imdb-1hidden,0.0732637178152799,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
21594,sentence-transformers/paraphrase-MiniLM-L3-v2,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0067683868110179,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
21595,google/t5-efficient-large-kv256,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0062521770596504,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
21596,google/t5-efficient-base-nl24,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.073845425620675,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
21597,google/t5-efficient-tiny-nl32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.047355594113469,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
21598,google/t5-efficient-large-nl32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
21599,Palak/microsoft_deberta-base_squad,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0093585029244422,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
21600,google/bert_uncased_L-8_H-128_A-2,The smaller BERT models are intended for environments with restricted computational resources,google/bert_uncased_L-8_H-128_A-2,1.0,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
21601,cammy/led-base-16384-100-MDS,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0798372775316238,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
21602,tau/tavbert-he,The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our Stack Overflow Dataset repository,UWB-AIR/MQDD-duplicates,0.0457640392705797,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
21603,microsoft/deberta-v2-xxlarge-mnli,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
21604,mikaelsouza/msft-regular-model,It was introduced in this paper,readerbench/RoBERT-small,0.0378130488097667,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
21605,hfl/rbtl3,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0976412333548068,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
21606,tobiaslee/bert-2l-768h-uncased,The hypothesis_template parameter is important and should be in Spanish,Recognai/zeroshot_selectra_small,0.0871145129203796,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
21607,IDEA-CCNL/Zhouwenwang-Unified-1.3B,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
21608,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0483804680407047,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
21609,google/t5-efficient-small-kv32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
21610,google/t5-efficient-base-nl2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0797657817602157,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
21611,danyaljj/opengpt2_pytorch_forward,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0476298568149407,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
21612,brad1141/Longformer-finetuned-norm,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0952343344688415,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
21613,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0621538236737251,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
21614,google/t5-efficient-large-nh2,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.006819425150752,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
21615,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0542099177837371,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
21616,google/t5-efficient-xl-nl4,"6,860,062 examples are extracted from below corpora",lassl/roberta-ko-small,0.0669412985444068,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
21617,amazon/bort,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0519119985401629,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21618,google/t5-efficient-large-dm128,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0909893848001956,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
21619,cross-encoder/nli-deberta-v3-xsmall,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,1.0,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
21620,google/t5-efficient-xl-nl16,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0087619889527559,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
21621,brad1141/Longformer-finetuned-norm,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0326501056551933,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
21622,IDEA-CCNL/Erlangshen-Longformer-330M,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,0.0689536724239587,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
21623,enelpol/czywiesz-context,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,1.0,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
21624,jcblaise/electra-tagalog-small-cased-generator,We open-source the model to allow greater usage within the Filipino NLP community,jcblaise/electra-tagalog-small-cased-generator,1.0,-1.0,NLP,f4e25246-ebec-4c97-8a1d-5ef296f2f888___electra-tagalog-small-cased-generator,HF
21625,google/t5-efficient-tiny-nl16,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0631385408341884,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
21626,nguyenvulebinh/envibert,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0224116407334804,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
21627,tau/tavbert-he,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
21628,google/t5-efficient-tiny-el8,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0476696267724036,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
21629,minimaxir/magic-the-gathering,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
21630,UWB-AIR/MQDD-duplicates,The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our Stack Overflow Dataset repository,UWB-AIR/MQDD-duplicates,1.0,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
21631,mwesner/bart-mlm,This model is a fine-tuned version of mwesner/bart-mlm on the CNN/Dailymail dataset,mwesner/bart-mlm,1.0,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
21632,google/t5-efficient-base-dm512,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0725532807409763,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
21633,microsoft/layoutlm-base-uncased,t5-super-super-tiny-bahasa-cased model was pretrained on multiple tasks,malay-huggingface/t5-super-super-tiny-bahasa-cased,0.0784682780504226,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
21634,Parsa/BBB_prediction_classification_SMILES,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.053615246588985,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
21635,google/t5-efficient-small-dm1000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0087027214467525,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
21636,google/t5-efficient-tiny-ff12000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-ff12000,1.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
21637,cross-encoder/ms-marco-MiniLM-L-2-v2,This model was trained on the MS Marco Passage Ranking task,cross-encoder/ms-marco-MiniLM-L-2-v2,1.0,-1.0,NLP,c2d1bb44-2e13-4b2e-8b5d-23fa4c9a88e9___ms-marco-MiniLM-L-2-v2,HF
21638,mimi/Waynehills-NLP-doogie,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
21639,google/t5-efficient-base-nl24,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0555451102554797,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
21640,google/t5-efficient-small-ff12000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-ff12000,1.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
21641,cosmoquester/bart-ko-small,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.0543397398044665,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
21642,google/t5-efficient-tiny-nl6,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0430866964161395,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
21643,SupriyaArun/squeezebert-uncased-finetuned-squad,For more information refer to SUPERB: Speech processing Universal PERformance Benchmark,superb/hubert-large-superb-sid,0.0969961434602737,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
21644,KoichiYasuoka/roberta-base-ukrainian,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
21645,stas/t5-very-small-random,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.024556066840887,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
21646,google/t5-efficient-tiny-nl16,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0610218569636344,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
21647,Palak/microsoft_deberta-base_squad,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0799015909433364,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
21648,google/t5-efficient-base-dm256,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0446387687698005,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
21649,antoiloui/belgpt2,I have made no attempts to censor,bigjoedata/rockbot,0.05002205632627,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
21650,suzuki/distilbert-base-uncased-finetuned-squad,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,0.0942725762724876,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
21651,google/t5-efficient-large-nl36,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0323446486145257,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
21652,lgrobol/electra-minuscule-discriminator,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
21653,google/t5-efficient-small-ff9000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0231882091611623,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
21654,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,T5-Efficient-BASE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv128,0.098055750131607,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
21655,google/t5-efficient-small-dm1000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0869964957237243,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
21656,google/t5-efficient-large-nh24,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
21657,allenai/wmt16-en-de-12-1,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0315275499597191,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
21658,microsoft/layoutlm-base-uncased,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0349774472415447,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
21659,mikaelsouza/msft-regular-model,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
21660,pulp/CHILDES-ParentBERTo,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
21661,razent/SciFive-large-Pubmed,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-8_H-768_A-12,0.0858411416411399,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
21662,mrm8488/bert-small-finetuned-squadv2,"It is also more accurate, especially when reinvesting the input embeddings' parameters into the core model, as is done on RemBERT",google/rembert,0.075863018631935,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
21663,aubmindlab/aragpt2-mega,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0453132800757884,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
21664,saibo/random-roberta-mini,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
21665,SetFit/deberta-v3-large__sst2__train-8-8,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0595319047570228,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
21666,google/t5-efficient-small-el16-dl4,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0680655874311923,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
21667,jweb/japanese-soseki-gpt2-1b,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0359152952829996,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
21668,fnlp/bart-large-chinese,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.0745555516332387,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
21669,IDEA-CCNL/Erlangshen-Longformer-110M,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,0.0004803091287613,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
21670,zoha/wav2vec2-base-timit-demo-colab,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0705956518650055,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
21671,eli4s/Bert-L12-h384-A6,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.054114419966936,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
21672,sentence-transformers/paraphrase-MiniLM-L3-v2,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0508784055709838,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
21673,nherve/flaubert-oral-ft,"They were trained with the FlauBERT software using the same parameters as the flaubert-base-uncased model (12 layers, 12 attention heads, 768 dims, 137M parameters, uncased)",nherve/flaubert-oral-ft,1.0,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
21674,tucan9389/kcbert-base-finetuned-squad,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0974025204777717,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
21675,google/t5-efficient-large-nl16,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
21676,google/t5-efficient-large-el6,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0271017383784056,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
21677,obss/mt5-base-3task-highlight-tquad2,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0848933830857276,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
21678,google/t5-efficient-tiny-nl32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0587513353675603,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
21679,GKLMIP/electra-myanmar-small-uncased,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0020279996097088,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
21680,google/t5-efficient-large-nl20,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0497857509180902,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
21681,google/t5-efficient-large-dm768,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0686496887356042,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
21682,skt/ko-gpt-trinity-1.2B-v0.5,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
21683,celine98/canine-s-finetuned-sst2,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0421977043151855,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
21684,kyryl0s/gpt2-uk-xxs,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0696685668081045,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
21685,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,I have made no attempts to censor,bigjoedata/rockbot,0.0259326174855232,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
21686,FremyCompany/xls-r-nl-v1-cv8-lm,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0368480198085307,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
21687,google/t5-efficient-small-kv32,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0805371142923831,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
21688,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv32,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21689,piotr-rybak/poleval2021-task4-herbert-large-encoder,The model was finetuned by jweb based on trained by rinna Co,jweb/japanese-soseki-gpt2-1b,0.0961795970797538,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
21690,google/t5-efficient-tiny-nl12,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0704203285276889,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
21691,Palak/microsoft_deberta-base_squad,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0766126438975334,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
21692,Gilles/FongBERT,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0317422673106193,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
21693,NovelAI/genji-python-6B,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0350911219914754,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
21694,google/t5-efficient-small-dm256,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0588955096900462,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
21695,TsinghuaAI/CPM-Generate,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,1.0,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
21696,google/t5-efficient-large-nl32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0316640175879001,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
21697,google/t5-efficient-small-nl36,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.081230092793703,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
21698,stas/t5-very-small-random,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
21699,jorge-henao/gpt2-small-spanish-disco-poetry-wt,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0428063422441482,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
21700,VietAI/gpt-neo-1.3B-vietnamese-news,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0538826473057269,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
21701,mmaguero/gn-bert-tiny-cased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0646005291491746,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
21702,lassl/gpt2-ko-small,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
21703,jnz/electra-ka,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
21704,saibo/random-roberta-tiny,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0354004558175801,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
21705,google/t5-efficient-small-ff6000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.011771410703659,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
21706,SupriyaArun/squeezebert-uncased-finetuned-squad,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0358703583478927,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
21707,google/t5-efficient-base-ff9000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0050942772068083,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
21708,patrickvonplaten/unispeech-sat-large-timit-ft,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.00028065033257,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
21709,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0791603475809097,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
21710,aditeyabaral/sentencetransformer-distilbert-hinglish-big,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0692375302314758,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
21711,TransQuest/monotransquest-hter-en_lv-it-nmt,"The quality estimation can be done at different levels: document level, sentence level and word level",TransQuest/monotransquest-hter-en_lv-it-nmt,1.0,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
21712,allenai/wmt19-de-en-6-6-big,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0082573629915714,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
21713,google/t5-efficient-small-ff12000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0612333416938781,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
21714,negfir/distilbert-base-uncased-finetuned-cola,This model is a fine-tuned version of negfir/uncased_L-12_H-128_A-2 on an unknown dataset,negfir/distilbert-base-uncased-finetuned-cola,1.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
21715,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0659822151064872,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
21716,aditeyabaral/sentencetransformer-bert-hinglish-small,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0166149102151393,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
21717,hajime9652/xlnet-japanese,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0920219421386718,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
21718,google/t5-efficient-large-nh8-nl32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
21719,google/t5-efficient-base-ff6000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0342761082574725,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
21720,google/t5-efficient-large-nl12,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0636149626225232,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
21721,stas/t5-very-small-random,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0073001347482204,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
21722,google/t5-efficient-large-nh8-nl32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0591011494398116,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
21723,google/t5-efficient-base-nl24,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0465389993041753,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
21724,anirudh21/xlnet-base-cased-finetuned-rte,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0163112897425889,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
21725,google/t5-efficient-small-nl2,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
21726,cross-encoder/nli-deberta-v3-xsmall,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0246219459921121,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
21727,mwesner/bart-mlm,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
21728,lgrobol/electra-minuscule-discriminator,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0385273159481584,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
21729,google/t5-efficient-xl-nl12,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0372458267956972,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
21730,anirudh21/xlnet-base-cased-finetuned-rte,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
21731,KoichiYasuoka/roberta-base-ukrainian,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
21732,allenai/macaw-answer-11b,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0023723604778448,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
21733,crabz/distil-slovakbert-ner,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0732179954648017,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
21734,google/t5-efficient-small-el4,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0781866833567619,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
21735,google/t5-efficient-large-kv16,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0584658812731503,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
21736,sarnikowski/convbert-medium-small-da-cased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0178019708643357,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
21737,google/bert_uncased_L-10_H-256_A-4,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-10_H-256_A-4,1.0,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
21738,google/t5-efficient-xl-nl4,This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named envibert,nguyenvulebinh/envibert,0.0891874805092811,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
21739,allenai/wmt19-de-en-6-6-big,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0281702727079391,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
21740,Milos/slovak-gpt-j-162M,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
21741,google/t5-efficient-base-ff12000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0583021510392427,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
21742,google/t5-efficient-tiny-nl12,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
21743,prajjwal1/bert-medium,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,34c0c0a8-7e6a-420b-9ebd-bb0cc78a7a25___bert-medium,HF
21744,google/t5-efficient-base-kv128,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0257460549473762,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
21745,superspray/electra_large_discriminator_squad2_custom_dataset,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0623441562056541,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
21746,cosmoquester/bart-ko-mini,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0497457782427469,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
21747,MoritzLaurer/MiniLM-L6-mnli,I have made no attempts to censor,bigjoedata/rockbot,0.0198399092381199,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
21748,KoichiYasuoka/roberta-base-ukrainian,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0675771534442901,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
21749,google/t5-efficient-large-nl32,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
21750,hfl/rbt3,T5-Efficient-BASE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el8,0.0206696055829524,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
21751,google/t5-efficient-mini-nl8,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0246033202856779,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
21752,stas/mt5-tiny-random,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0118674235418438,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
21753,NeuML/bert-small-cord19,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
21754,diper1998/distilgpt2-finetuned-AT,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0801913663744926,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
21755,google/t5-efficient-base-dm512,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0534450272098183,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
21756,stas/mt5-tiny-random,"The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0477980896830558,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
21757,google/t5-efficient-base-nh16,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0385705800727009,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
21758,ml6team/distilbart-tos-summarizer-tosdr,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.062264404259622,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
21759,IlyaGusev/rugpt3medium_sum_gazeta,Final perplexity on test set is 13.6,sberbank-ai/rugpt3large_based_on_gpt2,0.0868865847587585,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
21760,dbmdz/electra-small-turkish-cased-discriminator,It can be used to pre-train transformer networks using relatively little compute,dbmdz/electra-small-turkish-cased-discriminator,1.0,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
21761,google/t5-efficient-small-dm768,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
21762,google/t5-efficient-small-dm256,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0602949233725666,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
21763,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The article and  summarization text is reduced via extractive summarization before it is finetuned to the model,ml6team/distilbart-tos-summarizer-tosdr,0.0793574452400207,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
21764,SetFit/deberta-v3-large__sst2__train-8-8,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0978883132338523,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
21765,google/t5-efficient-base-nl24,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.033497055992484,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
21766,google/t5-efficient-base-dm256,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.013440447859466,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
21767,krevas/finance-koelectra-base-discriminator,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0098605616949498,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
21768,akdeniz27/deberta-v2-xlarge-cuad,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
21769,google/t5-efficient-tiny-nl2,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0163147244602441,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
21770,jcblaise/distilbert-tagalog-base-cased,We open-source the model to allow greater usage within the Filipino NLP community,jcblaise/distilbert-tagalog-base-cased,1.0,-1.0,NLP,29e88f0d-47b4-4733-8b59-3cbe0f4bb77e___distilbert-tagalog-base-cased,HF
21771,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-BASE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el8,0.0713081024587154,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
21772,google/t5-efficient-large-nl10,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0246079189237206,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
21773,mrm8488/xlm-multi-finetuned-xquadv1,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0394390746951102,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
21774,allenai/wmt19-de-en-6-6-big,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0152367018163204,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
21775,rinna/japanese-gpt2-xsmall,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0145791421333948,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
21776,google/t5-efficient-small-kv16,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0782580450177192,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
21777,IDEA-CCNL/Erlangshen-Longformer-330M,"To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power thanks to DistilCamemBERT",cmarkea/distilcamembert-base-qa,0.0715568587183952,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
21778,microsoft/layoutlm-base-uncased,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0913870334625244,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
21779,microsoft/layoutlm-large-uncased,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0727218091487884,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
21780,google/t5-11b-ssm-nqo,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0703497330347696,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
21781,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
21782,google/t5-efficient-tiny-el8,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0573785845190286,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
21783,microsoft/layoutlm-base-uncased,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0665121227502822,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
21784,everdoubling/byt5-Korean-small,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0497584849596023,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
21785,flboehm/reddit-bert-text_5,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0754521340131759,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
21786,celine98/canine-s-finetuned-sst2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0864422470331192,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
21787,stas/t5-very-small-random,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
21788,mrm8488/mobilebert-uncased-finetuned-squadv2,Amazon's BORT,amazon/bort,0.04443987707297,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
21789,google/t5-efficient-base-kv128,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0580749288201331,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
21790,GKLMIP/electra-myanmar-small-uncased,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0712769515812396,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
21791,google/t5-efficient-small-el16-dl4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0453086029738186,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
21792,google/t5-efficient-small-kv128,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0278536900877952,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
21793,mimicheng/codeparrot-ds,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0416949763894081,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
21794,cmarkea/distilcamembert-base-qa,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
21795,SupriyaArun/squeezebert-uncased-finetuned-squad,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0528958141803741,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
21796,Milos/slovak-gpt-j-405M,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
21797,google/t5-efficient-large-kv16,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0357980858534574,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
21798,GKLMIP/electra-myanmar-small-uncased,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0890275314450263,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
21799,skt/ko-gpt-trinity-1.2B-v0.5,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0872501283884047,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
21800,hfl/rbtl3,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0410466082394122,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
21801,nielsr/coref-roberta-large,Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me,nielsr/coref-roberta-large,1.0,-1.0,NLP,a42c9bdb-be23-4bfd-8add-30afe8d1724a___coref-roberta-large,HF
21802,ian0delond/model-test,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0927783548831939,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
21803,mimicheng/codeparrot-ds,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
21804,google/bert_uncased_L-8_H-128_A-2,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-128_A-2,1.0,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
21805,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0985678434371948,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
21806,sonoisa/byt5-small-japanese,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0586891025304794,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
21807,google/t5-efficient-large-nh4,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0001349877566098,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
21808,google/t5-efficient-base-kv32,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0499831587076187,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
21809,google/t5-efficient-tiny-el8,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.054748635739088,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
21810,thu-coai/LongLM-large,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0530100517595807,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
21811,gilparmentier/pokemon_gptj_model,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,0.0307446755468845,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
21812,google/t5-efficient-large-nh8,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0415083877742289,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
21813,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0694757401943206,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
21814,rinna/japanese-gpt2-xsmall,"Using this approach, the answer recall rate increases by a large margin, especially at small numbers of top-k",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0758040441821018,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
21815,IDEA-CCNL/Erlangshen-Longformer-330M,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0471647940576076,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
21816,amazon/bort,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0100919529795646,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21817,hfl/rbt3,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0344584118574857,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
21818,DemangeJeremy/4-sentiments-with-flaubert,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0574674662202596,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21819,sberbank-ai/rugpt3medium_based_on_gpt2,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0613295482471584,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
21820,google/t5-efficient-base-nl48,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0515212696045636,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
21821,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0353978797793388,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
21822,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0231996234506368,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
21823,google/t5-efficient-mini-nl6,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0417796913534403,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
21824,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0740986764430999,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
21825,aubmindlab/aragpt2-large,We collect 120G novels as the pretraining data for LongLM,thu-coai/LongLM-large,0.0690486766397952,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
21826,skt/ko-gpt-trinity-1.2B-v0.5,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0205744672566651,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
21827,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,The model achieves the following results on evaluation:,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,1.0,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
21828,google/t5-efficient-small-ff12000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0701396092772483,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
21829,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0784818902611732,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
21830,google/t5-efficient-large-nl32,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
21831,google/t5-efficient-base-nl24,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0869423449039459,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
21832,google/t5-efficient-small-nl22,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.00984488427639,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
21833,abdusahmbzuai/ft-tatoeba-ar-en,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0511672310531139,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
21834,google/t5-efficient-base-nl24,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0202840138226747,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
21835,rdenadai/BR_BERTo,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,0.0343043471996982,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
21836,mwesner/reformer-clm,T5-Efficient-LARGE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh32,0.0871282145380973,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
21837,DemangeJeremy/4-sentiments-with-flaubert,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.007481156848371,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21838,SetFit/deberta-v3-large__sst2__train-8-8,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0969252735376358,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
21839,hfl/rbtl3,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,0.0236202822998165,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
21840,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0774256587028503,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
21841,skt/ko-gpt-trinity-1.2B-v0.5,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0706795491278171,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
21842,google/t5-efficient-small-nl22,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0175685817375779,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
21843,Palak/microsoft_deberta-base_squad,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0579886063933372,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
21844,nielsr/coref-roberta-large,CorefRoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion,nielsr/coref-roberta-large,1.0,-1.0,NLP,a42c9bdb-be23-4bfd-8add-30afe8d1724a___coref-roberta-large,HF
21845,google/t5-efficient-base-nl48,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0714907981455325,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
21846,tau/tavbert-he,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0176963750272989,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
21847,google/t5-efficient-small-ff3000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0207982286810874,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
21848,IlyaGusev/rugpt3medium_sum_gazeta,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0824340544641017,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
21849,hyunwoongko/reddit-9B,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,1.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
21850,google/t5-efficient-tiny-nl12,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0717712473124265,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
21851,google/t5-efficient-large-el6,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0296072717756032,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
21852,google/t5-efficient-tiny-ff12000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.061135871335864,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
21853,VietAI/gpt-neo-1.3B-vietnamese-news,This model was created using the nn_pruning python library: the linear layers contains 27.0% of the original  weights,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
21854,stas/t5-very-small-random,This is a tiny random t5 model used for testingSee t5-make-very-small-model,stas/t5-very-small-random,1.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
21855,google/t5-efficient-base-kv256,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0366688184440135,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
21856,beston91/gpt2-xl_ft_logits_10k,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
21857,google/t5-efficient-large-nl20,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0614108759909868,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
21858,patrickvonplaten/unispeech-sat-large-timit-ft,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0845031663775444,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
21859,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.084518939256668,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
21860,GKLMIP/electra-myanmar-small-uncased,"The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card",minimaxir/magic-the-gathering,0.0232828855514526,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
21861,superb/hubert-large-superb-sid,"The base model is hubert-large-ll60k, which is pretrained on 16kHz  sampled speech audio",superb/hubert-large-superb-sid,1.0,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
21862,google/t5-efficient-large-dm512,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0285525508224963,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
21863,GKLMIP/electra-myanmar-small-uncased,This model used the pre-trained weights of distilbert-base-cased as starting point (only 4 layers) and was able to achieve 84% accuracy on the test set,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
21864,bolbolzaban/gpt2-persian,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0285591511055827,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
21865,mrm8488/deberta-v3-small-finetuned-mrpc,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0743185803294181,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
21866,soheeyang/rdr-ctx_encoder-single-trivia-base,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
21867,lgrobol/electra-minuscule-generator,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
21868,aubmindlab/aragpt2-mega,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0091884157154708,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
21869,google/t5-efficient-large-nl20,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0418144743889569,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
21870,Gilles/FongBERT,I have made no attempts to censor,bigjoedata/rockbot,0.022951877862215,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
21871,google/t5-efficient-small-dm768,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0615306878462433,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
21872,amazon/bort,This is a XLMRoBERTa language model pre-trained on ~3 GB of monolingual training corpus,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0802736580371856,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21873,microsoft/layoutlm-large-uncased,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0037249252200126,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
21874,everdoubling/byt5-Korean-small,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0487916719168424,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
21875,fnlp/cpt-base,Amazon's BORT,amazon/bort,0.0556161357089876,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
21876,tartuNLP/gpt-4-est-large,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0801494121551513,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
21877,mimicheng/codeparrot-ds,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
21878,hfl/rbtl3,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,0.0762141570448875,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
21879,cammy/led-base-16384-100-MDS,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0061952993273735,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
21880,mimicheng/codeparrot-ds,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0974064767360687,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
21881,PlanTL-GOB-ES/gpt2-base-bne,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-base-bne,1.0,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
21882,google/t5-efficient-xl-nl6,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
21883,stas/mt5-tiny-random,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0126069486141204,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
21884,negfir/distilbert-base-uncased-finetuned-cola,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0993784070014953,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
21885,google/t5-efficient-large-dm512,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0421722773462533,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
21886,google/t5-efficient-large-nl20,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0323876943439244,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
21887,VietAI/gpt-neo-1.3B-vietnamese-news,mGPT is pre-trained on the mC4 dataset using a causal language modeling objective,THUMT/mGPT,0.0834486931562423,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
21888,Finnish-NLP/convbert-base-generator-finnish,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,1.0,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
21889,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0446441823483577,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
21890,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.099617913365364,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
21891,DemangeJeremy/4-sentiments-with-flaubert,A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including Strixhaven and Commander 2021,minimaxir/magic-the-gathering,0.0617152154445648,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21892,superspray/electra_large_discriminator_squad2_custom_dataset,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0482878610491752,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
21893,CuongLD/wav2vec2-large-xlsr-vietnamese,The model can be used directly (without a language model) as follows:,CuongLD/wav2vec2-large-xlsr-vietnamese,1.0,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
21894,microsoft/layoutlm-large-uncased,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0167877469211816,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
21895,google/t5-efficient-base-nl36,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
21896,razent/SciFive-large-Pubmed,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0087939091026783,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
21897,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0301652383059263,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
21898,aditeyabaral/sentencetransformer-bert-hinglish-small,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0666752308607101,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
21899,ptaszynski/yacis-electra-small-japanese,The corpus was tokenized for pretraining with MeCab,ptaszynski/yacis-electra-small-japanese,1.0,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
21900,google/t5-efficient-small-dm2000,This does not make a very readable text,pere/DeUnCaser,0.0406513838097452,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
21901,google/t5-efficient-small-nl36,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0681270956993102,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
21902,google/t5-efficient-base-kv32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0126874335110187,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
21903,hf-internal-testing/tiny-random-rembert,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0101914089173078,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
21904,google/t5-efficient-large-dm128,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0373657653108239,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
21905,stas/t5-very-small-random,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0651935786008834,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
21906,hakurei/gpt-j-random-tinier,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
21907,aubmindlab/aragpt2-large,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0100337620824575,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
21908,google/t5-efficient-large-nh8-nl32,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0516681391745805,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
21909,google/t5-efficient-large-el2,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0562449721619486,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
21910,google/t5-efficient-base-kv16,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0478664645925164,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
21911,sberbank-ai/rugpt3medium_based_on_gpt2,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0206670532934367,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
21912,cross-encoder/stsb-TinyBERT-L-4,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.047611929476261,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
21913,junnyu/roformer_base_wwm_cluecorpussmall,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0252008183548847,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
21914,google/t5-efficient-base-kv256,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0458098370581864,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
21915,amazon/bort,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21916,malay-huggingface/t5-small-bahasa-cased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0196320371081431,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
21917,rahulacj/bertweet-base-finetuned-sentiment-analysis,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
21918,sberbank-ai/rugpt3large_based_on_gpt2,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0581556004472076,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
21919,mimi/Waynehills-NLP-doogie,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0933155566453933,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
21920,uer/pegasus-base-chinese-cluecorpussmall,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
21921,TJMUCH/transcriptome-iseeek,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
21922,mrm8488/bert-small-finetuned-squadv2,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0565575994551181,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
21923,negfir/distilbert-base-uncased-finetuned-squad,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
21924,akdeniz27/deberta-v2-xlarge-cuad,ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-1.4B,0.0637470185756683,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
21925,google/t5-efficient-tiny-el8,This does not make a very readable text,pere/DeUnCaser,0.0812986306846141,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
21926,google/t5-efficient-small-dm128,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
21927,xlnet-large-cased,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0671575795859097,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
21928,gilparmentier/pokemon_gptj_model,"This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:The model was fine-tuned on 2,000 manually annotated social media posts",j-hartmann/purchase-intention-english-roberta-large,0.0422539534047245,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
21929,prajjwal1/bert-medium,"They were introduced in the study Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (arxiv), and ported to HF for the study Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics (arXiv)",prajjwal1/bert-medium,1.0,-1.0,NLP,34c0c0a8-7e6a-420b-9ebd-bb0cc78a7a25___bert-medium,HF
21930,microsoft/layoutlm-base-uncased,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.013784185051918,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
21931,superspray/electra_large_discriminator_squad2_custom_dataset,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
21932,microsoft/layoutlm-base-cased,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
21933,cammy/led-large-16384-arxiv-100-MDS,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0973611027002334,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
21934,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0385657846927642,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
21935,brad1141/Longformer-finetuned-norm,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0830689817667007,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
21936,google/t5-efficient-mini-nl24,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0808000452816485,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
21937,groar/gpt-neo-1.3B-finetuned-escape2,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0923243016004562,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
21938,aditeyabaral/sentencetransformer-bert-hinglish-small,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0466559641063213,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
21939,patrickvonplaten/unispeech-sat-large-timit-ft,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
21940,TJMUCH/transcriptome-iseeek,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
21941,DeepPavlov/distilrubert-tiny-cased-conversational,"We also train a byte-level Byte-pair encoding (BPE) tokenizer that includes 56,000 tokens, which is truncated LOC with the length of 50 to save computation resources",Fujitsu/pytorrent,0.0242863868673642,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
21942,google/t5-efficient-base-nh16,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
21943,stas/mt5-tiny-random,This model is cased: it does make a difference between bulgarian and Bulgarian,rmihaylov/bert-base-pos-theseus-bg,0.0739566832780838,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
21944,google/t5-efficient-tiny-nh16,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0899624899029731,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
21945,minimaxir/magic-the-gathering,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
21946,bolbolzaban/gpt2-persian,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0680060908198356,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
21947,mimi/Waynehills-NLP-doogie,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0010884255170822,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
21948,cammy/led-large-16384-arxiv-100-MDS,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0936683416366577,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
21949,microsoft/layoutlm-base-cased,This is a ported version of  S3PRL's Hubert for the SUPERB Emotion Recognition task,superb/hubert-base-superb-er,0.0917602479457855,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
21950,negfir/distilbert-base-uncased-finetuned-cola,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,0.066444955766201,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
21951,cross-encoder/stsb-TinyBERT-L-4,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0452769524417817,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
21952,google/t5-efficient-xl-nl6,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0353184305131435,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
21953,beston91/gpt2-xl_ft_logits_10k,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0948140099644661,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
21954,google/t5-efficient-base-nl24,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0693985968828201,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
21955,microsoft/layoutlm-large-uncased,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0091244801878929,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
21956,lannelin/bert-imdb-1hidden,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
21957,hfl/rbt3,T5-Efficient-SMALL-NL22 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl22,0.0713035557419061,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
21958,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0828033089637756,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
21959,amazon/bort,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0837017409503459,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
21960,google/t5-efficient-base-nh16,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
21961,sberbank-ai/rugpt3large_based_on_gpt2,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
21962,google/t5-efficient-large-nh8,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0371715626679361,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
21963,lgrobol/electra-minuscule-discriminator,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0850555039942264,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
21964,google/t5-efficient-large-nh12,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0117362048476934,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
21965,allenai/wmt16-en-de-dist-6-1,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,1.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
21966,google/t5-efficient-base-ff6000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.017327762208879,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
21967,google/t5-efficient-small-kv16,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.054217243567109,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
21968,google/bert_uncased_L-10_H-128_A-2,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0170423748592535,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
21969,facebook/wmt21-dense-24-wide-en-x,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
21970,google/t5-efficient-xxl-nl4,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0296167340129613,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
21971,google/t5-efficient-small-kv256,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0923019945621489,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
21972,liaad/srl-pt_bertimbau-large,"This is part of a project from which resulted the following models:For more information, please see the accompanying article (See BibTeX entry and citation info below) and the project's github",liaad/srl-pt_bertimbau-large,1.0,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
21973,google/t5-efficient-small-el12,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0536841042339801,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
21974,anas-awadalla/bert-tiny-finetuned-squad,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0299015343189239,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
21975,google/reformer-crime-and-punishment,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
21976,Finnish-NLP/convbert-base-generator-finnish,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0209939830005168,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
21977,google/t5-efficient-large-el2,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0370811279863119,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
21978,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl20,0.011674664914608,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
21979,mimicheng/codeparrot-ds,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0605239495635032,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
21980,monsoon-nlp/dv-muril,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0503798027833302,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
21981,google/t5-efficient-small-ff1000,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
21982,akdeniz27/deberta-v2-xlarge-cuad,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0760697424411773,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
21983,jweb/japanese-soseki-gpt2-1b,Both pytorch(pytorch_model bin) and Rust(rust_modelot) models are providedNOTE: Use T5Tokenizer to initiate the tokenizer,jweb/japanese-soseki-gpt2-1b,1.0,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
21984,sentence-transformers/paraphrase-MiniLM-L3-v2,This repository provides a 1.3B-parameter finetuned Japanese GPT2 model,jweb/japanese-soseki-gpt2-1b,0.0573621280491352,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
21985,google/t5-efficient-large-el8,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0679844245314597,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
21986,DTAI-KULeuven/robbertje-1-gb-bort,This does not make a very readable text,pere/DeUnCaser,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
21987,mimi/Waynehills-NLP-doogie,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0942318439483642,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
21988,google/t5-efficient-base-kv32,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0621571242809294,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
21989,abdusahmbzuai/aradia-ctc-distilhubert-ft,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0917920842766761,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
21990,negfir/distilbert-base-uncased-finetuned-squad,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0425868034362793,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
21991,IlyaGusev/rugpt3medium_sum_gazeta,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0854487419128417,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
21992,google/t5-efficient-base-nl4,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
21993,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0897178277373313,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
21994,google/t5-efficient-small-el12,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
21995,abdusahmbzuai/aradia-ctc-distilhubert-ft,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0940024554729461,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
21996,google/t5-efficient-small-kv32,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0474419444799422,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
21997,stas/pegasus-cnn_dailymail-tiny-random,For best results use python as below,pszemraj/led-large-book-summary,0.0891683660447597,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
21998,mmaguero/gn-bert-tiny-cased,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0250140503048896,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
21999,google/t5-efficient-base-kv128,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0546160731464624,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
22000,SetFit/deberta-v3-large__sst2__train-8-8,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0737721696496009,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
22001,google/t5-efficient-base-nh16,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0707366671413182,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
22002,google/t5-efficient-large-el8,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
22003,zoha/wav2vec2-base-timit-demo-colab,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0990807488560676,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
22004,hajime9652/xlnet-japanese,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0521551668643951,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
22005,google/t5-efficient-base-ff12000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0196541296318173,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
22006,google/t5-efficient-xl-nl8,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.092387493699789,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
22007,diper1998/distilgpt2-finetuned-AT,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0778729766607284,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
22008,google/t5-efficient-tiny-nh1,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0742323510348796,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
22009,google/t5-efficient-tiny-el8,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0817959047853946,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
22010,google/t5-efficient-base-el16,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
22011,google/t5-efficient-base-nh24,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.042817385867238,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
22012,stas/t5-very-small-random,Source language: English,NYTK/translation-marianmt-en-hu,0.0053607337176799,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
22013,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0606517605483531,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
22014,fnlp/bart-large-chinese,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0505319535732269,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
22015,google/t5-efficient-small-nl40,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0014237314462661,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
22016,google/t5-efficient-base-el16,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0290337931364774,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
22017,google/t5-efficient-tiny-nl12,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.023994374088943,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
22018,google/t5-efficient-base-kv32,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0006525395438075,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
22019,google/t5-efficient-small-ff1000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0623511187732219,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
22020,Milos/slovak-gpt-j-405M,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
22021,Norod78/hebrew_poetry-gpt_neo-tiny,"Hebrew poetry text generation model, fined tuned upon hebrew-gpt_neo-tiny which was trained using EleutherAI's gpt-neo",Norod78/hebrew_poetry-gpt_neo-tiny,1.0,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
22022,phueb/BabyBERTa-2,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
22023,izumi-lab/bert-small-japanese-fin,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0238555800169706,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
22024,funnel-transformer/small,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
22025,groar/gpt-neo-1.3B-finetuned-escape2,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0489707924425601,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
22026,castorini/afriberta_base,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0328975098673254,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
22027,rahulacj/bertweet-base-finetuned-sentiment-analysis,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0164058562368154,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
22028,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0867498666048049,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
22029,google/t5-efficient-base-dm2000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0566403381526469,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
22030,google/t5-efficient-small-kv16,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
22031,google/t5-efficient-tiny-nh32,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0694018565118312,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
22032,saibo/random-roberta-tiny,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
22033,cammy/led-large-16384-arxiv-100-MDS,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
22034,google/t5-efficient-tiny-ff9000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0171994511038065,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
22035,Palak/microsoft_deberta-base_squad,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0431421250104904,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
22036,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This is a converted pytorch checkpoint for bert with 2L trained from scratch,tobiaslee/bert-2l-768h-uncased,0.0532975532114505,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
22037,obss/mt5-base-3task-highlight-tquad2,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0293248575180768,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
22038,VietAI/gpt-neo-1.3B-vietnamese-news,"From the original ELECTRA model card: ""ELECTRA models are trained to distinguish ""real"" input tokens vs ""fake"" input tokens generated by another neural network, similar to the discriminator of a GAN",Recognai/selectra_medium,0.0654028244316577,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
22039,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0642680823802948,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
22040,danyaljj/opengpt2_pytorch_forward,I have made no attempts to censor,bigjoedata/rockbot,0.0103863288337985,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
22041,google/t5-efficient-large-nl36,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0617706328630447,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
22042,GKLMIP/electra-myanmar-small-uncased,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
22043,indobenchmark/indobert-base-p1,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
22044,google/t5-efficient-small-el12,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
22045,malay-huggingface/t5-super-tiny-bahasa-cased,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0655534490942954,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
22046,google/t5-efficient-small-kv16,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0294073224067688,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
22047,google/t5-efficient-mini-nl6,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0251979753375053,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
22048,google/t5-efficient-base-nl48,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.010727547109127,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
22049,TJMUCH/transcriptome-iseeek,Masked language modeling(MLM) and sentence order prediction(SOP) are used as training task,junnyu/roformer_base_wwm_cluecorpussmall,0.0975447222590446,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22050,lassl/roberta-ko-small,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
22051,minimaxir/magic-the-gathering,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.030713690444827,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
22052,allenai/macaw-answer-11b,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0575754928092161,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
22053,google/t5-efficient-base-dm256,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0339913405478,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
22054,allenai/wmt16-en-de-dist-6-1,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.060365155339241,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
22055,saibo/random-roberta-tiny,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
22056,hfl/rbt3,T5-Efficient-LARGE-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl20,0.0481765512377023,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22057,google/t5-efficient-large-dm768,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0527736619114875,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
22058,google/t5-efficient-small-ff6000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0572921037673949,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
22059,hfl/rbt3,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,0.0964037999510764,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22060,skplanet/dialog-koelectra-small-generator,"ELECTRA models are trained to distinguish ""real"" input tokens vs ""fake"" input tokens generated by another neural network, similar to the discriminator of a GAN",skplanet/dialog-koelectra-small-generator,1.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
22061,tau/tavbert-he,T5-Efficient-SMALL-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm128,0.0285814721137284,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
22062,dbmdz/bert-medium-historic-multilingual-cased,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0949681550264358,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
22063,Palak/microsoft_deberta-base_squad,This does not make a very readable text,pere/DeUnCaser,0.0660241842269897,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
22064,google/t5-efficient-small-dm1000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0749308988451957,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
22065,suzuki/distilbert-base-uncased-finetuned-squad,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0996955633163452,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
22066,deep-learning-analytics/triviaqa-t5-base,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0174042106206927,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
22067,minimaxir/magic-the-gathering,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0233324454165994,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
22068,stas/mt5-tiny-random,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
22069,google/t5-efficient-small-kv16,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0935563929378985,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
22070,google/t5-efficient-large-nh8,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
22071,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
22072,flax-community/bengali-t5-base,bengali-t5-base is a model trained on the Bengali portion of MT5 dataset,flax-community/bengali-t5-base,1.0,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
22073,google/t5-efficient-base-ff9000,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
22074,microsoft/layoutlm-base-uncased,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0971509292721748,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22075,google/t5-efficient-large-kv16,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.069404762238264,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
22076,google/t5-efficient-large-nl20,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
22077,flboehm/reddit-bert-text_5,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0813434049487114,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
22078,mikaelsouza/msft-regular-model,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
22079,diper1998/distilgpt2-finetuned-AT,"FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows",nherve/flaubert-oral-ft,0.0046146344393491,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
22080,facebook/m2m100-12B-avg-10-ckpt,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0599375547220309,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
22081,SupriyaArun/squeezebert-uncased-finetuned-squad,Our Historic Language Models Zoo contains support for the following languages - incl,dbmdz/bert-medium-historic-multilingual-cased,0.0686538815498352,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22082,google/t5-efficient-small-dm256,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
22083,VietAI/gpt-neo-1.3B-vietnamese-news,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0247337240725756,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
22084,KoichiYasuoka/roberta-base-ukrainian,"The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card",minimaxir/magic-the-gathering,0.0874170660972595,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
22085,KBLab/megatron-bert-large-swedish-cased-165k,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0307064231795569,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
22086,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.094249203801155,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
22087,flax-community/gpt2-medium-persian,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
22088,google/t5-efficient-small-el8,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0422971528023481,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
22089,google/t5-efficient-large-dm768,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.074010107666254,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
22090,SupriyaArun/squeezebert-uncased-finetuned-squad,"Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa)",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0745503008365631,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22091,fnlp/bart-base-chinese,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.030163500458002,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
22092,amazon/bort,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22093,superspray/electra_large_discriminator_squad2_custom_dataset,This is intended to be used as a translation model between PROT-BERT and GO-Language,damlab/GO-language,0.021838903427124,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
22094,ian0delond/model-test,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0604521259665489,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
22095,suzuki/distilbert-base-uncased-finetuned-squad,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0814711302518844,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
22096,aubmindlab/aragpt2-mega,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0591631978750228,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
22097,google/t5-efficient-small-ff3000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0413702763617038,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
22098,IDEA-CCNL/Erlangshen-Longformer-110M,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.031321907415986,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
22099,google/t5-efficient-tiny-ff12000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0448248516768216,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
22100,google/t5-efficient-small-nl24,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0639383755624293,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
22101,google/t5-efficient-small-nl22,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0120858252048492,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
22102,cosmoquester/bart-ko-small,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0592527116338411,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
22103,allenai/wmt16-en-de-dist-6-1,"For instance, you can ask ""Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"", and the model will hopefully generate ""Positive""",saurkulsh/T0pp,0.0310515901073813,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
22104,stas/mt5-tiny-random,"We modify the original position code of longformer to rotational position coding, and use 180G of data to pretraining",IDEA-CCNL/Erlangshen-Longformer-330M,0.0920225009322166,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
22105,google/t5-efficient-base-nl2,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
22106,cammy/led-base-16384-100-MDS,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0162082351744174,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
22107,clue/roberta_chinese_3L312_clue_tiny,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0377976410090922,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
22108,google/t5-efficient-base-kv16,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
22109,superspray/electra_large_discriminator_squad2_custom_dataset,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.064000703394413,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
22110,razent/SciFive-large-Pubmed,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0848752036690711,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
22111,uer/t5-v1_1-small-chinese-cluecorpussmall,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
22112,Iskaj/hf-challenge-test,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0260853432118892,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
22113,google/t5-efficient-base-nl48,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0388217587023973,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
22114,google/t5-efficient-base-dm1000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.005354918539524,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
22115,IDEA-CCNL/Erlangshen-Longformer-110M,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
22116,flboehm/reddit-bert-text_5,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0669494271278381,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
22117,google/t5-efficient-tiny-nh32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
22118,saurkulsh/T0pp,"To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks",saurkulsh/T0pp,1.0,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
22119,ToToKr/kobigbird-bert-base-finetuned-klue,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0968587547540664,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
22120,microsoft/layoutlm-base-cased,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0736049860715866,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
22121,google/t5-efficient-base-el16,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0954885445535182,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
22122,hfl/chinese-legal-electra-base-generator,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0009091390917698,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
22123,gchhablani/fnet-base-finetuned-wnli,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0145534696057438,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
22124,stas/t5-very-small-random,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0598247572779655,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
22125,aditeyabaral/sentencetransformer-bert-hinglish-small,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
22126,google/t5-efficient-base-nh24,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0905054248869418,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
22127,microsoft/layoutlm-base-cased,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0768434330821037,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
22128,google/bert_uncased_L-6_H-256_A-4,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0110978856682777,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
22129,aditeyabaral/sentencetransformer-bert-hinglish-small,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
22130,google/t5-efficient-large-dm768,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0275894394144415,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
22131,google/t5-efficient-small-el64,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
22132,tau/tavbert-he,T5-Efficient-MINI-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl8,0.0601993594318627,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
22133,oskrmiguel/mt5-simplification-spanish,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.033630346879363,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
22134,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0853104814887046,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
22135,hfl/rbtl3,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0602499693632125,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
22136,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl2,0.0942130535840988,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
22137,google/t5-efficient-base-nl36,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
22138,IDEA-CCNL/Erlangshen-Longformer-330M,"The quality estimation can be done at different levels: document level, sentence level and word level",TransQuest/monotransquest-hter-en_lv-it-nmt,0.086966834962368,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
22139,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0307406615465879,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
22140,google/t5-efficient-base-el16,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0719607807695865,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
22141,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0822894871234893,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
22142,Iskaj/hf-challenge-test,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0933832675218582,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
22143,google/t5-efficient-tiny-ff3000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0367428255267441,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
22144,mimicheng/codeparrot-ds,The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1,nyu-mll/roberta-med-small-1M-3,0.0921476855874061,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
22145,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0081170871853828,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
22146,KoichiYasuoka/roberta-base-ukrainian,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.077730119228363,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
22147,Milos/slovak-gpt-j-405M,This KinyaRoBERTa model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda,jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
22148,allenai/wmt16-en-de-12-1,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0070020891726017,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
22149,google/t5-efficient-large-el2,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0779804773628711,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
22150,google/t5-efficient-base-nl40,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0819956064224243,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
22151,mwesner/bart-mlm,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0538904182612896,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
22152,stas/pegasus-cnn_dailymail-tiny-random,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.037741514854133,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
22153,fnlp/bart-large-chinese,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0661501511931419,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
22154,stas/t5-very-small-random,"For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github",sarnikowski/convbert-medium-small-da-cased,0.0522786155343055,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
22155,google/t5-efficient-tiny-nl32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0466282591223716,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
22156,superb/hubert-base-superb-er,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0584365408867597,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
22157,funnel-transformer/large-base,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0423409778624772,-1.0,NLP,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
22158,skplanet/dialog-koelectra-small-generator,"At small scale, ELECTRA achieves strong results even when trained on a single GPU",skplanet/dialog-koelectra-small-generator,1.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
22159,lgris/distilxlsr_bp_8-12,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,1.0,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
22160,Milos/slovak-gpt-j-405M,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0428885680933792,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
22161,celine98/canine-s-finetuned-sst2,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0278212949633598,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
22162,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
22163,Milos/slovak-gpt-j-1.4B,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0478135854937135,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
22164,nyu-mll/roberta-med-small-1M-3,The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1,nyu-mll/roberta-med-small-1M-3,1.0,-1.0,NLP,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
22165,google/t5-efficient-tiny-nh8,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0635034628212451,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
22166,Iskaj/hf-challenge-test,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0599231272935867,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
22167,tucan9389/kcbert-base-finetuned-squad,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
22168,razent/SciFive-large-Pubmed,"Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint",KBLab/megatron-bert-large-swedish-cased-165k,0.0708475951105355,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
22169,minimaxir/magic-the-gathering,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0173558141104876,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
22170,hfl/rbt3,T5-Efficient-BASE-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff1000,0.0403637932613491,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22171,Finnish-NLP/convbert-base-generator-finnish,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.023271320015192,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
22172,allenai/wmt16-en-de-12-1,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0623438656330108,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
22173,facebook/blenderbot-3B,Building open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-3B,1.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
22174,clue/xlnet_chinese_large,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.047580262646079,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
22175,TJMUCH/transcriptome-iseeek,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0269528403878211,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22176,cammy/led-large-16384-arxiv-100-MDS,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0234934594482183,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
22177,flax-community/bengali-t5-base,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
22178,ductuan024/AimeLaw,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0152819817885756,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
22179,VietAI/gpt-neo-1.3B-vietnamese-news,Zero-shot SELECTRA is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset,Recognai/zeroshot_selectra_small,0.0499809719622135,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
22180,yorko/scibert_scivocab_uncased_long_4096,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.03146050311625,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
22181,google/t5-efficient-base-kv256,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
22182,castorini/afriberta_large,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0624619219452142,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
22183,indobenchmark/indobert-base-p1,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
22184,Fujitsu/pytorrent,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0151809913416703,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
22185,google/t5-efficient-small-dm256,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
22186,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.070487655699253,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
22187,ToToKr/kobigbird-bert-base-finetuned-klue,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0576193407177925,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
22188,google/t5-efficient-tiny-nh32,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0505664125084877,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
22189,google/t5-efficient-large-kv128,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0514471577480435,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
22190,stas/t5-very-small-random,com/peterwestuw/GPT2ForwardBackward,danyaljj/opengpt2_pytorch_forward,0.0742524042725563,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
22191,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
22192,inokufu/flaubert-base-uncased-xnli-sts,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0393657856620847,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
22193,Palak/microsoft_deberta-base_squad,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0753160715103149,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
22194,tuner007/pegasus_qa,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0145041607320308,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
22195,anas-awadalla/bert-tiny-finetuned-squad,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0549070090055465,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
22196,google/t5-efficient-small-ff12000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0310509139671921,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
22197,SetFit/deberta-v3-large__sst2__train-8-8,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0272123795002698,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
22198,google/t5-efficient-large-nh32,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
22199,google/t5-efficient-base-nh8,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0797288529574871,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
22200,allenai/wmt16-en-de-dist-6-1,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0206154529005288,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
22201,aliosm/ComVE-gpt2-large,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0621672365814447,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
22202,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0718643739819526,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
22203,microsoft/layoutlm-base-uncased,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0015662349760532,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22204,google/t5-efficient-tiny-nh8,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0607595350593328,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
22205,google/t5-efficient-base-el6,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0663134269416331,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
22206,liaad/srl-pt_bertimbau-large,To use the transformers portion of this model:,liaad/srl-pt_bertimbau-large,1.0,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
22207,hfl/rbtl3,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0893627107143401,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
22208,google/t5-efficient-large-nl10,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0717450864613055,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
22209,diper1998/distilgpt2-finetuned-AT,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0884521305561065,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
22210,google/t5-efficient-xl-nl16,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.089937724173069,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
22211,google/t5-efficient-base-nl40,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,1.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
22212,google/reformer-crime-and-punishment,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
22213,lgrobol/electra-minuscule-generator,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0682182647287845,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
22214,hfl/rbt3,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0570750571787357,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22215,google/t5-efficient-base-nl36,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
22216,google/t5-efficient-small-el12,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0145377870649099,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
22217,google/t5-efficient-small-kv128,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0552457608282565,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
22218,cosmoquester/bart-ko-small,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0235126689076423,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
22219,google/t5-efficient-mini,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0346559304744004,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
22220,patrickvonplaten/unispeech-sat-large-timit-ft,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0491823405027389,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
22221,tucan9389/kcbert-base-finetuned-squad,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0592985749244689,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
22222,Fujitsu/pytorrent,We use PyTorrent dataset to train a preliminary DistilBERT-Masked Language Modeling(MLM) model from scratch,Fujitsu/pytorrent,1.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
22223,tartuNLP/gpt-4-est-large,Global attention is user-configured based on the task to allow the model to learn task-specific representations,mrm8488/longformer-base-4096-spanish,0.0563461333513259,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
22224,lannelin/bert-imdb-1hidden,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
22225,hfl/chinese-electra-small-ex-discriminator,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
22226,hfl/rbt3,This is a Lonformer version of the SciBERT uncased model by Allen AI,yorko/scibert_scivocab_uncased_long_4096,0.0711625404655933,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22227,IDEA-CCNL/Erlangshen-Longformer-330M,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
22228,microsoft/layoutlm-base-cased,"Hebrew poetry text generation model, fined tuned upon hebrew-gpt_neo-tiny which was trained using EleutherAI's gpt-neo",Norod78/hebrew_poetry-gpt_neo-tiny,0.0490710325539112,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
22229,flaubert/flaubert_small_cased,For more details please refer to the official website,flaubert/flaubert_small_cased,1.0,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
22230,minimaxir/magic-the-gathering,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
22231,anirudh21/xlnet-base-cased-finetuned-rte,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0106730060651898,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
22232,google/t5-efficient-large-el8,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0522730574011802,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
22233,Sindhu/muril-large-squad2,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0281521417200565,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
22234,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
22235,IDEA-CCNL/Erlangshen-Longformer-110M,You can use the raw model for summarization,rmihaylov/pegasus-base-cnn-dailymail-bg,0.037237275391817,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
22236,brad1141/Longformer-finetuned-norm,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0895551592111587,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
22237,facebook/wmt21-dense-24-wide-en-x,We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric,andrejmiscic/simcls-scorer-billsum,0.0271767903119325,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
22238,zoha/wav2vec2-base-timit-demo-colab,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0987713634967804,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
22239,stas/mt5-tiny-random,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0548449158668518,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
22240,google/t5-11b-ssm-nqo,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0053170708318551,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
22241,google/t5-efficient-small-nl20,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
22242,google/t5-efficient-large-nh8-nl32,Its original repository can be found here,tau/splinter-large-qass,0.056466257199645,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
22243,google/t5-efficient-base-ff12000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0147705310955643,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
22244,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0933121964335441,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
22245,google/t5-efficient-base-nl4,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0602295007556676,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
22246,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
22247,crabz/distil-slovakbert-ner,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0973694920539856,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
22248,akdeniz27/deberta-v2-xlarge-cuad,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0281072594225406,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
22249,VietAI/gpt-neo-1.3B-vietnamese-news,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0856431908905505,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
22250,ian0delond/model-test,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0810211226344108,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
22251,google/t5-efficient-large-nh32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
22252,mldmm/GlassBERTa,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0242342944256961,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
22253,google/t5-efficient-tiny-nh1,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0694382414221763,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
22254,lannelin/bert-imdb-1hidden,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0382261412839094,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
22255,google/t5-efficient-base-nh8,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0136950155720114,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
22256,google/t5-efficient-base-dm256,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0117854196578264,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
22257,cointegrated/rubert-tiny-bilingual-nli,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.0654394005735715,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
22258,google/t5-efficient-large-kv16,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0497677791863679,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
22259,lgrobol/electra-minuscule-generator,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0964408330619334,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
22260,microsoft/layoutlm-large-uncased,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0788372606039047,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
22261,mimi/Waynehills-NLP-doogie,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
22262,rinna/japanese-gpt2-xsmall,This is a sentence-transformers model: It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search,mrp/simcse-model-wangchanberta,0.0710098072886466,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
22263,google/t5-efficient-small-nl24,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0877534188330173,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
22264,indobenchmark/indobert-base-p1,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0614099080363909,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
22265,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0952547416090965,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
22266,google/t5-efficient-base-kv256,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0752177387475967,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
22267,nyu-mll/roberta-base-10M-3,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
22268,ian0delond/model-test,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0549732372164726,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
22269,mwesner/reformer-clm,"For the model without those weights, see tau/splinter-large",tau/splinter-large-qass,0.0945626124739646,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
22270,google/t5-efficient-large-nl16,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nl16,1.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
22271,microsoft/layoutlm-large-uncased,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,0.0859688371419906,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
22272,lannelin/bert-imdb-1hidden,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
22273,google/t5-efficient-mini-nl24,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0373535584658384,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
22274,TristanBehrens/js-fakes-4bars,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,1.0,-1.0,NLP,84456894-0906-4840-8de2-f427e2536a64___js-fakes-4bars,HF
22275,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0600237175822258,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
22276,tucan9389/kcbert-base-finetuned-squad,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
22277,google/t5-efficient-base-kv32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
22278,bigjoedata/rockbot,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,0.0011193022752801,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
22279,google/t5-efficient-large-nl32,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0510056354105472,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
22280,aditeyabaral/sentencetransformer-bert-hinglish-small,"This is the set of Chinese T5 Version 1.1 models pre-trained by UER-py, which is introduced in this paper",uer/t5-v1_1-small-chinese-cluecorpussmall,0.0836337506771087,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
22281,DemangeJeremy/4-sentiments-with-flaubert,org/abs/1904.09223This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and  a series of experiments have been conducted to check the accuracy of the conversion,nghuyong/ernie-1.0,0.0858406499028205,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
22282,google/t5-efficient-xl-nl28,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0351787768304347,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
22283,google/t5-efficient-small-dm1000,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0275307418778538,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
22284,google/t5-efficient-base-nl36,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.07377165928483,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
22285,amazon/bort,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22286,google/t5-efficient-small-nl22,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-nl22,1.0,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
22287,superspray/electra_large_discriminator_squad2_custom_dataset,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
22288,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Both pytorch(pytorch_model bin) and Rust(rust_modelot) models are providedNOTE: Use T5Tokenizer to initiate the tokenizer,jweb/japanese-soseki-gpt2-1b,0.064758576452732,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
22289,google/t5-efficient-base-dm512,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0495744533836841,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
22290,google/t5-efficient-large-el2,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0168960429728031,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
22291,brad1141/Longformer-finetuned-norm,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0858298614621162,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
22292,mrp/simcse-model-wangchanberta,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0371260438114404,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
22293,google/t5-efficient-small-el32,This does not make a very readable text,pere/DeUnCaser,0.0790609903633594,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
22294,google/t5-efficient-tiny-nl16,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0866129286587238,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
22295,CuongLD/wav2vec2-large-xlsr-vietnamese,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.077155090868473,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
22296,uer/t5-v1_1-small-chinese-cluecorpussmall,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0567291723564267,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
22297,google/t5-efficient-xl-nl4,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.064091645181179,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
22298,cammy/led-large-16384-arxiv-100-MDS,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0305484253913164,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
22299,amazon/bort,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.0729915797710418,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22300,castorini/afriberta_large,"The model has been shown to obtain competitive downstream performances on text classification and Named Entity Recognition on several African languages, including those it was not pretrained on",castorini/afriberta_large,1.0,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
22301,hajime9652/xlnet-japanese,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0952609181404113,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
22302,rahulacj/bertweet-base-finetuned-sentiment-analysis,"Below is list of data we trained on,You can use this model by installing torch or tensorflow and Huggingface library transformers",malay-huggingface/xlnet-tiny-bahasa-cased,0.0957276374101638,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
22303,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-mega,0.0929510295391082,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
22304,DTAI-KULeuven/robbertje-1-gb-bort,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0376861033340295,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
22305,mwesner/bart-mlm,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0866464376449585,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
22306,google/t5-efficient-small-el12,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0677353776991367,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
22307,google/t5-efficient-small-dm1000,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0762470141053199,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
22308,google/t5-efficient-large-dm2000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0230426359921694,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
22309,google/t5-efficient-base-kv128,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0257412949576973,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
22310,google/t5-efficient-base-kv16,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0494394358247518,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
22311,TJMUCH/transcriptome-iseeek,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0526775047183036,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22312,SetFit/deberta-v3-large__sst2__train-8-8,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
22313,google/t5-efficient-base-ff9000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
22314,google/t5-efficient-tiny-nh32,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.037946980446577,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
22315,google/t5-efficient-base-kv16,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0536070093512534,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
22316,google/t5-efficient-base-dm256,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0542186312377452,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
22317,SupriyaArun/squeezebert-uncased-finetuned-squad,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22318,google/t5-efficient-large-nh4,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,1.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
22319,gayanin/bart-mlm-pubmed-medterm,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0945398956537246,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
22320,groar/gpt-neo-1.3B-finetuned-escape2,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0627281963825225,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
22321,razent/SciFive-large-Pubmed,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0643812417984008,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
22322,google/t5-efficient-xl-nl2,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0534078255295753,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
22323,sberbank-ai/rugpt3large_based_on_gpt2,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0503186001442372,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
22324,google/t5-efficient-base-nl4,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
22325,SupriyaArun/squeezebert-uncased-finetuned-squad,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0610222592949867,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22326,hfl/chinese-electra-small-ex-generator,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
22327,SetFit/deberta-v3-large__sst2__train-8-8,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0437376797199249,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
22328,everdoubling/byt5-Korean-small,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0019401669502258,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
22329,negfir/distilbert-base-uncased-finetuned-cola,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0526264533400535,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
22330,amazon/bort,"When using the model, make sure that your speech input is also sampled at 16kHz",microsoft/unispeech-sat-base,0.0787130557000637,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22331,hfl/chinese-legal-electra-base-generator,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
22332,mrm8488/bert-small-finetuned-squadv2,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0176049862056971,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
22333,Iskaj/hf-challenge-test,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
22334,google/t5-efficient-small-kv16,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
22335,google/t5-efficient-large-nl12,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0870837531983852,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
22336,hfl/chinese-electra-small-ex-generator,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
22337,google/t5-efficient-small-ff12000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0669876970350742,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
22338,google/reformer-crime-and-punishment,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0184852606616914,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
22339,google/t5-efficient-tiny-ff9000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.019281718879938,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
22340,oskrmiguel/mt5-simplification-spanish,This is a fine-tuned mt5-small model for generating simple text from complex text,oskrmiguel/mt5-simplification-spanish,1.0,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
22341,DemangeJeremy/4-sentiments-with-flaubert,"Pour toute utilisation de ce modèle, merci d'utiliser cette citation : Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface",DemangeJeremy/4-sentiments-with-flaubert,1.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
22342,Recognai/zeroshot_selectra_small,The hypothesis_template parameter is important and should be in Spanish,Recognai/zeroshot_selectra_small,1.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
22343,google/t5-efficient-tiny-nl16,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0372342374175786,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
22344,Finnish-NLP/convbert-base-generator-finnish,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0440095618367194,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
22345,akdeniz27/deberta-v2-xlarge-cuad,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0833799242973327,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
22346,google/t5-efficient-mini,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0973314344882964,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
22347,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff1000,0.0077521540224551,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
22348,dbmdz/electra-small-turkish-cased-discriminator,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0396235417574643,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
22349,google/t5-efficient-small-nl40,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
22350,allenai/wmt16-en-de-dist-6-1,Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team,xlnet-large-cased,0.0292138122022151,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
22351,KBLab/megatron-bert-large-swedish-cased-165k,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
22352,NovelAI/genji-python-6B,This particular model is trained on python only code approaching 4GB in size,NovelAI/genji-python-6B,1.0,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
22353,hajime9652/xlnet-japanese,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0990656092762947,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
22354,negfir/distilbert-base-uncased-finetuned-cola,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0173659175634384,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
22355,uer/chinese_roberta_L-2_H-256,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0486992429941892,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
22356,hfl/rbt3,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0976412333548068,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22357,aliosm/ComVE-gpt2-large,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0225682826712727,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
22358,microsoft/layoutlm-large-uncased,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0520309619605541,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
22359,mimicheng/codeparrot-ds,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0805739238858223,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
22360,google/t5-efficient-tiny-ff3000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
22361,google/t5-efficient-mini-nl24,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0101350303739309,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
22362,minimaxir/magic-the-gathering,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.034578766208142,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
22363,google/t5-efficient-large-nl10,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0519401337951421,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
22364,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-NH8-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8-nl32,0.0807740439971287,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
22365,google/t5-efficient-tiny-ff12000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0219596941024064,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
22366,allenai/macaw-3b,"To use CPT, please import the file modeling_cpt",fnlp/cpt-base,0.0455173812806606,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
22367,mimicheng/codeparrot-ds,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0828450322151184,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
22368,IDEA-CCNL/Erlangshen-Longformer-330M,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0339584015309809,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
22369,stas/pegasus-cnn_dailymail-tiny-random,"6,831,079 examples are extracted from below corpora",lassl/gpt2-ko-small,0.0807444341480731,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
22370,google/t5-efficient-large-dm256,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-dm256,1.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
22371,IDEA-CCNL/Erlangshen-Longformer-110M,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.0303749544546007,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
22372,razent/SciFive-base-PMC,This model was finetuned for 3 epochs with a max_len (input) of 768 tokens and target_max_len of 192 tokens,ml6team/mt5-small-german-finetune-mlsum,0.0671280529350042,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
22373,google/t5-efficient-base-dm2000,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,1.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
22374,nghuyong/ernie-tiny,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
22375,thu-coai/LongLM-large,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,0.0309834734847148,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
22376,google/t5-efficient-base-nl32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.059547422453761,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
22377,bolbolzaban/gpt2-persian,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0608366858214139,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
22378,google/t5-efficient-xl-nl6,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0064420141279697,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
22379,microsoft/unispeech-sat-base,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
22380,stas/mt5-tiny-random,"It is also more accurate, especially when reinvesting the input embeddings' parameters into the core model, as is done on RemBERT",google/rembert,0.0400715991854667,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
22381,Milos/slovak-gpt-j-405M,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0039792985965808,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
22382,sonoisa/byt5-small-japanese,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0838658586144447,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
22383,lannelin/bert-imdb-1hidden,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
22384,facebook/blenderbot-3B,"While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot",facebook/blenderbot-3B,1.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
22385,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.0523612312972545,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
22386,Vilnius-Lithuania-iGEM/Albumin,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0687784689168134,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
22387,google/t5-efficient-base-kv16,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0456183422356843,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
22388,google/t5-efficient-small-dm128,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0140450224280356,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
22389,google/t5-efficient-small-kv32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0412032790482044,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
22390,beston91/gpt2-xl_ft_logits_10k,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0526611357927322,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
22391,google/t5-efficient-large-nh32,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0671637393534183,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
22392,google/t5-efficient-base-nl48,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0632232576608657,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
22393,KoichiYasuoka/roberta-base-ukrainian,It was introduced in this paper,readerbench/RoBERT-small,0.0610635429620742,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
22394,google/t5-efficient-small-dm256,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
22395,google/t5-efficient-base-ff6000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0069025103002786,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
22396,google/t5-efficient-tiny-nh16,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0383526738733052,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
22397,anirudh21/xlnet-base-cased-finetuned-rte,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0270662624388933,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
22398,google/t5-efficient-xl-nl16,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0620132610201835,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
22399,google/t5-efficient-xl-nl6,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0887951701879501,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
22400,fnlp/bart-large-chinese,"This model, ibert-roberta-base, is an integer-only quantized version of RoBERTa, and was introduced in this paper",kssteven/ibert-roberta-base,0.0879429019987582,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
22401,rahulacj/bertweet-base-finetuned-sentiment-analysis,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0536718666553497,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
22402,google/t5-efficient-small-ff9000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0326703498139977,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
22403,google/t5-efficient-large-nh8-nl32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0671809390187263,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
22404,google/t5-efficient-base-ff1000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0389877706766128,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
22405,google/t5-efficient-base-ff6000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0334322256967425,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
22406,google/t5-efficient-base-nl36,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
22407,google/t5-efficient-base-nl40,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0722063947468995,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
22408,google/t5-efficient-xl-nl16,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0187904518097639,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
22409,razent/SciFive-base-PMC,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0559480190277099,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
22410,stas/t5-very-small-random,Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0897392332553863,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
22411,google/t5-efficient-xl-nl16,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
22412,SupriyaArun/squeezebert-uncased-finetuned-squad,"Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking",xlnet-large-cased,0.0941058993339538,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22413,suzuki/distilbert-base-uncased-finetuned-squad,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0588794164359569,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
22414,google/t5-efficient-large-kv32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0734308511018752,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
22415,mrm8488/deberta-v3-small-finetuned-mrpc,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0063502714037895,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
22416,hfl/rbt3,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22417,google/t5-efficient-base-nh8,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0669913887977599,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
22418,google/t5-efficient-small-el12,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0656288843601941,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
22419,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0481356009840965,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
22420,aubmindlab/aragpt2-large,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0076204854995011,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
22421,anirudh21/xlnet-base-cased-finetuned-rte,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0844271332025528,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
22422,google/bert_uncased_L-8_H-256_A-4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
22423,mmaguero/gn-bert-tiny-cased,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0163234607316553,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
22424,indobenchmark/indobert-base-p1,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
22425,google/t5-efficient-tiny-nl2,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0618341602385043,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
22426,google/t5-efficient-tiny-nh1,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
22427,facebook/m2m100-12B-avg-10-ckpt,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0289914126818378,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
22428,google/t5-efficient-base-dm512,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.007681671064347,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
22429,IlyaGusev/rugpt3medium_sum_gazeta,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0164389610290527,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
22430,google/t5-efficient-small-kv256,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0395548567175864,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
22431,ToToKr/kobigbird-bert-base-finetuned-klue,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0818816274404525,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
22432,IDEA-CCNL/Erlangshen-Longformer-110M,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0221933731809257,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
22433,google/t5-efficient-small-nl36,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0927371606230735,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
22434,tartuNLP/gpt-4-est-large,The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our Stack Overflow Dataset repository,UWB-AIR/MQDD-duplicates,0.0841845571994781,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
22435,google/t5-efficient-small-ff6000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
22436,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0545461401343345,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
22437,google/t5-efficient-large-kv32,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0658548511564731,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
22438,amazon/bort,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22439,rdenadai/BR_BERTo,T5-Efficient-TINY-NH1 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh1,0.0332768882314364,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
22440,TJMUCH/transcriptome-iseeek,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0006832014769315,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22441,google/t5-efficient-tiny-nl24,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0181937385350465,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
22442,TJMUCH/transcriptome-iseeek,Each was trained on a TPUv3-8 which was made avilable to me via the TPU Research Cloud Program,Norod78/hebrew_poetry-gpt_neo-tiny,0.0741947442293167,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22443,malay-huggingface/t5-super-tiny-bahasa-cased,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0934557504951953,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
22444,google/t5-efficient-xl-nl28,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.048920340836048,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
22445,google/t5-efficient-small-el32,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0697471648454666,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
22446,cammy/led-large-16384-arxiv-100-MDS,"The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0939900130033493,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
22447,fnlp/cpt-base,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0181513018906116,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
22448,google/t5-efficient-xl-nl16,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0755810737609863,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
22449,tuner007/pegasus_qa,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0755885243415832,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
22450,google/t5-efficient-mini,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0552655197679996,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
22451,google/byt5-large,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0357712963595986,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
22452,google/t5-efficient-base-nl24,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0059785461053251,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
22453,google/t5-efficient-base-nl4,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0696749612689017,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
22454,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Each was trained on a TPUv3-8 which was made avilable to me via the TPU Research Cloud Program,Norod78/hebrew_poetry-gpt_neo-tiny,0.0954696685075759,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
22455,everdoubling/byt5-Korean-small,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
22456,uer/t5-v1_1-base-chinese-cluecorpussmall,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
22457,sentence-transformers/paraphrase-MiniLM-L3-v2,ipynb and the weights were converted to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead,google/reformer-crime-and-punishment,0.0670594051480293,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
22458,lannelin/bert-imdb-1hidden,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,1.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
22459,cammy/led-large-16384-arxiv-100-MDS,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
22460,beston91/gpt2-xl_ft_logits_10k,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0592407211661338,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
22461,tau/tavbert-he,We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy,hyunwoongko/reddit-9B,0.0376356542110443,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
22462,facebook/blenderbot-3B,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0603291067693914,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
22463,hfl/rbtl3,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0594444042071699,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
22464,rinna/japanese-gpt-1b,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
22465,IlyaGusev/rugpt3medium_sum_gazeta,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0637095607817172,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
22466,google/t5-efficient-base-dm256,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0708652585744857,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
22467,google/t5-efficient-base-kv16,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0195894846692681,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
22468,brad1141/Longformer-finetuned-norm,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0337498150765895,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
22469,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff12000,0.0859630033373832,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
22470,google/t5-efficient-large-el2,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0215633539482951,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
22471,soheeyang/rdr-ctx_encoder-single-trivia-base,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,1.0,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
22472,prajjwal1/bert-medium,"If you use the model, please consider citing both the papers:",prajjwal1/bert-medium,1.0,-1.0,NLP,34c0c0a8-7e6a-420b-9ebd-bb0cc78a7a25___bert-medium,HF
22473,google/t5-efficient-base-kv256,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0596051774919032,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
22474,SupriyaArun/squeezebert-uncased-finetuned-squad,It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al,xlnet-large-cased,0.093431606888771,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22475,mgreenbe/bertlet-base-uncased-for-sequence-classification,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0835406333208084,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
22476,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0707815065979957,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
22477,google/t5-efficient-large-nl10,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0577081181108951,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
22478,wukevin/tcr-bert,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0536481477320194,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
22479,brad1141/Longformer-finetuned-norm,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
22480,google/t5-efficient-large-dm2000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0497012436389922,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
22481,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,mMiniLM-L6-v2-en-pt-msmarco-v2 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,1.0,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
22482,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0423251636326313,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
22483,microsoft/layoutlm-base-cased,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0546437948942184,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
22484,VietAI/gpt-neo-1.3B-vietnamese-news,"Therefore, this model does not need a tokenizer",google/reformer-enwik8,0.0352318286895751,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
22485,lgrobol/electra-minuscule-generator,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.0633467100560664,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
22486,google/t5-efficient-small-nl40,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
22487,google/t5-efficient-small-dm768,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.05046858638525,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
22488,mrp/simcse-model-wangchanberta,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0718165803700685,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
22489,google/t5-efficient-small-nl22,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
22490,hfl/chinese-xlnet-mid,"BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences",google/bigbird-roberta-large,0.0502883745357394,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
22491,hfl/rbt3,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0569835398346185,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22492,KoichiYasuoka/roberta-small-belarusian,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0745587982237338,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
22493,amazon/bort,Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements,hyunwoongko/reddit-9B,0.0391536587849259,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22494,lannelin/bert-imdb-1hidden,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
22495,piotr-rybak/poleval2021-task4-herbert-large-encoder,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0380220785737037,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
22496,google/t5-efficient-large-nl20,T5-Efficient-LARGE-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl20,1.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
22497,google/t5-efficient-tiny-nl24,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0411891061812639,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
22498,KoichiYasuoka/roberta-base-ukrainian,"The model has 10 layers, 6 attention heads, 768 hidden units and 3072 feed forward size",castorini/afriberta_large,0.033263374119997,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
22499,nyu-mll/roberta-base-10M-3,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
22500,google/t5-efficient-large-dm256,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0397702995687723,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
22501,razent/SciFive-base-PMC,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
22502,google/t5-efficient-base-el8,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0462406370788812,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
22503,google/t5-efficient-base-nh8,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0640757009387016,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
22504,mrp/simcse-model-wangchanberta,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.028332132846117,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
22505,microsoft/layoutlm-large-uncased,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0802314430475235,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
22506,google/t5-efficient-tiny-nl32,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
22507,patrickvonplaten/unispeech-sat-large-timit-ft,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
22508,stas/pegasus-cnn_dailymail-tiny-random,The hyperparameters for training followed the setting for RoBERTa,KBLab/megatron-bert-large-swedish-cased-165k,0.0581777449697255,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
22509,google/t5-efficient-large-nh8,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0670502074062824,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
22510,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0006177253089845,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
22511,google/t5-efficient-large-kv128,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0129651958122849,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
22512,sentence-transformers/paraphrase-MiniLM-L3-v2,There are also BiLSTM models available as well as these two models in 'https://github,Parsa/BBB_prediction_classification_SMILES,0.0534590780735015,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
22513,TJMUCH/transcriptome-iseeek,You can use BelGPT-2 with 🤗 transformers:Below is the list of all French copora used to pre-trained the model:,antoiloui/belgpt2,0.0734148621559143,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22514,MoritzLaurer/MiniLM-L6-mnli,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
22515,google/t5-efficient-base-kv256,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
22516,ctoraman/RoBERTa-TR-medium-char,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0492069113999604,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
22517,superspray/electra_large_discriminator_squad2_custom_dataset,"The model that can directly translate between the 9,900 directions of 100 languages",facebook/m2m100-12B-avg-10-ckpt,0.0787367150187492,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
22518,google/t5-efficient-base-dm2000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0358329452574252,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
22519,google/t5-efficient-tiny-ff12000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0440668957307935,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
22520,mwesner/reformer-clm,T5-Efficient-XL-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl12,0.0967821031808853,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
22521,negfir/distilbert-base-uncased-finetuned-cola,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0760693922638893,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
22522,google/bert_uncased_L-8_H-768_A-12,The smaller BERT models are intended for environments with restricted computational resources,google/bert_uncased_L-8_H-768_A-12,1.0,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
22523,amazon/bort,"Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint",KBLab/megatron-bert-large-swedish-cased-165k,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22524,minimaxir/magic-the-gathering,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0345502002164721,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
22525,google/t5-efficient-base-dm512,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0306824436411261,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
22526,microsoft/layoutlm-large-uncased,LayoutLM archives the SOTA results on multiple datasets,microsoft/layoutlm-large-uncased,1.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
22527,microsoft/layoutlm-base-uncased,This is a Lonformer version of the SciBERT uncased model by Allen AI,yorko/scibert_scivocab_uncased_long_4096,0.0540755316615104,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22528,facebook/blenderbot-90M,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
22529,MoritzLaurer/MiniLM-L6-mnli,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
22530,google/t5-efficient-large-nh12,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0500543285161256,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
22531,hfl/rbtl3,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0440182136371731,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
22532,google/t5-efficient-large-nh2,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0304302312433719,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
22533,mrm8488/deberta-v3-small-finetuned-mrpc,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0212866365909576,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
22534,hfl/rbt3,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbt3,1.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22535,rdenadai/BR_BERTo,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
22536,mwesner/bart-mlm,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0879560112953186,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
22537,abdusahmbzuai/aradia-ctc-distilhubert-ft,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0816234797239303,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
22538,everdoubling/byt5-Korean-small,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
22539,google/t5-efficient-large-nh2,T5-Efficient-LARGE-NH2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh2,1.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
22540,mwesner/reformer-clm,T5-Efficient-BASE-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff12000,0.0680235475301742,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
22541,malay-huggingface/xlnet-base-bahasa-cased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,8.398418625193509e-05,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
22542,fnlp/bart-large-chinese,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
22543,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl48,0.0914970561861991,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
22544,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
22545,google/t5-efficient-small-kv128,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0599695276468991,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
22546,piotr-rybak/poleval2021-task4-herbert-large-encoder,You can find more information in our paper AraGPT2The code in this repository was used to train all GPT2 variants,aubmindlab/aragpt2-mega,0.0787135958671569,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
22547,SupriyaArun/squeezebert-uncased-finetuned-squad,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0881360694766044,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22548,beston91/gpt2-xl_ft_logits_10k,I have made no attempts to censor,bigjoedata/rockbot,0.0025939177721738,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
22549,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
22550,hfl/rbtl3,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
22551,sentence-transformers/paraphrase-MiniLM-L3-v2,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0045264959335327,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
22552,google/t5-efficient-small-kv128,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.02154953032732,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
22553,google/t5-efficient-base-nl2,Its original repository can be found here,tau/splinter-large-qass,0.0612607728689908,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
22554,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0755272507667541,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
22555,cammy/led-large-16384-arxiv-100-MDS,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0695428997278213,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
22556,google/t5-efficient-base-el8,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0768850483000278,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
22557,razent/SciFive-base-PMC,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
22558,readerbench/RoBERT-small,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0295805003494023,-1.0,NLP,af9865c1-d52f-4c4d-8696-c19b9085823b___RoBERT-small,HF
22559,1one5ome/gpt2-chinese-gulong,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0673639550805091,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
22560,nghuyong/ernie-tiny,I have made no attempts to censor,bigjoedata/rockbot,0.0240479732553164,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
22561,razent/SciFive-large-Pubmed,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0419739885255694,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
22562,hf-internal-testing/tiny-random-rembert,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
22563,skt/ko-gpt-trinity-1.2B-v0.5,"Ko-GPT-Trinity refers to the class of models, while 1.2B represents the number of parameters of this particular pre-trained model",skt/ko-gpt-trinity-1.2B-v0.5,1.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
22564,pszemraj/led-large-book-summary,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0507907178252935,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
22565,google/t5-efficient-xl-nl16,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0155161470174789,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
22566,google/t5-efficient-xl-nl4,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
22567,clue/roberta_chinese_3L312_clue_tiny,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0260806251317262,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
22568,google/t5-efficient-large-el6,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0656608473509549,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
22569,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0068693086504936,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
22570,google/t5-efficient-small-ff6000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.046686939895153,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
22571,google/t5-efficient-small-el4,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
22572,TristanBehrens/js-fakes-4bars,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0336460590362548,-1.0,NLP,84456894-0906-4840-8de2-f427e2536a64___js-fakes-4bars,HF
22573,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.071739450097084,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
22574,TJMUCH/transcriptome-iseeek,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22575,sentence-transformers/paraphrase-MiniLM-L3-v2,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0786423534154892,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
22576,tartuNLP/gpt-4-est-large,"This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ""hidden_act"": ""relu"" entry",madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,0.0964665189385414,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
22577,google/t5-efficient-base-nh8,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0497321728616952,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
22578,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-SMALL-EL64 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el64,0.0762008279561995,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
22579,cammy/led-base-16384-100-MDS,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0997891277074813,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
22580,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0175750758498907,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
22581,TJMUCH/transcriptome-iseeek,The original language model has been trained using PyTorch and hence the use of pytorch_model,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0878544300794601,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22582,microsoft/layoutlm-base-cased,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0349774472415447,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
22583,google/t5-efficient-large-nh32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
22584,google/t5-efficient-small-dm128,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-dm128,1.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
22585,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0332139357924461,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
22586,google/t5-efficient-tiny-nl24,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0553081743419169,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
22587,facebook/wmt21-dense-24-wide-en-x,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.027660872042179,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
22588,indobenchmark/indobert-base-p1,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0499108669658501,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
22589,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,This does not make a very readable text,pere/DeUnCaser,0.0234820395708084,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
22590,lgrobol/electra-minuscule-generator,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
22591,hfl/rbt3,T5-Efficient-TINY-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff12000,0.0634988211095332,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22592,amazon/bort,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0759395211935043,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22593,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0776152163743972,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
22594,everdoubling/byt5-Korean-small,"If you use our model or find it useful in your projects, please cite our work:",jcblaise/electra-tagalog-small-cased-generator,0.016701591014862,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
22595,google/t5-efficient-small-nl36,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0725361220538616,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
22596,google/t5-efficient-tiny-ff12000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0462513156235217,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
22597,google/t5-efficient-small-dm256,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0774553641676902,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
22598,google/t5-efficient-small-el32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
22599,google/t5-efficient-xl-nl4,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
22600,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0762820914387703,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
22601,uer/bart-chinese-4-768-cluecorpussmall,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0761555582284927,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
22602,google/t5-efficient-tiny-nl32,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.094790544360876,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
22603,cammy/led-large-16384-arxiv-100-MDS,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0614619106054306,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
22604,google/t5-efficient-small-nl2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0351753477007149,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
22605,google/t5-efficient-small-el4,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0639073997735977,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
22606,ml6team/mt5-small-german-finetune-mlsum,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
22607,mimicheng/codeparrot-ds,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0819044709205627,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
22608,cammy/led-large-16384-arxiv-100-MDS,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0494462400674819,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
22609,gayanin/bart-mlm-pubmed-medterm,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
22610,PlanTL-GOB-ES/gpt2-base-bne,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-base-bne,1.0,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
22611,TristanBehrens/js-fakes-4bars,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0215219378471374,-1.0,NLP,84456894-0906-4840-8de2-f427e2536a64___js-fakes-4bars,HF
22612,DemangeJeremy/4-sentiments-with-flaubert,"ELECTRA models are trained to distinguish ""real"" input tokens vs ""fake"" input tokens generated by another neural network, similar to the discriminator of a GAN",skplanet/dialog-koelectra-small-generator,0.0614998266100883,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
22613,tau/tavbert-he,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0552253387868404,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
22614,google/t5-efficient-large-nh32,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0337783694267272,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
22615,negfir/distilbert-base-uncased-finetuned-squad,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.079643115401268,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
22616,saibo/random-roberta-tiny,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,1.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
22617,microsoft/layoutlm-large-uncased,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.099951758980751,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
22618,hfl/chinese-xlnet-mid,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0028439899906515,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
22619,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0401910431683063,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
22620,tau/tavbert-he,Apply for Task 1: Legal Document Retrieval on ALQAC 2021 datasetThe model achieved 0.80 on the leaderboard(1st place score is 0.88),ductuan024/AimeLaw,0.0454329606145619,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
22621,rmihaylov/pegasus-base-cnn-dailymail-bg,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0289601488038897,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
22622,TJMUCH/transcriptome-iseeek,We use SimCSE here by using mBERT as the baseline model and training the model with Thai Wikipedia hereUsing this model becomes easy when you have sentence-transformers installed:,mrp/simcse-model-wangchanberta,0.0352075658738613,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22623,DTAI-KULeuven/robbertje-1-gb-bort,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0152329482759038,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
22624,ncduy/phobert-large-finetuned-vietnamese_students_feedback,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0600392557680606,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
22625,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
22626,SupriyaArun/squeezebert-uncased-finetuned-squad,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0431437715888023,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22627,microsoft/layoutlm-large-uncased,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,0.0583458803594112,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
22628,mwesner/reformer-clm,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.066567562520504,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
22629,mimicheng/codeparrot-ds,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
22630,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0261073745787143,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
22631,zoha/wav2vec2-base-timit-demo-colab,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
22632,patrickvonplaten/unispeech-sat-large-timit-ft,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0827564746141433,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
22633,ian0delond/model-test,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0817174911499023,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
22634,tartuNLP/gpt-4-est-large,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0742563754320144,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
22635,nguyenvulebinh/envibert,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0304460984965165,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
22636,microsoft/layoutlm-base-cased,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-8_H-768_A-12,0.0839941129088401,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
22637,google/t5-efficient-small-ff6000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0255329934880137,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
22638,nherve/flaubert-oral-ft,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
22639,google/t5-efficient-tiny-ff12000,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0681503619998692,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
22640,google/t5-efficient-small-dm1000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
22641,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.028393186815083,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
22642,google/t5-efficient-base-kv16,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0222300663590431,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
22643,google/t5-efficient-small-ff1000,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0955252796411514,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
22644,allenai/wmt19-de-en-6-6-big,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0644376967102288,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
22645,superspray/electra_large_discriminator_squad2_custom_dataset,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0309438034892082,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
22646,google/t5-efficient-base-kv256,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.02197827398777,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
22647,gayanin/bart-mlm-pubmed-medterm,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0664188265800476,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
22648,andrejmiscic/simcls-scorer-billsum,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
22649,google/t5-efficient-base-kv16,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0522606521844863,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
22650,stas/mt5-tiny-random,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0064425263553857,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
22651,google/t5-efficient-base-nl36,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.070308081805706,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
22652,mwesner/reformer-clm,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,0.0478422529995441,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
22653,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0652926340699195,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
22654,google/t5-efficient-xl-nl28,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
22655,google/t5-efficient-base-kv16,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
22656,stas/pegasus-cnn_dailymail-tiny-random,"To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus",FremyCompany/xls-r-nl-v1-cv8-lm,0.0802979692816734,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
22657,google/t5-efficient-large-nl10,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0010928530246019,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
22658,hyunwoongko/reddit-9B,Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements,hyunwoongko/reddit-9B,1.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
22659,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0550004243850708,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
22660,microsoft/unihanlm-base,PaperUse it like how you use XLM :)The training corpus is solely from Wikipedia so the model may perform worse on informal text data,microsoft/unihanlm-base,1.0,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
22661,diper1998/distilgpt2-finetuned-AT,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0923861637711525,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
22662,google/t5-efficient-large-nh8,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0487230699509381,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
22663,IDEA-CCNL/Erlangshen-Longformer-110M,This model is case-sensitive: it makes a difference between finnish and Finnish,flax-community/RoBERTa-large-finnish,0.0057517359964549,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
22664,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0792838037014007,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
22665,groar/gpt-neo-1.3B-finetuned-escape2,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0762899667024612,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
22666,google/t5-efficient-small-el64,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-el64,1.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
22667,google/t5-efficient-small-el32,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0596390515565871,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
22668,google/t5-efficient-small-el4,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0334210926666856,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
22669,negfir/distilbert-base-uncased-finetuned-cola,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0595585778355598,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
22670,google/t5-efficient-xl-nl6,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.047962162643671,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
22671,saurkulsh/T0pp,These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language,saurkulsh/T0pp,1.0,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
22672,mrm8488/deberta-v3-small-finetuned-mrpc,Generated from the official Squad2 evaluation scriptMuRIL is specifically trained to work on 18 Indic languages and English,Sindhu/muril-large-squad2,0.0953326076269149,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
22673,google/t5-efficient-small-nl2,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0710784010589122,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
22674,ctoraman/RoBERTa-TR-medium-char,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,1.0,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
22675,google/t5-efficient-base-ff12000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0547039471566676,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
22676,stas/t5-very-small-random,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0178523659706115,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
22677,mimicheng/codeparrot-ds,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.088455781340599,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
22678,google/t5-efficient-small-el4,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0427436269819736,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
22679,google/t5-efficient-tiny-nl2,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0578952394425869,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
22680,jorge-henao/gpt2-small-spanish-disco-poetry-wt,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0385385826230049,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
22681,facebook/blenderbot-90M,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",facebook/blenderbot-90M,1.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
22682,aditeyabaral/sentencetransformer-bert-hinglish-small,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0749478563666343,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
22683,google/t5-efficient-base-kv256,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0741521194577216,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
22684,jweb/japanese-soseki-gpt2-1b,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
22685,stas/t5-very-small-random,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.053639605641365,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
22686,google/t5-efficient-small-nl22,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0694188345223664,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
22687,google/t5-efficient-large-nl32,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0600831229239701,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
22688,amazon/bort,"ByT5 works especially well on noisy text data,e",google/byt5-xl,0.0529028251767157,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22689,sonoisa/byt5-small-japanese,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0566737242043018,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
22690,fnlp/bart-base-chinese,TransQuest outperforms current open-source quality estimation frameworks such as OpenKiwi and DeepQuest,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0606077443808317,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
22691,negfir/distilbert-base-uncased-finetuned-cola,"To the best of our knowledge, CPM is the largest Chinese pre-trained language model, which could facilitate downstream Chinese NLP tasks, such as conversation, essay generation, cloze test, and language understanding",TsinghuaAI/CPM-Generate,0.0918932408094406,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
22692,microsoft/layoutlm-base-uncased,Its original repository can be found here,tau/splinter-large-qass,0.0014627575874328,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22693,RabotaRu/HRBert-mini,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0766233410686254,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
22694,rahulacj/bertweet-base-finetuned-sentiment-analysis,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0282867103815078,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
22695,google/t5-efficient-small-dm128,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
22696,IDEA-CCNL/Erlangshen-Longformer-330M,You can use the raw model for summarization,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0213689841330051,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
22697,diper1998/distilgpt2-finetuned-AT,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0445441156625747,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
22698,google/t5-efficient-base-nh8,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0378723144531249,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
22699,google/t5-efficient-tiny-nh8,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
22700,aditeyabaral/sentencetransformer-bert-hinglish-small,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.042570985853672,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
22701,sonoisa/byt5-small-japanese,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0497602894902229,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
22702,hakurei/gpt-j-random-tinier,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.031915372237563,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
22703,danyaljj/opengpt2_pytorch_forward,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0458492413163184,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
22704,google/t5-efficient-large-dm128,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0759538151323795,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
22705,hfl/rbt3,"However, this model outperforms the base model by large margins",tau/splinter-large-qass,0.0435438584536313,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22706,jhu-clsp/bibert-ende,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
22707,ErykWdowiak/GPTalian,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0357050493359565,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
22708,malay-huggingface/t5-super-tiny-bahasa-cased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0088870674371719,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
22709,skt/ko-gpt-trinity-1.2B-v0.5,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
22710,cammy/led-large-16384-arxiv-100-MDS,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
22711,Parsa/BBB_prediction_classification_SMILES,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0177432869871457,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
22712,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0714981704950332,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
22713,mwesner/reformer-clm,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0558750256896019,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
22714,google/t5-efficient-large-nh4,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
22715,brad1141/Longformer-finetuned-norm,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
22716,google/t5-efficient-base-kv16,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.05519500374794,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
22717,DemangeJeremy/4-sentiments-with-flaubert,I have made no attempts to censor,bigjoedata/rockbot,0.0172317493706941,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
22718,SupriyaArun/squeezebert-uncased-finetuned-squad,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,0.0290912929922342,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22719,google/t5-efficient-large-kv32,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0448771119117736,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
22720,akdeniz27/deberta-v2-xlarge-cuad,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0123078683391213,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
22721,tartuNLP/gpt-4-est-base,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0679518040269613,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
22722,ErykWdowiak/GPTalian,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
22723,allenai/wmt16-en-de-dist-6-1,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
22724,sultan/BioM-ELECTRA-Large-Generator,Our findings highlight the significant effect of design choices on improving the performance of biomedical language models,sultan/BioM-ELECTRA-Large-Generator,1.0,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
22725,google/t5-efficient-small-dm768,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
22726,stas/t5-very-small-random,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0271713100373744,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
22727,tau/tavbert-he,T5-Efficient-BASE-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff1000,0.0066527985036372,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
22728,dbmdz/electra-small-turkish-cased-discriminator,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0599593147635459,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
22729,lgrobol/electra-minuscule-generator,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
22730,amazon/bort,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22731,amazon/bort,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0538276806473732,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22732,flax-community/gpt2-medium-persian,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0245497518529494,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
22733,ToToKr/kobigbird-bert-base-finetuned-klue,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0812385231256485,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
22734,eli4s/Bert-L12-h256-A4,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.029204580001533,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
22735,yarongef/DistilProtBert,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0727936575810114,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
22736,hfl/rbt3,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22737,mwesner/reformer-clm,T5-Efficient-SMALL-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff12000,0.0983632430434227,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
22738,prajjwal1/bert-mini,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,d524d2bf-0c8c-4e35-817b-27b8261b2166___bert-mini,HF
22739,google/t5-efficient-tiny-ff6000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0342924688011407,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
22740,google/t5-efficient-small-kv32,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0517312642186879,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
22741,google/t5-efficient-base-nl4,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0728131458163261,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
22742,mwesner/reformer-clm,Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture,skt/ko-gpt-trinity-1.2B-v0.5,0.0468726567924022,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
22743,google/t5-efficient-tiny-nl16,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0144474976696073,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
22744,mimi/Waynehills-NLP-doogie,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0683086067438125,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
22745,google/t5-efficient-base-kv128,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
22746,mimicheng/codeparrot-ds,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
22747,cross-encoder/nli-deberta-v3-xsmall,This model is based on microsoft/deberta-v3-xsmallThe model was trained on the SNLI and MultiNLI datasets,cross-encoder/nli-deberta-v3-xsmall,1.0,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
22748,razent/SciFive-large-Pubmed,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0464964639395475,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
22749,dbmdz/electra-small-turkish-cased-discriminator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
22750,GKLMIP/electra-myanmar-small-uncased,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,1.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
22751,google/t5-efficient-small-kv32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0789946690201759,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
22752,ian0delond/model-test,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0693086087703704,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
22753,google/t5-efficient-small-ff9000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0542971529066561,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
22754,hfl/rbtl3,T5-Efficient-BASE-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff9000,0.0227709785103798,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
22755,google/t5-efficient-large-nh12,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0734654515981673,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
22756,anirudh21/xlnet-base-cased-finetuned-rte,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.042171936482191,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
22757,clue/roberta_chinese_3L312_clue_tiny,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
22758,rinna/japanese-gpt-1b,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0233685026566187,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
22759,mgreenbe/bertlet-base-uncased-for-sequence-classification,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0803834497928619,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
22760,microsoft/layoutlm-base-uncased,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0491154454648494,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22761,negfir/distilbert-base-uncased-finetuned-cola,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0934567004442215,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
22762,DemangeJeremy/4-sentiments-with-flaubert,"This model, ibert-roberta-base, is an integer-only quantized version of RoBERTa, and was introduced in this paper",kssteven/ibert-roberta-base,0.0428676032461225,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
22763,flboehm/reddit-bert-text_5,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0698983147740364,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
22764,1one5ome/gpt2-chinese-gulong,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0450822822749614,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
22765,uer/t5-v1_1-small-chinese-cluecorpussmall,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0707081705331801,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
22766,soheeyang/rdr-ctx_encoder-single-trivia-base,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0378208694358666,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
22767,brad1141/Longformer-finetuned-norm,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.031280655413866,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
22768,piotr-rybak/poleval2021-task4-herbert-large-encoder,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0279886927455663,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
22769,allenai/wmt19-de-en-6-6-big,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
22770,TJMUCH/transcriptome-iseeek,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22771,google/t5-efficient-tiny-ff12000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0572494119405746,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
22772,hfl/rbt3,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.027769286185503,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22773,google/t5-efficient-base-kv16,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0116478689014911,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
22774,google/t5-efficient-large-nh8-nl32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0388947408646345,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
22775,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0512662082910537,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
22776,google/t5-efficient-mini,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0931163355708122,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
22777,anirudh21/xlnet-base-cased-finetuned-rte,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0670495256781578,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
22778,google/t5-efficient-tiny-nl24,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0703300051391124,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
22779,google/t5-efficient-base-dm2000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0137381181120872,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
22780,google/t5-efficient-large-nh8-nl32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.029503721743822,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
22781,everdoubling/byt5-Korean-small,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0211137868463992,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
22782,google/t5-efficient-base-nl4,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0457070097327231,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
22783,amazon/bort,"To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus",FremyCompany/xls-r-nl-v1-cv8-lm,0.0626481361687183,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22784,diper1998/distilgpt2-finetuned-AT,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0545370019972324,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
22785,stas/t5-very-small-random,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0577020309865474,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
22786,sentence-transformers/paraphrase-MiniLM-L3-v2,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0090442039072513,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
22787,malay-huggingface/xlnet-tiny-bahasa-cased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0118908553073804,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
22788,jnz/electra-ka,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0255466289818286,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
22789,tuner007/pegasus_qa,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0499685741961002,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
22790,google/t5-efficient-tiny-nl32,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0726597476750611,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
22791,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0721542760729789,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
22792,google/t5-efficient-base-ff12000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
22793,hfl/chinese-electra-180g-small-ex-discriminator,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
22794,google/t5-efficient-large-nh24,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0860918536782264,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
22795,mimi/Waynehills-NLP-doogie,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0613209418952465,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
22796,TJMUCH/transcriptome-iseeek,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0840573906898498,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
22797,uer/chinese_roberta_L-6_H-128,have shown that the standard BERT recipe is effective on a wide range of model sizes,uer/chinese_roberta_L-6_H-128,1.0,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
22798,google/t5-efficient-small-kv128,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
22799,minimaxir/magic-the-gathering,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,0.0414576460607349,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
22800,jnz/electra-ka,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
22801,google/t5-efficient-base-nh32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0124368704855442,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
22802,google/t5-efficient-base-nl48,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0174864511936902,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
22803,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0687552243471145,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
22804,tucan9389/kcbert-base-finetuned-squad,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0061627905815839,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
22805,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
22806,mimi/Waynehills-NLP-doogie,Its original repository can be found here,tau/splinter-large-qass,0.037224743515253,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
22807,aditeyabaral/sentencetransformer-bert-hinglish-small,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0998425930738449,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
22808,tau/tavbert-he,T5-Efficient-SMALL-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff3000,0.0280190352350472,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
22809,mnaylor/bigbird-base-mimic-mortality,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
22810,hfl/rbtl3,T5-Efficient-SMALL-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv32,0.0641852170228958,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
22811,beston91/gpt2-xl_ft_logits_10k,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0187661349773406,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
22812,negfir/distilbert-base-uncased-finetuned-squad,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
22813,google/t5-efficient-small-nl22,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0613480154424904,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
22814,Iskaj/hf-challenge-test,† ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-162M,0.0878603607416153,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
22815,google/t5-efficient-xxl-nl4,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
22816,malay-huggingface/t5-super-tiny-bahasa-cased,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0254073804244398,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
22817,akdeniz27/deberta-v2-xlarge-cuad,ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants,hfl/chinese-legal-electra-base-generator,0.0737567022442817,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
22818,1one5ome/gpt2-chinese-gulong,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0255342461168766,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
22819,Finnish-NLP/convbert-base-generator-finnish,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0424108419567346,-1.0,NLP,7c411417-1a85-458e-9966-961ffb11f12a___convbert-base-generator-finnish,HF
22820,Fujitsu/pytorrent,Pretrained weights based on PyTorrent Dataset which is a curated data from a large official Python packages,Fujitsu/pytorrent,1.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
22821,celine98/canine-s-finetuned-sst2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
22822,obss/mt5-base-3task-highlight-tquad2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0225057248026132,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
22823,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,This model was created using the nn_pruning python library: the linear layers contains 27.0% of the original  weights,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,1.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
22824,google/t5-efficient-small-nl36,This does not make a very readable text,pere/DeUnCaser,0.0570349562913178,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
22825,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0160932037979364,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
22826,microsoft/layoutlm-base-uncased,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0868481695652008,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22827,microsoft/layoutlm-base-uncased,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0546437948942184,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22828,uer/bart-chinese-4-768-cluecorpussmall,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
22829,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl36,0.0902293659746646,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
22830,fnlp/bart-base-chinese,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0262773260474204,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
22831,Milos/slovak-gpt-j-405M,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
22832,rdenadai/BR_BERTo,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 240",eli4s/Bert-L12-h240-A12,0.0250780147810776,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
22833,google/t5-efficient-tiny-nh16,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
22834,bookbot/distil-wav2vec2-adult-child-cls-37m,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0106764944891134,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
22835,google/t5-efficient-small-dm128,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0860485322773456,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
22836,lgris/distilxlsr_bp_8-12,Its original repository can be found here,tau/splinter-large-qass,0.0070603381221492,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
22837,mwesner/reformer-clm,"Slovak GPT-J-1.4B with the whopping 1,415,283,792 parameters is the latest and the largest model released in Slovak GPT-J series",Milos/slovak-gpt-j-1.4B,0.0901668518781662,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
22838,clue/roberta_chinese_3L312_clue_tiny,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
22839,google/t5-efficient-base-ff1000,T5-Efficient-BASE-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff1000,1.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
22840,google/t5-efficient-small-kv128,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0788008123636245,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
22841,rahulacj/bertweet-base-finetuned-sentiment-analysis,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0858289524912834,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
22842,hfl/rbt3,T5-Efficient-LARGE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm256,0.0445603039115666,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
22843,google/t5-efficient-tiny-nl16,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
22844,microsoft/layoutlm-base-uncased,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0986369401216507,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22845,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0457966029644012,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
22846,amazon/bort,The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider max_seq_length (4096 vs,yorko/scibert_scivocab_uncased_long_4096,0.0462273224256932,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22847,jnz/electra-ka,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0914002507925033,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
22848,hfl/rbtl3,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
22849,fnlp/bart-large-chinese,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0830008424818515,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
22850,google/bert_uncased_L-8_H-128_A-2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
22851,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0259018875658512,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
22852,brad1141/Longformer-finetuned-norm,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0748801901936531,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
22853,google/t5-efficient-xl-nl28,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0590273961424827,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
22854,malay-huggingface/xlnet-tiny-bahasa-cased,xlnet-tiny-bahasa-cased model was pretrained on ~1.4 Billion words,malay-huggingface/xlnet-tiny-bahasa-cased,1.0,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
22855,google/t5-efficient-large-kv256,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
22856,SupriyaArun/squeezebert-uncased-finetuned-squad,"With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in WMT 2020",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0819442868232727,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22857,hfl/rbtl3,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.0774548575282096,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
22858,google/t5-efficient-large-el4,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0725526362657546,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
22859,castorini/afriberta_large,AfriBERTa large is a pretrained multilingual language model with around 126 million parameters,castorini/afriberta_large,1.0,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
22860,facebook/m2m100-12B-avg-10-ckpt,"Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example",facebook/m2m100-12B-avg-10-ckpt,1.0,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
22861,jweb/japanese-soseki-gpt2-1b,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0229535996913909,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
22862,nghuyong/ernie-tiny,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.048539537936449,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
22863,amazon/bort,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22864,FremyCompany/xls-r-nl-v1-cv8-lm,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0081824441440403,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
22865,diper1998/distilgpt2-finetuned-AT,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0386497564613819,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
22866,microsoft/unihanlm-base,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
22867,google/t5-efficient-base-nl40,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0803948938846587,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
22868,jhu-clsp/bibert-ende,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0650745493670304,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
22869,google/t5-efficient-base-ff12000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
22870,google/t5-efficient-xl-nl28,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0464868415147066,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
22871,google/t5-efficient-large-nh8-nl32,T5-Efficient-LARGE-NH8-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8-nl32,1.0,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
22872,google/t5-efficient-small-ff3000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0410246374085545,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
22873,negfir/distilbert-base-uncased-finetuned-cola,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,0.0778817683458328,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
22874,jorge-henao/gpt2-small-spanish-disco-poetry-wt,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0974638238549232,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
22875,google/t5-efficient-tiny-nl2,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0632166787981986,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
22876,skt/ko-gpt-trinity-1.2B-v0.5,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
22877,google/t5-efficient-mini-nl24,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0626212563365697,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
22878,google/t5-efficient-small-kv16,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
22879,google/t5-efficient-large-el8,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0337127279490232,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
22880,microsoft/deberta-v2-xxlarge-mnli,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0159031972289085,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
22881,mwesner/reformer-clm,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0928854271769523,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
22882,google/t5-efficient-large-kv128,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0486031658947467,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
22883,mrm8488/deberta-v3-small-finetuned-mrpc,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0639027655124664,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
22884,superspray/electra_large_discriminator_squad2_custom_dataset,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0491221658885479,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
22885,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0947353169322013,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
22886,google/t5-efficient-large-nh8-nl32,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0536784622818231,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
22887,google/t5-efficient-mini,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0564306918531656,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
22888,allenai/wmt19-de-en-6-6-big,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0452568433247506,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
22889,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0721087306737899,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
22890,google/t5-efficient-large-dm256,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0793562680482864,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
22891,lgrobol/electra-minuscule-generator,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0209391675889491,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
22892,sberbank-ai/rugpt3medium_based_on_gpt2,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
22893,hfl/chinese-electra-180g-small-ex-discriminator,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0641145917276541,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
22894,google/bert_uncased_L-10_H-768_A-12,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
22895,brad1141/Longformer-finetuned-norm,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0496313050389289,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
22896,saibo/random-roberta-mini,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0409307712689042,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
22897,google/t5-efficient-mini,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0493898224085568,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
22898,stas/t5-very-small-random,"Encoder structure-based Bidirection language model, focusing on solving various natural language understanding tasks",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0901694372296333,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
22899,hfl/chinese-xlnet-mid,It's important to note that tokenizer of random-roberta-mini is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-mini,0.0661520759264627,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
22900,google/t5-efficient-base-nh24,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0956840477883815,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
22901,google/t5-efficient-base-el6,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0312208738178014,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
22902,google/bigbird-roberta-large,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0135795399546622,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
22903,google/t5-efficient-tiny-ff12000,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0168074471876025,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
22904,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-8_H-768_A-12,0.0854707956314086,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
22905,google/t5-efficient-base-kv128,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0055965669453144,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
22906,j-hartmann/purchase-intention-english-roberta-large,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
22907,google/t5-efficient-base-nl40,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0697790458798408,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
22908,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.038490479812026,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
22909,microsoft/layoutlm-large-uncased,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0859142318367958,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
22910,akdeniz27/deberta-v2-xlarge-cuad,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0407670810818672,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
22911,google/t5-efficient-large-nl12,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0771660506725311,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
22912,mrm8488/xlm-multi-finetuned-xquadv1,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0830765441060065,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
22913,qarib/bert-base-qarib_far_6500k,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
22914,google/t5-efficient-tiny-nl12,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0500214267522095,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
22915,hfl/rbtl3,T5-Efficient-BASE-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff6000,0.0317304758355021,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
22916,qarib/bert-base-qarib_far_6500k,Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity,google/bert_uncased_L-8_H-768_A-12,0.0182129293680191,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
22917,SupriyaArun/squeezebert-uncased-finetuned-squad,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
22918,filco306/gpt2-switchboard-paraphraser,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.017789022065699,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
22919,aditeyabaral/sentencetransformer-bert-hinglish-small,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0947354063391685,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
22920,google/t5-efficient-small-dm256,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0417156023904681,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
22921,flboehm/reddit-bert-text_5,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0320322476327419,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
22922,microsoft/layoutlm-base-cased,We do hope our model might be used for both academic and industrial applications,asi/gpt-fr-cased-base,0.0487392172217369,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
22923,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Zero-shot SELECTRA is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset,Recognai/zeroshot_selectra_small,0.0937188565731048,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
22924,anas-awadalla/bert-tiny-finetuned-squad,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0577737241983413,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
22925,sonoisa/byt5-small-japanese,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,0.0758179426193237,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
22926,SetFit/deberta-v3-large__sst2__train-8-8,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
22927,suzuki/distilbert-base-uncased-finetuned-squad,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0842504054307937,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
22928,fnlp/bart-large-chinese,TransQuest outperforms current open-source quality estimation frameworks such as OpenKiwi and DeepQuest,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0603921301662921,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
22929,j-hartmann/purchase-intention-english-roberta-large,"Then, it was compressed via progressive module replacing",rmihaylov/bert-base-pos-theseus-bg,0.0106853228062391,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
22930,google/t5-efficient-large-dm512,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0487488340586423,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
22931,google/t5-efficient-large-kv128,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0027380343526602,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
22932,google/t5-efficient-small-nl40,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.027451068162918,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
22933,nherve/flaubert-oral-ft,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0564360196391741,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
22934,hfl/chinese-electra-180g-small-ex-discriminator,"For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA",hfl/chinese-electra-180g-small-ex-discriminator,1.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
22935,google/t5-efficient-tiny-ff9000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0833317004144191,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
22936,google/t5-efficient-large-el4,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0367969069629907,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
22937,clue/xlnet_chinese_large,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.072602966800332,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
22938,flboehm/reddit-bert-text_5,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0438552908599376,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
22939,crabz/distil-slovakbert-ner,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0674046725034713,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
22940,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0656420737504959,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
22941,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0892520695924758,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
22942,DemangeJeremy/4-sentiments-with-flaubert,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0889462307095527,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
22943,dbmdz/convbert-base-german-europeana-cased,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0585855692625045,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
22944,microsoft/layoutlm-base-uncased,This BERT model was trained using the Megatron-LM library,KBLab/megatron-bert-large-swedish-cased-165k,0.0544226467609405,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22945,pszemraj/led-large-book-summary,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
22946,DTAI-KULeuven/robbertje-1-gb-bort,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
22947,google/t5-efficient-xl-nl8,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-xl-nl8,1.0,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
22948,crabz/distil-slovakbert-ner,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0346596390008926,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
22949,abdusahmbzuai/ft-tatoeba-ar-en,I have made no attempts to censor,bigjoedata/rockbot,0.0978025048971176,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
22950,google/t5-efficient-large-dm256,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0757289826869964,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
22951,amazon/bort,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0045167431235313,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22952,google/t5-efficient-large-nh8,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0786331892013549,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
22953,mimi/Waynehills-NLP-doogie,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
22954,amazon/bort,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0706117134541272,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22955,FremyCompany/xls-r-nl-v1-cv8-lm,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0051827509887516,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
22956,hfl/chinese-electra-180g-small-generator,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0512275919318198,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
22957,enelpol/czywiesz-context,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
22958,abdusahmbzuai/ft-tatoeba-ar-en,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.039228968322277,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
22959,akdeniz27/deberta-v2-xlarge-cuad,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0872084870934486,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
22960,tau/tavbert-he,T5-Efficient-BASE-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff9000,0.0002864906564355,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
22961,google/t5-efficient-large-el8,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0760439690202474,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
22962,beston91/gpt2-xl_ft_logits_10k,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0816347151994705,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
22963,uer/t5-v1_1-base-chinese-cluecorpussmall,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
22964,Recognai/selectra_medium,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0387569577433168,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
22965,IDEA-CCNL/Erlangshen-Longformer-330M,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0483618397265672,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
22966,google/t5-efficient-small-el16-dl4,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0246195718646048,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
22967,amazon/bort,"Besides, many tasks may be formatted such that the output is directly generated in natural language",asi/gpt-fr-cased-base,0.0217218790203332,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
22968,google/t5-efficient-mini-nl6,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.064156588166952,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
22969,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0009280455609162,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
22970,google/t5-efficient-large-nh24,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
22971,zoha/wav2vec2-base-timit-demo-colab,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0553450770676136,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
22972,hfl/chinese-legal-electra-base-generator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
22973,flax-community/RoBERTa-large-finnish,"Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words",flax-community/RoBERTa-large-finnish,1.0,-1.0,NLP,bd6a99f8-f445-4aab-803e-729051effeb2___RoBERTa-large-finnish,HF
22974,google/bert_uncased_L-4_H-128_A-2,The smaller BERT models are intended for environments with restricted computational resources,google/bert_uncased_L-4_H-128_A-2,1.0,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
22975,google/t5-efficient-large-kv32,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0242943987250328,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
22976,nghuyong/ernie-tiny,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.021561337634921,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
22977,wukevin/tcr-bert,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0234533436596393,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
22978,google/t5-efficient-base-el8,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0373495249077676,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
22979,microsoft/layoutlm-base-uncased,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22980,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0238822437822818,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
22981,hajime9652/xlnet-japanese,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0716300010681152,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
22982,google/t5-efficient-base-dm2000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0376113951206206,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
22983,mimi/Waynehills-NLP-doogie,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0386959835886955,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
22984,google/t5-efficient-small-el8,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
22985,tau/tavbert-he,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0866721719503402,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
22986,google/t5-efficient-base-el6,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.04617659188807,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
22987,kyryl0s/gpt2-uk-xxs,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
22988,google/t5-efficient-large-kv16,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0253651775419712,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
22989,stas/mt5-tiny-random,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0913110077381134,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
22990,google/t5-efficient-small-kv128,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0288701634854077,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
22991,google/t5-efficient-xl-nl2,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0612646173685788,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
22992,google/t5-efficient-xl-nl8,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0722759738564491,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
22993,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0073502386609712,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
22994,google/t5-efficient-tiny-nl16,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-tiny-nl16,1.0,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
22995,mrp/simcse-model-wangchanberta,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0055141625925899,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
22996,razent/SciFive-base-PMC,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0292650712653994,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
22997,microsoft/layoutlm-base-uncased,It has less than 1% difference in performance compared to the BERT based model while having 2.0x the speed,khizon/distilbert-unreliable-news-eng-4L,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
22998,google/t5-efficient-small-el64,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
22999,beston91/gpt2-xl_ft_logits_10k,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0887628346681594,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
23000,google/t5-efficient-base-dm256,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0526183657348155,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
23001,google/t5-efficient-mini,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0746551677584648,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
23002,minimaxir/magic-the-gathering,This model is a distilled version of wav2vec2-adult-child-cls on a private adult/child speech classification dataset,bookbot/distil-wav2vec2-adult-child-cls-52m,0.0494898343458771,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
23003,google/t5-efficient-small-kv256,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0597051149234175,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
23004,google/t5-efficient-tiny-nl12,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.063099998049438,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
23005,google/bert_uncased_L-8_H-128_A-2,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-8_H-128_A-2,1.0,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
23006,google/t5-efficient-base-el6,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0286392495036124,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
23007,mimicheng/codeparrot-ds,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0354565195739269,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
23008,brad1141/Longformer-finetuned-norm,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0868279933929443,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
23009,google/t5-efficient-large-nh4,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
23010,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.095844566822052,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
23011,hajime9652/xlnet-japanese,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0967071503400802,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
23012,google/t5-efficient-xl-nl16,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0966673791408538,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
23013,google/t5-efficient-tiny-nh32,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0537193380296229,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
23014,google/t5-efficient-small-dm1000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
23015,thu-coai/LongLM-large,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.040600098359088,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
23016,microsoft/layoutlm-base-cased,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0170902032405138,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23017,lannelin/bert-imdb-1hidden,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
23018,dbmdz/convbert-base-german-europeana-cased,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,1.0,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
23019,google/t5-efficient-xxl-nl4,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0663826074451207,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
23020,google/bert_uncased_L-10_H-256_A-4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.012903448815147,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
23021,clue/roberta_chinese_3L312_clue_tiny,"1, you could use these model as followsMODEL_NAME：",clue/roberta_chinese_3L312_clue_tiny,1.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
23022,google/t5-efficient-large-kv16,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0425402615219354,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
23023,google/t5-efficient-base-el16,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0444250786677002,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
23024,VietAI/gpt-neo-1.3B-vietnamese-news,This model is not expected to perform well in any other languages,Sindhu/muril-large-squad2,0.076527977362275,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23025,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0909158065915107,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
23026,hfl/rbt3,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,0.0178629495203495,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23027,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff6000,0.0594499316066503,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
23028,cammy/led-large-16384-arxiv-100-MDS,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.096470721065998,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
23029,google/t5-efficient-mini-nl24,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0269812229089439,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
23030,google/bert_uncased_L-10_H-768_A-12,I have made no attempts to censor,bigjoedata/rockbot,0.0217506180827816,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
23031,thu-coai/LongLM-large,T5-Efficient-SMALL-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv128,0.0546359050398071,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
23032,sentence-transformers/paraphrase-MiniLM-L3-v2,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0952547416090965,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
23033,mrm8488/bert-small-finetuned-squadv2,I have made no attempts to censor,bigjoedata/rockbot,0.0741390250623226,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
23034,lgrobol/electra-minuscule-generator,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0059879524633288,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
23035,SupriyaArun/squeezebert-uncased-finetuned-squad,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0374166741967201,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
23036,sentence-transformers/paraphrase-MiniLM-L3-v2,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0282419547438621,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
23037,TJMUCH/transcriptome-iseeek,Training metrics were logged via Tensorboard,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0861265510320663,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23038,krevas/finance-koelectra-base-generator,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0232656584121286,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
23039,amazon/bort,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.066215194761753,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23040,google/t5-efficient-base-ff9000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0161162922158837,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
23041,uer/chinese_roberta_L-2_H-256,"In order to facilitate users to reproduce the results, we used the publicly available corpus and provided all training details",uer/chinese_roberta_L-2_H-256,1.0,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
23042,negfir/distilbert-base-uncased-finetuned-cola,"Important: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch)",pszemraj/led-large-book-summary,0.078286737203598,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
23043,google/t5-efficient-base-dm1000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0494881095364689,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
23044,google/t5-efficient-base-el6,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0397017104551196,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
23045,abdusahmbzuai/ft-tatoeba-ar-en,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0822367966175079,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
23046,google/t5-efficient-large-el6,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0407069781795144,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
23047,IDEA-CCNL/Erlangshen-Longformer-330M,This model is cased: it does make a difference between bulgarian and Bulgarian,rmihaylov/bert-base-pos-theseus-bg,0.0013300571590662,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
23048,mrm8488/deberta-v3-small-finetuned-mrpc,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0813748985528945,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
23049,M-FAC/bert-mini-finetuned-stsb,"For fair comparison against default Adam baseline, we finetune the model in the same framework as described here https://github",M-FAC/bert-mini-finetuned-stsb,1.0,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
23050,PlanTL-GOB-ES/gpt2-large-bne,GPT2-large-bne is a transformer-based model for the Spanish language,PlanTL-GOB-ES/gpt2-large-bne,1.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
23051,nghuyong/ernie-tiny,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0264821778982877,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
23052,google/t5-efficient-small-dm256,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
23053,google/t5-efficient-mini-nl12,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0441678203642367,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
23054,aditeyabaral/sentencetransformer-bert-hinglish-small,"Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B",Milos/slovak-gpt-j-162M,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
23055,rahulacj/bertweet-base-finetuned-sentiment-analysis,This model was trained on TPU and the details are as follows:With the help ofHuggingface-Transformers 2.5,clue/roberta_chinese_3L312_clue_tiny,0.079889103770256,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
23056,allenai/wmt16-en-de-12-1,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
23057,mikaelsouza/msft-regular-model,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
23058,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This model is a rather small instance of GPT-2 trained on TristanBehrens/js-fakes-4bars,TristanBehrens/js-fakes-4bars,0.0637010633945465,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
23059,google/t5-efficient-small-kv16,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0164778940379619,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
23060,khizon/distilbert-unreliable-news-eng-4L,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
23061,flaubert/flaubert_small_cased,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0142431228554674,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
23062,bigjoedata/rockbot,"Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint",KBLab/megatron-bert-large-swedish-cased-165k,0.025576239451766,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
23063,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0653207153081893,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
23064,Recognai/selectra_medium,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.04782488476485,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
23065,google/t5-efficient-base-el16,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0324047235772013,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
23066,allenai/wmt16-en-de-dist-6-1,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.063064731657505,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
23067,microsoft/layoutlm-large-uncased,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
23068,microsoft/layoutlm-large-uncased,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0986369401216507,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
23069,stas/t5-very-small-random,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0086629316210746,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23070,pszemraj/led-large-book-summary,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,1.0,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
23071,Iskaj/hf-challenge-test,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0577605962753295,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
23072,google/t5-efficient-small-kv256,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
23073,ebagdasa/propaganda_positive_bart_large,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0445938619474569,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
23074,microsoft/unispeech-sat-base,Note: This model does not have a tokenizer as it was pretrained on audio alone,microsoft/unispeech-sat-base,1.0,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
23075,google/t5-efficient-base-nh24,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0562648437917232,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
23076,cammy/led-large-16384-arxiv-100-MDS,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0585333555936813,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
23077,malay-huggingface/t5-tiny-bahasa-cased,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0511645376682281,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
23078,DemangeJeremy/4-sentiments-with-flaubert,"It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed",phueb/BabyBERTa-2,0.0118353581055998,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23079,google/t5-efficient-xl-nl6,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0070155002176761,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
23080,mwesner/reformer-clm,T5-Efficient-TINY-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff3000,0.0995526239275932,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
23081,allenai/wmt16-en-de-dist-6-1,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0615077670663594,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
23082,stas/t5-very-small-random,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23083,ErykWdowiak/GPTalian,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0527429595589637,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
23084,google/t5-efficient-base-nh8,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0694769732654094,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
23085,google/t5-efficient-small-ff3000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0305911991745233,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
23086,google/t5-efficient-small-ff9000,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0601669661700725,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
23087,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-LARGE-EL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el2,0.0727105848491191,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23088,google/t5-efficient-small-ff3000,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.059296166524291,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
23089,tau/tavbert-he,This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU,kssteven/ibert-roberta-large,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
23090,ian0delond/model-test,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0623527467250824,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
23091,jweb/japanese-soseki-gpt2-1b,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
23092,google/t5-efficient-base-nl4,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0633517280220985,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
23093,google/t5-efficient-large-nl12,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0202629901468753,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
23094,funnel-transformer/small,Pretrained model on English language using a similar objective objective as ELECTRA,funnel-transformer/small,1.0,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
23095,pere/DeUnCaser,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
23096,google/t5-efficient-small-ff9000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0565577251836657,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
23097,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.098014086484909,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
23098,hfl/chinese-electra-180g-small-generator,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
23099,beston91/gpt2-xl_ft_logits_10k,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0938713699579238,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
23100,jean-paul/kinyaRoberta-small,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0572373928236109,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
23101,google/t5-efficient-tiny-nl24,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0721643771976232,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
23102,ml6team/distilbart-tos-summarizer-tosdr,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0466841286979615,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
23103,mikaelsouza/msft-regular-model,I have made no attempts to censor,bigjoedata/rockbot,0.0195231884717941,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
23104,google/t5-efficient-tiny-nl2,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0860141031444072,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
23105,google/t5-large-ssm-nq,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0728845198949177,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
23106,google/t5-efficient-xl-nl12,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0498999878764152,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
23107,stas/pegasus-cnn_dailymail-tiny-random,Our model performs well in Chinese continuation generation,IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0496387798339128,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
23108,anas-awadalla/bert-tiny-finetuned-squad,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0376790799200534,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
23109,abdusahmbzuai/ft-tatoeba-ar-en,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0401670783758163,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
23110,asi/gpt-fr-cased-base,"Besides, many tasks may be formatted such that the output is directly generated in natural language",asi/gpt-fr-cased-base,1.0,-1.0,NLP,e7478ab0-0948-4c6a-8cf4-406764f74d32___gpt-fr-cased-base,HF
23111,aubmindlab/aragpt2-mega,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
23112,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
23113,akdeniz27/deberta-v2-xlarge-cuad,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0501871779561042,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
23114,diper1998/distilgpt2-finetuned-AT,Boris was trained on around 78B tokens of French text from the C4 dataset,Cedille/fr-boris,0.0965874791145324,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23115,cammy/led-base-16384-100-MDS,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0856314003467559,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
23116,lgrobol/electra-minuscule-generator,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
23117,amazon/bort,This model was finetuned for 3 epochs with a max_len (input) of 768 tokens and target_max_len of 192 tokens,ml6team/mt5-small-german-finetune-mlsum,0.0655506215989589,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23118,google/t5-efficient-base-kv256,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0479205157607793,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
23119,google/t5-efficient-base-ff12000,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.043916156515479,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
23120,google/t5-efficient-base-el8,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
23121,superspray/electra_large_discriminator_squad2_custom_dataset,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0024039521813392,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
23122,google/t5-efficient-large-kv16,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
23123,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl48,0.0773504227399825,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
23124,google/t5-efficient-xl-nl2,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0880773365497589,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
23125,google/t5-efficient-small-nl48,This does not make a very readable text,pere/DeUnCaser,0.0604899860918521,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
23126,tucan9389/kcbert-base-finetuned-squad,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0897745788097381,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
23127,google/t5-efficient-small-kv128,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0651287920773029,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
23128,THUMT/mGPT,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,1.0,-1.0,NLP,337b82e9-8452-490f-be57-2e0059a8854e___mGPT,HF
23129,IDEA-CCNL/Erlangshen-Longformer-110M,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0450597815215587,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
23130,hfl/chinese-legal-electra-base-generator,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0347680797179539,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
23131,google/t5-efficient-small-ff3000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0606328733265399,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
23132,jorge-henao/gpt2-small-spanish-disco-poetry-wt,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
23133,google/t5-efficient-tiny-nl2,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0443179681897163,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
23134,google/t5-efficient-base-nl4,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0326320324093103,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
23135,google/t5-efficient-base-dm512,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0655400827527045,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
23136,google/t5-efficient-small-el8,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0223794104531407,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
23137,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0242533981800079,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
23138,abdusahmbzuai/aradia-ctc-distilhubert-ft,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0998720601201057,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
23139,KBLab/megatron-bert-base-swedish-cased-600k,The model has three sister models trained on the same dataset:,KBLab/megatron-bert-base-swedish-cased-600k,1.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
23140,suzuki/distilbert-base-uncased-finetuned-squad,Amazon's BORT,amazon/bort,0.0878874063491821,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
23141,celine98/canine-s-finetuned-sst2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0810391455888748,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
23142,negfir/distilbert-base-uncased-finetuned-cola,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.056997150182724,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
23143,amazon/bort,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0361425708979367,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23144,SupriyaArun/squeezebert-uncased-finetuned-squad,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0919983088970184,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
23145,microsoft/layoutlm-base-cased,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0787746682763099,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23146,Fujitsu/pytorrent,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
23147,google/t5-efficient-large-nh8-nl32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0101207047700882,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
23148,jorge-henao/gpt2-small-spanish-disco-poetry-wt,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.032476894557476,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
23149,rinna/japanese-gpt-1b,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
23150,piotr-rybak/poleval2021-task4-herbert-large-encoder,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.0911856591701507,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
23151,SetFit/deberta-v3-large__sst2__train-8-8,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0763319656252861,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
23152,diper1998/distilgpt2-finetuned-AT,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0912421196699142,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23153,dhpollack/distilbert-dummy-sentiment,This is a dummy model that can be used for testing the transformers pipeline with the task sentiment-analysis,dhpollack/distilbert-dummy-sentiment,1.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
23154,google/t5-efficient-tiny-nl2,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0536545254290103,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
23155,brad1141/Longformer-finetuned-norm,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0545461624860763,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
23156,groar/gpt-neo-1.3B-finetuned-escape2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0787035077810287,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
23157,google/t5-efficient-tiny-nh1,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
23158,google/t5-efficient-large-dm128,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0637033861130475,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
23159,mgreenbe/bertlet-base-uncased-for-sequence-classification,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0255710128694772,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
23160,google/t5-efficient-small-el12,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0927768237888813,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
23161,google/t5-efficient-small-nl36,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0321477223187685,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
23162,google/t5-efficient-tiny-nl12,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0679890550673007,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
23163,mldmm/GlassBERTa,"BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences",google/bigbird-roberta-large,0.0367820505052804,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
23164,aditeyabaral/sentencetransformer-bert-hinglish-small,This repository provides a 1.3B-parameter finetuned Japanese GPT2 model,jweb/japanese-soseki-gpt2-1b,0.0544427335262298,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
23165,hfl/rbt3,T5-Efficient-SMALL-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff6000,0.060982609167695,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23166,google/t5-efficient-mini,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0554389543831348,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
23167,tuner007/pegasus_qa,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.0881800651550293,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
23168,everdoubling/byt5-Korean-small,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
23169,sentence-transformers/paraphrase-MiniLM-L3-v2,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0221370961517095,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
23170,google/t5-efficient-small-dm128,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
23171,minimaxir/magic-the-gathering,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
23172,google/t5-efficient-small-nl2,T5-Efficient-SMALL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl2,1.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
23173,Milos/slovak-gpt-j-405M,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0480436695118744,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
23174,hfl/rbt3,T5-Efficient-TINY-NH1 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh1,0.0798850953578948,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23175,google/t5-efficient-small-dm1000,This does not make a very readable text,pere/DeUnCaser,0.0390270752832293,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
23176,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-TINY-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff12000,0.0729249082505702,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
23177,google/t5-efficient-tiny-ff6000,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0767573826014995,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
23178,diper1998/distilgpt2-finetuned-AT,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0220368988811969,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23179,rahulacj/bertweet-base-finetuned-sentiment-analysis,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0695418417453765,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
23180,diper1998/distilgpt2-finetuned-AT,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0171943176537752,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23181,google/t5-efficient-small-nl24,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0069843353703617,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
23182,stas/mt5-tiny-random,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0699542313814163,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23183,google/t5-efficient-base-nl4,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0033646849915385,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
23184,lannelin/bert-imdb-1hidden,Source language: English,NYTK/translation-marianmt-en-hu,0.0733388774096965,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
23185,lgrobol/electra-minuscule-discriminator,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0580028071999549,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
23186,google/t5-efficient-small-el8,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0285105518996715,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
23187,google/t5-efficient-tiny-nh16,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0412168223410844,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
23188,google/t5-efficient-small-dm128,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
23189,google/t5-efficient-large-kv128,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
23190,zoha/wav2vec2-base-timit-demo-colab,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.0725938528776168,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
23191,google/t5-efficient-small-el64,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0350498892366885,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
23192,google/t5-efficient-large-dm512,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0672747157514095,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
23193,google/t5-efficient-large-kv256,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
23194,sberbank-ai/rugpt3large_based_on_gpt2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
23195,google/t5-efficient-tiny-ff3000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0691420622169971,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
23196,ToToKr/kobigbird-bert-base-finetuned-klue,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
23197,google/t5-efficient-large-kv32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0447326758876442,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
23198,google/t5-efficient-small-el4,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-small-el4,1.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
23199,hfl/rbt3,T5-Efficient-LARGE-NH12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh12,0.0762141570448875,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23200,google/bert_uncased_L-12_H-256_A-4,The smaller BERT models are intended for environments with restricted computational resources,google/bert_uncased_L-12_H-256_A-4,1.0,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
23201,hfl/rbtl3,T5-Efficient-SMALL-EL16-DL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el16-dl4,0.0569157749414443,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
23202,google/t5-efficient-large-nl12,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
23203,google/t5-efficient-large-kv128,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-kv128,1.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
23204,sentence-transformers/paraphrase-MiniLM-L3-v2,This model is a rather small instance of GPT-2 trained on TristanBehrens/js-fakes-4bars,TristanBehrens/js-fakes-4bars,0.0703696012496948,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
23205,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
23206,google/t5-efficient-small-el8,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0446093175560235,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
23207,M-FAC/bert-mini-finetuned-stsb,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0156799731776118,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
23208,groar/gpt-neo-1.3B-finetuned-escape2,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0910837799310684,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
23209,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0585997812449932,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
23210,hfl/rbt3,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.038535620085895,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23211,google/t5-efficient-xxl-nl4,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0597069263458251,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
23212,hfl/rbtl3,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.0847365781664848,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
23213,google/t5-efficient-xl-nl4,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0070155002176761,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
23214,google/t5-efficient-tiny-el8,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0339640774764121,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
23215,pere/DeUnCaser,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,1.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
23216,google/t5-efficient-base-kv32,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0730064027011394,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
23217,uer/pegasus-base-chinese-cluecorpussmall,"The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0179944345727562,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
23218,stas/t5-very-small-random,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.082119233906269,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23219,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
23220,google/t5-efficient-base-nh32,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0607086103409528,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
23221,aditeyabaral/sentencetransformer-bert-hinglish-small,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0773276984691619,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
23222,google/t5-efficient-base-el16,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
23223,hfl/chinese-xlnet-mid,The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider max_seq_length (4096 vs,yorko/scibert_scivocab_uncased_long_4096,0.0431516828636328,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
23224,hfl/chinese-xlnet-mid,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.014176171272993,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
23225,stas/mt5-tiny-random,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.055143866688013,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23226,pulp/CHILDES-ParentBERTo,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0529904942959546,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
23227,google/t5-efficient-tiny-nh8,This does not make a very readable text,pere/DeUnCaser,0.0574539080262183,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
23228,Parsa/BBB_prediction_classification_SMILES,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0810873160759607,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
23229,monsoon-nlp/dv-muril,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0425484838585058,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
23230,SetFit/deberta-v3-large__sst2__train-8-8,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0393551029264926,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
23231,google/t5-efficient-small-ff9000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0676509700715541,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
23232,google/t5-efficient-base-nh24,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
23233,VietAI/gpt-neo-1.3B-vietnamese-news,"All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0410633869469165,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23234,google/t5-efficient-large-kv32,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0500568486750125,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
23235,google/t5-efficient-base-el16,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0569155123084783,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
23236,DeepPavlov/distilrubert-tiny-cased-conversational-v1,The model achieves 0.58 accuracy on MLM objective and 0.80 on NSP objective,google/fnet-large,0.0306987324729561,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
23237,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0768962055444717,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
23238,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0458097085356712,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
23239,microsoft/layoutlm-large-uncased,It is a two-stage approach consisting of a generator and a scorer,andrejmiscic/simcls-scorer-billsum,0.0665763467550277,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
23240,KoichiYasuoka/roberta-base-ukrainian,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0881534591317176,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
23241,minimaxir/magic-the-gathering,"The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e",cross-encoder/ms-marco-MiniLM-L-4-v2,0.0391264916397631,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
23242,soheeyang/rdr-ctx_encoder-single-trivia-base,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0506879209230343,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
23243,skt/ko-gpt-trinity-1.2B-v0.5,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0150826070457697,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
23244,uer/t5-v1_1-small-chinese-cluecorpussmall,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0257771909236908,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
23245,NovelAI/genji-python-6B,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
23246,rahulacj/bertweet-base-finetuned-sentiment-analysis,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0843276306986808,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
23247,razent/SciFive-base-PMC,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0098984721116721,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
23248,google/t5-efficient-large-nh8,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0319632738828658,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
23249,stas/mt5-tiny-random,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23250,mrm8488/xlm-multi-finetuned-xquadv1,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0637332499027251,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
23251,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.077861800789833,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
23252,google/t5-efficient-large-nl32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.048432215116918,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
23253,lgrobol/electra-minuscule-generator,Vocabulary size is 384,ctoraman/RoBERTa-TR-medium-char,0.0650432519614696,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
23254,Milos/slovak-gpt-j-162M,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0,-1.0,NLP,90d48b56-34ee-4300-b481-a37d3540446b___slovak-gpt-j-162M,HF
23255,rinna/japanese-gpt2-xsmall,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
23256,IDEA-CCNL/Erlangshen-Longformer-330M,This model is case-sensitive: it makes a difference between tirana and Tirana,macedonizer/al-roberta-base,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
23257,google/t5-efficient-small-el64,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0563744343817233,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
23258,google/t5-efficient-tiny-nh1,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
23259,google/t5-efficient-small-nl40,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.062836043536663,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
23260,google/t5-efficient-large-nl32,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
23261,google/t5-efficient-small-ff3000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0394738907925784,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
23262,VietAI/gpt-neo-1.3B-vietnamese-news,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0756385028362273,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23263,negfir/distilbert-base-uncased-finetuned-cola,"Google's mT5mT5 is pretrained on the mC4 corpus, covering 101 languages:",mrm8488/mT5-small-finetuned-tydiqa-for-xqa,0.0903903096914291,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
23264,superb/hubert-base-superb-er,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0272456463426351,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
23265,piotr-rybak/poleval2021-task4-herbert-large-encoder,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
23266,mimicheng/codeparrot-ds,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
23267,Sindhu/muril-large-squad2,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0583375263959169,-1.0,NLP,f6d50b9f-97bb-42f4-9888-58409a4ef7a0___muril-large-squad2,HF
23268,malay-huggingface/xlnet-tiny-bahasa-cased,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
23269,google/t5-efficient-small-el4,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0496135484427213,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
23270,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0947354063391685,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
23271,cammy/led-large-16384-arxiv-100-MDS,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
23272,google/t5-efficient-base-dm2000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0717548076063394,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
23273,stas/pegasus-cnn_dailymail-tiny-random,", quantization-aware training) of the quantized model",kssteven/ibert-roberta-large,0.0778166055679321,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
23274,google/t5-efficient-large-nl32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.004747338593006,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
23275,google/t5-efficient-tiny-nl12,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
23276,DemangeJeremy/4-sentiments-with-flaubert,This model is finetuned for QA task on Squad2 from Muril Large checkpoint,Sindhu/muril-large-squad2,0.0792516730725765,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23277,google/t5-efficient-small-kv32,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
23278,saibo/random-roberta-tiny,Amazon's BORT,amazon/bort,0.0310012497939169,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
23279,mimicheng/codeparrot-ds,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0824509337544441,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
23280,yorko/scibert_scivocab_uncased_long_4096,"512) which is handy in case of working with long texts, e",yorko/scibert_scivocab_uncased_long_4096,1.0,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
23281,cammy/led-large-16384-arxiv-100-MDS,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.070411667227745,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
23282,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
23283,google/t5-efficient-large-dm2000,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0371450651437044,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
23284,rdenadai/BR_BERTo,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0288584431012471,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
23285,google/t5-efficient-large-nl36,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0711374282836913,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
23286,google/t5-efficient-large-dm768,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0367862377315759,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
23287,google/t5-efficient-base-nl32,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0629326216876506,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
23288,google/t5-efficient-xl-nl2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0673010200262069,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
23289,facebook/hubert-xlarge-ll60k,"Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc",facebook/hubert-xlarge-ll60k,1.0,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
23290,SupriyaArun/squeezebert-uncased-finetuned-squad,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.0717497244477272,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
23291,hfl/rbt3,T5-Efficient-BASE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl32,0.0448424331843853,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23292,google/t5-efficient-xl-nl6,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0592279657721519,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
23293,hfl/rbt3,"Pre-Training with Whole Word Masking for Chinese BERTYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping HuThis repository is developed based on：https://github",hfl/rbt3,1.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23294,google/bert_uncased_L-8_H-256_A-4,The smaller BERT models are intended for environments with restricted computational resources,google/bert_uncased_L-8_H-256_A-4,1.0,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
23295,IDEA-CCNL/Erlangshen-Longformer-110M,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0510867238044738,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
23296,stas/mt5-tiny-random,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23297,rinna/japanese-gpt2-xsmall,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
23298,gayanin/bart-mlm-pubmed-medterm,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0601867288351058,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
23299,cammy/led-base-16384-100-MDS,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0642290711402893,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
23300,jonfd/convbert-small-igc-is,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,1.0,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
23301,google/bert_uncased_L-10_H-768_A-12,"This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking)",google/bert_uncased_L-10_H-768_A-12,1.0,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
23302,VietAI/gpt-neo-1.3B-vietnamese-news,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0181592404842377,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23303,mwesner/reformer-clm,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0435501746833324,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
23304,google/bert_uncased_L-10_H-768_A-12,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0280540433401862,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
23305,dhpollack/distilbert-dummy-sentiment,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0170910768210887,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
23306,suzuki/distilbert-base-uncased-finetuned-squad,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
23307,google/t5-efficient-base-dm512,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-dm512,1.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
23308,amazon/bort,Vocabulary size is 384,ctoraman/RoBERTa-TR-medium-char,0.0533314123749733,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23309,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0548111752917368,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
23310,IDEA-CCNL/Erlangshen-Longformer-110M,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0206236876547336,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
23311,google/t5-efficient-large-kv32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0105148702859878,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
23312,allenai/macaw-3b,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.073385568956534,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
23313,hfl/chinese-electra-small-ex-discriminator,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0091894874349236,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
23314,indobenchmark/indobert-base-p1,The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective,indobenchmark/indobert-base-p1,1.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
23315,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0609292667359113,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
23316,mikaelsouza/msft-regular-model,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0585065968334674,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
23317,mikaelsouza/msft-regular-model,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
23318,jpcorb20/pegasus-large-reddit_tifu-samsum-512,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0203361970682938,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
23319,google/t5-efficient-mini,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
23320,zoha/wav2vec2-base-timit-demo-colab,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0700570344924926,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
23321,IDEA-CCNL/Erlangshen-Longformer-110M,This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property,mldmm/GlassBERTa,0.0632715746760368,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
23322,Milos/slovak-gpt-j-405M,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
23323,1one5ome/gpt2-chinese-gulong,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0072078709490597,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
23324,mimicheng/codeparrot-ds,This model is a fine-tuned version of gpt2 on an unknown dataset,mimicheng/codeparrot-ds,1.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
23325,google/t5-efficient-tiny-nl32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0546995401382445,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
23326,google/t5-efficient-mini-nl12,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
23327,DemangeJeremy/4-sentiments-with-flaubert,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.030325410887599,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23328,FremyCompany/xls-r-nl-v1-cv8-lm,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0847825060288111,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
23329,DemangeJeremy/4-sentiments-with-flaubert,When using the model make sure that your speech input is also sampled at 16Khz,superb/hubert-large-superb-sid,0.0635145362466573,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23330,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The training code is available here: SBERT,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0696529746055603,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
23331,google/t5-efficient-small-ff1000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
23332,google/t5-efficient-small-ff3000,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0977990366518497,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
23333,google/t5-efficient-xl-nl2,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0192936742678284,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
23334,tau/tavbert-he,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-xxl-nl4,0.0536144822835922,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
23335,inokufu/flaubert-base-uncased-xnli-sts,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,1.0,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
23336,khizon/distilbert-unreliable-news-eng-4L,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
23337,dbmdz/bert-medium-historic-multilingual-cased,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0400597993284463,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
23338,lgrobol/electra-minuscule-discriminator,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.003763023763895,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
23339,google/t5-efficient-small-nl22,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
23340,google/t5-efficient-tiny-el8,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0442117433995008,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
23341,mwesner/reformer-clm,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0487122982740402,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
23342,indobenchmark/indobert-base-p1,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
23343,cammy/led-large-16384-arxiv-100-MDS,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0522197075188159,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
23344,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,DistilWav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the XLS-R architecture,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,1.0,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
23345,hfl/rbt3,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23346,microsoft/layoutlm-base-cased,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.0620152428746223,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23347,microsoft/layoutlm-base-cased,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0167877469211816,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23348,amazon/bort,The learned pattern probabilities can be used for de novo SMILES generation,mrm8488/chEMBL_smiles_v1,0.0423009293153881,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23349,negfir/distilbert-base-uncased-finetuned-squad,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0839645788073539,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
23350,monsoon-nlp/dv-muril,"This is an experiment in transfer learning, to insert Dhivehi word and word-piece tokens into Google's MuRIL model",monsoon-nlp/dv-muril,1.0,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
23351,google/t5-efficient-large-kv128,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.006296606734395,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
23352,1one5ome/gpt2-chinese-gulong,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0914993062615394,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
23353,rdenadai/BR_BERTo,This molecular design concept eliminates the need for virtual compound library enumeration and enables virtual compound design without requiring secondary or external activity prediction,mrm8488/chEMBL_smiles_v1,0.0359488998850186,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
23354,google/t5-efficient-small-dm2000,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0871020555496215,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
23355,google/t5-efficient-tiny-ff12000,Its original repository can be found here,tau/splinter-large-qass,0.0325918775051831,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
23356,microsoft/layoutlm-large-uncased,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 240",eli4s/Bert-L12-h240-A12,0.0911180227994918,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
23357,stas/mt5-tiny-random,"Reader-Distilled Retriever (RDR)Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit",soheeyang/rdr-ctx_encoder-single-trivia-base,0.0873135551810264,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23358,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0376915261149406,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
23359,google/t5-efficient-large-nl20,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0344187170267105,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
23360,hfl/rbt3,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0764307342469692,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23361,mimicheng/codeparrot-ds,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0012809783220291,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
23362,akdeniz27/deberta-v2-xlarge-cuad,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
23363,google/bert_uncased_L-8_H-128_A-2,Its original repository can be found here,tau/splinter-large-qass,0.0176675698409477,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
23364,nyu-mll/roberta-base-10M-3,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0449911169707774,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
23365,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
23366,1one5ome/gpt2-chinese-gulong,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0407178550958633,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
23367,Cedille/fr-boris,As a consequence the model still has good performance in English language,Cedille/fr-boris,1.0,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
23368,microsoft/deberta-v2-xxlarge-mnli,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
23369,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.0050887502729892,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23370,GKLMIP/electra-myanmar-small-uncased,"At small scale, ELECTRA achieves strong results even when trained on a single GPU",skplanet/dialog-koelectra-small-generator,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
23371,google/t5-efficient-small-dm768,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0308562815189361,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
23372,google/t5-efficient-tiny-ff12000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0828743502497673,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
23373,TJMUCH/transcriptome-iseeek,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0981551706790924,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23374,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0647457838058471,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
23375,google/t5-efficient-small-el32,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0703895259648561,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
23376,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.0058288909494876,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23377,minimaxir/magic-the-gathering,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.0189067758619785,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
23378,khizon/distilbert-unreliable-news-eng-4L,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0216014431789517,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
23379,google/t5-efficient-base-el16,Its original repository can be found here,tau/splinter-large-qass,0.0602474641054868,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
23380,microsoft/layoutlm-base-cased,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0476888976991176,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23381,google/t5-efficient-base-nh32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0631077606230973,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
23382,google/t5-efficient-small-dm768,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0618721954524516,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
23383,flboehm/reddit-bert-text_5,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0989105030894279,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
23384,google/t5-efficient-tiny-nl12,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0607108045369386,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
23385,negfir/distilbert-base-uncased-finetuned-cola,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.0601767934858799,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
23386,google/t5-efficient-large-dm512,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
23387,diper1998/distilgpt2-finetuned-AT,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0933703854680061,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23388,mrm8488/xlm-multi-finetuned-xquadv1,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0336193442344665,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
23389,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0915831476449966,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
23390,cammy/led-large-16384-arxiv-100-MDS,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0571059621870517,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
23391,facebook/blenderbot-90M,"Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0086454744450747,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
23392,stas/mt5-tiny-random,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23393,jkeruotis/LitBERTa-uncased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0140311010181903,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
23394,google/t5-efficient-small-el16-dl4,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
23395,superspray/electra_large_discriminator_squad2_custom_dataset,"If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:",nherve/flaubert-oral-ft,0.0321129113435745,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
23396,google/t5-efficient-large-dm256,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.039624030701816,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
23397,thu-coai/LongLM-large,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.0201604201768835,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
23398,allenai/wmt16-en-de-dist-6-1,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0697445645928382,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
23399,mikaelsouza/msft-regular-model,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0587563961744308,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
23400,google/t5-efficient-mini-nl6,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0746749937534332,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
23401,zoha/wav2vec2-base-timit-demo-colab,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
23402,castorini/afriberta_large,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0359901397023349,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
23403,nghuyong/ernie-tiny,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0455574402585625,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
23404,tau/tavbert-he,T5-Efficient-XL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl2,0.0477568097412585,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
23405,TJMUCH/transcriptome-iseeek,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23406,hfl/rbt3,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.0847365781664848,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23407,google/t5-efficient-small-nl24,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
23408,mimi/Waynehills-NLP-doogie,"The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden",KBLab/megatron-bert-large-swedish-cased-165k,0.0984827205538749,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
23409,DeepPavlov/distilrubert-tiny-cased-conversational-v1,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
23410,superspray/electra_large_discriminator_squad2_custom_dataset,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0655486434698104,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
23411,Parsa/BBB_prediction_classification_SMILES,For best results use python as below,pszemraj/led-large-book-summary,0.0187687339882055,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
23412,diper1998/distilgpt2-finetuned-AT,We collect 120G novels as the pretraining data for LongLM,thu-coai/LongLM-large,0.0804429799318313,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23413,mmaguero/gn-bert-tiny-cased,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0627853628247976,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
23414,diper1998/distilgpt2-finetuned-AT,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23415,google/t5-efficient-tiny-nh32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0732407383620738,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
23416,tau/splinter-large-qass,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0031432532187965,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
23417,minimaxir/magic-the-gathering,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
23418,hajime9652/xlnet-japanese,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.018017828464508,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
23419,anirudh21/xlnet-base-cased-finetuned-rte,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0763489007949829,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
23420,mrm8488/xlm-multi-finetuned-xquadv1,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
23421,google/t5-efficient-xl-nl16,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0379374884068965,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
23422,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-SMALL-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv128,0.0582009218633174,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
23423,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0578135624527931,-1.0,NLP,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
23424,clue/xlnet_chinese_large,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0144206788390874,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
23425,google/t5-efficient-base-ff6000,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0462497025728225,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
23426,hfl/chinese-legal-electra-base-generator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
23427,tartuNLP/gpt-4-est-large,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.090425968170166,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
23428,google/t5-efficient-small-nl2,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0225691497325897,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
23429,google/t5-efficient-small-ff1000,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0372919864021241,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
23430,nghuyong/ernie-1.0,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0564918098971247,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
23431,VietAI/gpt-neo-1.3B-vietnamese-news,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23432,DeepPavlov/distilrubert-tiny-cased-conversational,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0540972550710041,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
23433,kssteven/ibert-roberta-base,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
23434,amazon/bort,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23435,akdeniz27/deberta-v2-xlarge-cuad,"zip, use 180G of data to continue trainingThere is no structure of Longformer-base in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-110M,0.0491227842867374,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
23436,abdusahmbzuai/ft-tatoeba-ar-en,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0921110212802887,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
23437,google/t5-efficient-large-nl32,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-large-nl32,1.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
23438,rinna/japanese-gpt2-xsmall,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0645731476445992,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
23439,filco306/gpt2-switchboard-paraphraser,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0272554538678378,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
23440,google/t5-efficient-small-nl36,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0709516070783138,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
23441,google/t5-efficient-mini-nl24,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0640956088900565,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
23442,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0410429928451775,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
23443,malay-huggingface/t5-super-tiny-bahasa-cased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
23444,google/t5-efficient-base-nl48,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0232100039720535,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
23445,google/t5-efficient-small-kv128,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
23446,cammy/led-base-16384-100-MDS,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
23447,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M,Milos/slovak-gpt-j-405M,0.0307087264955043,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
23448,google/t5-efficient-large-kv32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0378233906812965,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
23449,anirudh21/xlnet-base-cased-finetuned-rte,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0738198012113571,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
23450,diper1998/distilgpt2-finetuned-AT,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23451,minimaxir/magic-the-gathering,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0482262223958968,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
23452,stas/pegasus-cnn_dailymail-tiny-random,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0606600884348153,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
23453,rdenadai/BR_BERTo,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0159486619134743,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
23454,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0421483106911182,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
23455,akdeniz27/deberta-v2-xlarge-cuad,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0747479051351547,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
23456,superspray/electra_large_discriminator_squad2_custom_dataset,This project was funded by the Language Technology Programme for Icelandic 2019-2023,jonfd/convbert-small-igc-is,0.0775782018899917,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
23457,google/t5-efficient-large-el2,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0282688569277525,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
23458,stas/pegasus-cnn_dailymail-tiny-random,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0137454494833946,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
23459,IDEA-CCNL/Erlangshen-Longformer-330M,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0394011633470654,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
23460,jkeruotis/LitBERTa-uncased,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0403251666575669,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
23461,google/t5-efficient-mini-nl8,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
23462,lannelin/bert-imdb-1hidden,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.06128125389417,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
23463,minimaxir/magic-the-gathering,"You can fine-tune roberta-small-belarusian for downstream tasks, such as POS-tagging, dependency-parsing, and so on",KoichiYasuoka/roberta-small-belarusian,0.082525834441185,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
23464,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
23465,amazon/bort,", MatMul, GELU, Softmax, and LayerNorm) with closely approximating integer operations",kssteven/ibert-roberta-large,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23466,DemangeJeremy/4-sentiments-with-flaubert,The three provided models are randomly selected from 10 that were trained and reported in the paper,phueb/BabyBERTa-2,0.0860528349876403,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23467,microsoft/layoutlm-base-cased,"MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks",mrm8488/mobilebert-uncased-finetuned-squadv2,0.0287247318774461,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23468,google/t5-efficient-base-nh32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
23469,microsoft/layoutlm-base-cased,You can use BelGPT-2 with 🤗 transformers:Below is the list of all French copora used to pre-trained the model:,antoiloui/belgpt2,0.0468668937683105,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23470,google/t5-efficient-small-el4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0306699629873036,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
23471,ToToKr/kobigbird-bert-base-finetuned-klue,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0657592490315437,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
23472,rdenadai/BR_BERTo,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0237718668455879,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
23473,nghuyong/ernie-tiny,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0152527044216791,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
23474,google/t5-efficient-tiny-nh16,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0591464340686797,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
23475,mrm8488/deberta-v3-small-finetuned-mrpc,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0621411502361297,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
23476,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Besides, many tasks may be formatted such that the output is directly generated in natural language",asi/gpt-fr-cased-base,0.0950738489627838,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
23477,lassl/gpt2-ko-small,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.042361291591078,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
23478,razent/SciFive-large-Pubmed,It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight,saibo/random-roberta-tiny,0.0292650712653994,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
23479,rinna/japanese-gpt-1b,"It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 ""words"" after splitting on whitespace, which resulted in 80249 articles",ml6team/mt5-small-german-finetune-mlsum,0.0245443812261025,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
23480,Norod78/hebrew_poetry-gpt_neo-tiny,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0562296696007251,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
23481,google/t5-efficient-small-ff3000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0109854219481348,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
23482,hfl/rbtl3,This is a Lonformer version of the SciBERT uncased model by Allen AI,yorko/scibert_scivocab_uncased_long_4096,0.0711625404655933,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
23483,facebook/wmt21-dense-24-wide-en-x,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
23484,beston91/gpt2-xl_ft_logits_10k,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0788191109895706,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
23485,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl24,0.0143196620047092,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23486,google/t5-efficient-small-el4,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0586327388882636,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
23487,google/t5-efficient-base-ff6000,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0431228820234537,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
23488,google/t5-efficient-base-nh24,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0667124949395656,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
23489,hf-internal-testing/tiny-random-rembert,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0280009936541318,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
23490,google/t5-efficient-small-nl20,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0543262204155325,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
23491,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0677470266819,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
23492,DTAI-KULeuven/robbertje-1-gb-bort,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0527082892755666,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
23493,microsoft/layoutlm-base-uncased,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0787746682763099,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
23494,google/t5-efficient-mini-nl8,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0137143712490797,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
23495,mrm8488/longformer-base-4096-spanish,Its original repository can be found here,tau/splinter-large-qass,0.0061334896832704,-1.0,NLP,47fa5077-4d92-4913-a31a-b5f621200d4b___longformer-base-4096-spanish,HF
23496,google/t5-efficient-tiny-nl16,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0598068516701459,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
23497,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0197659209370613,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
23498,google/t5-efficient-base-nh24,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
23499,google/t5-efficient-xl-nl16,"You can download the 24 Chinese RoBERTa miniatures either from the UER-py Modelzoo page, or via HuggingFace from the links below:",uer/chinese_roberta_L-6_H-128,0.0632010251283645,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
23500,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0673956051468849,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
23501,google/t5-efficient-small-dm128,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0017468435689807,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
23502,google/t5-efficient-small-el8,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0815833285450935,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
23503,mikaelsouza/msft-regular-model,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0880759730935096,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
23504,google/t5-efficient-small-kv128,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0060133785009384,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
23505,google/t5-efficient-base-ff9000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0464079156517982,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
23506,clue/roberta_chinese_3L312_clue_tiny,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0055930316448211,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
23507,google/t5-efficient-base-dm2000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
23508,TJMUCH/transcriptome-iseeek,The model is a DPR retriever further finetuned using knowledge distillation from the DPR reader,soheeyang/rdr-ctx_encoder-single-trivia-base,0.090554341673851,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23509,mwesner/reformer-clm,T5-Efficient-SMALL-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff3000,0.0947061404585838,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
23510,facebook/wmt21-dense-24-wide-en-x,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0287856990471482,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
23511,google/t5-efficient-xl-nl6,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0958713442087173,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
23512,dhpollack/distilbert-dummy-sentiment,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0028684213757515,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
23513,DeepPavlov/distilrubert-tiny-cased-conversational,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.044306855648756,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
23514,Iskaj/hf-challenge-test,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0675465911626815,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
23515,DemangeJeremy/4-sentiments-with-flaubert,The model architecture is custom for production so it only contains 70M parameters,nguyenvulebinh/envibert,0.0415387637913226,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23516,TJMUCH/transcriptome-iseeek,Mean f1 ROUGE scores were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table,ml6team/mt5-small-german-finetune-mlsum,0.0948179885745048,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23517,SupriyaArun/squeezebert-uncased-finetuned-squad,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0885349065065383,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
23518,google/t5-efficient-base-ff1000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0536699648946523,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
23519,M-FAC/bert-mini-finetuned-stsb,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.024762547109276,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
23520,stas/t5-very-small-random,"Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking",xlnet-large-cased,0.060064397752285,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23521,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0820255577564239,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
23522,fnlp/bart-large-chinese,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0777371954172849,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
23523,krevas/finance-koelectra-base-generator,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,1.0,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
23524,uer/t5-v1_1-base-chinese-cluecorpussmall,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.067205449566245,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
23525,Milos/slovak-gpt-j-405M,Splinter-base is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021),tau/splinter-base-qass,0.0554059309264023,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
23526,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh32,0.0590034797787666,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
23527,stas/t5-very-small-random,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23528,nherve/flaubert-oral-asr,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.058380785708626,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
23529,aubmindlab/aragpt2-mega,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0270694578066468,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
23530,lgris/distilxlsr_bp_8-12,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0134242769951621,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
23531,google/t5-efficient-base-kv32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0219018720090389,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
23532,Milos/slovak-gpt-j-405M,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0677457166214783,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
23533,google/t5-efficient-xl-nl6,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
23534,google/t5-efficient-base-ff12000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0593177862465381,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
23535,brad1141/Longformer-finetuned-norm,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0656236857175827,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
23536,google/t5-efficient-large-nh8,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
23537,abdusahmbzuai/aradia-ctc-distilhubert-ft,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0668517649173736,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
23538,google/t5-efficient-base-ff12000,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
23539,ductuan024/AimeLaw,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0289932610467075,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
23540,brad1141/Longformer-finetuned-norm,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0645175874233245,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
23541,google/t5-efficient-tiny-nl6,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0181359574198722,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
23542,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a German Europeana ConvBERT model 🎉We use the open source Europeana newspapers that were provided by The European Library,dbmdz/convbert-base-german-europeana-cased,0.0416642166674137,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
23543,google/t5-efficient-small-dm256,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
23544,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
23545,negfir/distilbert-base-uncased-finetuned-cola,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
23546,groar/gpt-neo-1.3B-finetuned-escape2,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
23547,google/t5-efficient-large-nh4,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0873960331082343,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
23548,abdusahmbzuai/aradia-ctc-distilhubert-ft,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0791825205087661,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
23549,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased ELECTRA base model for Turkish 🎉We release a base ELECTRA model for Turkish, that was trained on the same data as BERTurk",dbmdz/electra-base-turkish-cased-discriminator,0.0837906971573829,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
23550,abdusahmbzuai/ft-tatoeba-ar-en,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0919374823570251,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
23551,groar/gpt-neo-1.3B-finetuned-escape2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.079643428325653,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
23552,aditeyabaral/sentencetransformer-bert-hinglish-small,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
23553,google/t5-efficient-base-kv256,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0284702796489,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
23554,lassl/roberta-ko-small,"If you want to get information for training, you should see config",lassl/roberta-ko-small,1.0,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
23555,cammy/led-base-16384-100-MDS,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0466140396893024,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
23556,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0635901093482971,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
23557,google/t5-efficient-base-kv16,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
23558,kssteven/ibert-roberta-large,", to TensorRT) for integer-only deployment of the model",kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
23559,google/t5-efficient-base-dm1000,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0922259017825126,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
23560,google/t5-efficient-base-nh8,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
23561,google/t5-efficient-base-nl40,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0446080211549997,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
23562,google/reformer-crime-and-punishment,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0492094866931438,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
23563,razent/SciFive-base-PMC,For best results use python as below,pszemraj/led-large-book-summary,0.018614170141518,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
23564,stas/t5-very-small-random,Generated from the official Squad2 evaluation scriptMuRIL is specifically trained to work on 18 Indic languages and English,Sindhu/muril-large-squad2,0.0423405915498733,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23565,google/byt5-xl,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
23566,1one5ome/gpt2-chinese-gulong,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0654024928808212,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
23567,google/fnet-large,Disclaimer: This model card has been written by gchhablani,google/fnet-large,1.0,-1.0,NLP,be8c3b54-acb9-41d3-9190-d3a35ba6f7a6___fnet-large,HF
23568,google/t5-efficient-base-kv16,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0090715317055583,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
23569,google/t5-efficient-large-el8,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0572234280407428,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
23570,cammy/led-base-16384-100-MDS,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0985836908221244,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
23571,SupriyaArun/squeezebert-uncased-finetuned-squad,This model is a fine-tuned version of squeezebert/squeezebert-uncased on the squad dataset,SupriyaArun/squeezebert-uncased-finetuned-squad,1.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
23572,google/t5-efficient-xl-nl2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0364839285612106,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
23573,sonoisa/byt5-small-japanese,This is a ByT5 (a tokenizer-free extension of the Text-to-Text Transfer Transformer) model pretrained on Japanese corpus,sonoisa/byt5-small-japanese,1.0,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
23574,google/t5-efficient-base-ff12000,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-ff12000,1.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
23575,mwesner/reformer-clm,T5-Efficient-BASE-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff1000,0.0789003074169159,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
23576,IDEA-CCNL/Erlangshen-Longformer-110M,The exact expression to filter the dataset was the following:The fine-tuned model was evaluated on 2000 random articles from the validation set,ml6team/mt5-small-german-finetune-mlsum,0.0421640584245324,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
23577,cammy/led-large-16384-arxiv-100-MDS,"For the ﬁrst task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input",thu-coai/LongLM-large,0.0733025521039962,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
23578,beston91/gpt2-xl_ft_logits_10k,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0786742344498634,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
23579,fnlp/bart-large-chinese,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0236984770745038,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
23580,google/t5-efficient-large-nl16,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0504387086257338,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
23581,VietAI/gpt-neo-1.3B-vietnamese-news,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0514594614505767,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23582,google/t5-efficient-tiny-nl24,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0076486459001898,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
23583,lannelin/bert-imdb-1hidden,This project was funded by the Language Technology Programme for Icelandic 2019-2023,jonfd/convbert-small-igc-is,0.0668755111594994,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
23584,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0738490670919418,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
23585,microsoft/layoutlm-base-uncased,Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team,funnel-transformer/small,0.0933935195207595,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
23586,google/t5-efficient-tiny-ff12000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0624514333903788,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
23587,google/t5-efficient-base-el6,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
23588,SajjadAyoubi/distil-bigbird-fa-zwnj,"The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts",SajjadAyoubi/distil-bigbird-fa-zwnj,1.0,-1.0,NLP,93bf4f14-6c7a-41bc-854c-061751656605___distil-bigbird-fa-zwnj,HF
23589,google/t5-efficient-base-nl48,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0179838803596794,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
23590,VietAI/gpt-neo-1.3B-vietnamese-news,The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API,aubmindlab/aragpt2-mega,0.0972941629588603,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23591,google/t5-efficient-large-dm512,Its original repository can be found here,tau/splinter-large-qass,0.0494936350733041,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
23592,DeepPavlov/distilrubert-tiny-cased-conversational-v1,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0632762620225548,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
23593,google/t5-efficient-base-nh24,"In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count",google/t5-efficient-base-nh24,1.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
23594,google/t5-efficient-small-ff12000,"To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method",facebook/wmt21-dense-24-wide-en-x,0.0794873796403408,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
23595,NYTK/translation-marianmt-en-hu,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,1.0,-1.0,NLP,04ab8b45-5473-4cfe-a2de-c945b14ed956___translation-marianmt-en-hu,HF
23596,IDEA-CCNL/Zhouwenwang-Unified-1.3B,"It will continue to be optimized in the direction of model scale, knowledge integration, and supervision task assistance",IDEA-CCNL/Zhouwenwang-Unified-1.3B,1.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
23597,patrickvonplaten/unispeech-sat-large-timit-ft,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
23598,google/t5-efficient-tiny-nl6,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0194608131423592,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
23599,j-hartmann/purchase-intention-english-roberta-large,"There is no structure of Zhouwenwang-Unified-110M in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-110M from Fengshenbang-LMYou can use Zhouwenwang-110M to continue writing",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
23600,mgreenbe/bertlet-base-uncased-for-sequence-classification,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0771703869104385,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
23601,SetFit/deberta-v3-large__sst2__train-8-8,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0939222052693367,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
23602,hfl/chinese-electra-180g-small-generator,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
23603,1one5ome/gpt2-chinese-gulong,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
23604,rdenadai/BR_BERTo,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0610200663407643,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
23605,google/t5-efficient-xl-nl4,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0110475458204746,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
23606,google/t5-efficient-tiny-ff12000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0267409272491931,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
23607,google/t5-efficient-tiny-nl32,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0357624143362045,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
23608,diper1998/distilgpt2-finetuned-AT,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0141580458730459,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23609,negfir/distilbert-base-uncased-finetuned-squad,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.062561959028244,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
23610,clue/roberta_chinese_3L312_clue_tiny,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.0138545869849621,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
23611,google/t5-efficient-base-nl40,"A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al",tau/tavbert-he,0.0437853615731,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
23612,google/t5-efficient-base-kv32,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.056026067584753,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
23613,google/t5-efficient-base-nl4,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0394453201442955,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
23614,google/t5-efficient-xl-nl28,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0856130123138427,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
23615,mrm8488/xlm-multi-finetuned-xquadv1,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0768268778920173,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
23616,google/t5-efficient-small-kv32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0607241727411746,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
23617,aditeyabaral/sentencetransformer-bert-hinglish-small,Both pytorch(pytorch_model bin) and Rust(rust_modelot) models are providedNOTE: Use T5Tokenizer to initiate the tokenizer,jweb/japanese-soseki-gpt2-1b,0.064758576452732,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
23618,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context,sberbank-ai/rugpt3large_based_on_gpt2,0.0882949829101562,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
23619,google/t5-efficient-base-nh24,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0171675607562065,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
23620,negfir/distilbert-base-uncased-finetuned-squad,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
23621,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This is a sentence-transformers model: It maps sentences and paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,1.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
23622,hajime9652/xlnet-japanese,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
23623,khizon/distilbert-unreliable-news-eng-4L,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
23624,google/t5-efficient-base-el16,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0440619383007287,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
23625,google/t5-efficient-large-kv128,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0501161888241767,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
23626,tau/tavbert-he,This repository provides a 1.3B-parameter finetuned Japanese GPT2 model,jweb/japanese-soseki-gpt2-1b,0.0290998881682753,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
23627,uer/pegasus-base-chinese-cluecorpussmall,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0240874830633401,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
23628,mwesner/reformer-clm,T5-Efficient-XL-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl8,0.0829398334026336,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
23629,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
23630,Recognai/selectra_medium,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0509693184867501,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
23631,TJMUCH/transcriptome-iseeek,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0858679562807083,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23632,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation",hyunwoongko/reddit-9B,0.0592083372175693,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
23633,google/t5-efficient-xl-nl8,"com/sarnikowski/danish_transformers If you have any questions feel free to open an issue on the danish_transformers repository, or send an email to p",sarnikowski/convbert-medium-small-da-cased,0.0428845882415771,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
23634,tau/tavbert-he,T5-Efficient-LARGE-NH2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh2,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
23635,google/t5-efficient-small-dm768,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0840229541063308,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
23636,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0928415358066558,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
23637,fnlp/cpt-base,"To use CPT, please import the file modeling_cpt",fnlp/cpt-base,1.0,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
23638,google/t5-efficient-base-nh8,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.031250236555934,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
23639,stas/mt5-tiny-random,"It has been trained in ""multi-angle"" fashion,  which means it can handle a flexible set of input and output ""slots""  (question, answer, multiple-choice options, context, and explanation)",allenai/macaw-answer-11b,0.0531354695558548,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23640,diper1998/distilgpt2-finetuned-AT,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23641,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0639312639832496,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
23642,google/t5-efficient-large-nl32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
23643,google/t5-efficient-base-nh32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0596174672245978,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
23644,obss/mt5-base-3task-highlight-tquad2,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
23645,google/t5-efficient-large-nl20,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
23646,ctoraman/RoBERTa-TR-medium-char,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-char,1.0,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
23647,ml6team/distilbart-tos-summarizer-tosdr,"Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64)",eli4s/Bert-L12-h240-A12,0.063609318807721,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
23648,bigjoedata/rockbot,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
23649,Norod78/hebrew_poetry-gpt_neo-tiny,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0686440058052539,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
23650,jcblaise/distilbert-tagalog-base-cased,Use jcblaise/roberta-tagalog-base or jcblaise/roberta-tagalog-large instead for better performance,jcblaise/distilbert-tagalog-base-cased,1.0,-1.0,NLP,29e88f0d-47b4-4733-8b59-3cbe0f4bb77e___distilbert-tagalog-base-cased,HF
23651,diper1998/distilgpt2-finetuned-AT,"To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents",PlanTL-GOB-ES/gpt2-large-bne,0.0682703703641891,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23652,TJMUCH/transcriptome-iseeek,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23653,nherve/flaubert-oral-ft,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
23654,junnyu/roformer_base_wwm_cluecorpussmall,GPT2-base and medium uses the code from the gpt2 folder and can trains models from the minimaxir/gpt-2-simple repository,aubmindlab/aragpt2-mega,0.0781942730148633,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
23655,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0625551541646321,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
23656,mrm8488/bert-small-finetuned-squadv2,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.060241313651204,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
23657,google/t5-efficient-xl-nl6,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0488917604088783,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
23658,tucan9389/kcbert-base-finetuned-squad,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0230705365538597,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
23659,google/t5-efficient-tiny-el8,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0448808893561363,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
23660,google/t5-efficient-tiny-el8,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0092930952087045,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
23661,hajime9652/xlnet-japanese,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0442254096269607,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
23662,hfl/chinese-electra-180g-small-generator,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
23663,macedonizer/al-roberta-base,"More precisely, it was pre-trained with the Masked language modeling (MLM) objective",macedonizer/al-roberta-base,1.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
23664,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,We modify the original position code of longformer to rotational position coding，and on the basis of chinese_roformer_L-12_H-768_A-12,IDEA-CCNL/Erlangshen-Longformer-110M,0.0651978850364685,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
23665,superspray/electra_large_discriminator_squad2_custom_dataset,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0687930136919021,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
23666,google/t5-efficient-large-nh12,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0320061594247817,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
23667,google/t5-efficient-small-dm1000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0606780033558606,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
23668,google/t5-efficient-large-el4,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
23669,rahulacj/bertweet-base-finetuned-sentiment-analysis,This model is a fine-tuned version of cardiffnlp/bertweet-base-sentiment on an unknown dataset,rahulacj/bertweet-base-finetuned-sentiment-analysis,1.0,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
23670,razent/SciFive-large-Pubmed,This model is block sparse: the linear layers contains 12.5% of the original weights,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0810306519269942,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
23671,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0837237387895584,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
23672,microsoft/layoutlm-large-uncased,Fine-tuned facebook/wav2vec2-xls-r-1b on English using the Common Voice 8,jonatasgrosman/wav2vec2-xls-r-1b-english,0.0798484236001968,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
23673,rdenadai/BR_BERTo,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,1.0,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
23674,microsoft/layoutlm-base-cased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0495082549750804,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23675,DeepPavlov/distilrubert-tiny-cased-conversational-v1,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0521311424672603,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
23676,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl4,0.0968660563230514,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
23677,anirudh21/xlnet-base-cased-finetuned-rte,"In our work, we have trained the BigBird model for the Persian language to process texts up to 4096 in the Farsi (Persian) language using sparse attention",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0671476423740387,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
23678,hfl/rbt3,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0893627107143401,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23679,google/t5-efficient-base-dm2000,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0660307500511407,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
23680,hfl/chinese-electra-small-ex-generator,"For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA",hfl/chinese-electra-small-ex-generator,1.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
23681,google/t5-efficient-base-nh24,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0816614590585231,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
23682,zoha/wav2vec2-base-timit-demo-colab,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0579295307397842,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
23683,google/t5-efficient-large-nl32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0314828995615244,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
23684,microsoft/layoutlm-large-uncased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0495082549750804,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
23685,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl36,0.0766000524163246,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
23686,malay-huggingface/bert-tiny-bahasa-cased,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
23687,DemangeJeremy/4-sentiments-with-flaubert,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0383983328938484,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23688,Palak/microsoft_deberta-base_squad,"There is no structure of Longformer-large in Transformers, you can run follow code to get structure of longformer from Fengshenbang-LMIf you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Erlangshen-Longformer-330M,0.0720912143588066,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
23689,hakurei/gpt-j-random-tinier,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0109025407582521,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
23690,TJMUCH/transcriptome-iseeek,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0957521870732307,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23691,mwesner/reformer-clm,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0682672262191772,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
23692,hfl/rbt3,T5-Efficient-LARGE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh32,0.0681560523808002,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23693,google/t5-efficient-tiny-nh1,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0251156305894255,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
23694,Norod78/hebrew_poetry-gpt_neo-tiny,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0753248073160648,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
23695,google/t5-efficient-small-el12,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0553368236869573,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
23696,microsoft/layoutlm-base-cased,This is a Lonformer version of the SciBERT uncased model by Allen AI,yorko/scibert_scivocab_uncased_long_4096,0.0540755316615104,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23697,superspray/electra_large_discriminator_squad2_custom_dataset,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0641623958945274,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
23698,IDEA-CCNL/Erlangshen-Longformer-330M,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.014555387198925,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
23699,google/t5-efficient-large-nl12,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0759166628122329,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
23700,akdeniz27/deberta-v2-xlarge-cuad,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
23701,google/t5-efficient-small-nl40,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0604173969477414,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
23702,superspray/electra_large_discriminator_squad2_custom_dataset,"Please check out our EMNLP 2021 paper ""BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation"" for more details",jhu-clsp/bibert-ende,0.0727991908788681,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
23703,google/t5-efficient-small-el64,Its original repository can be found here,tau/splinter-large-qass,0.0696992818266152,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
23704,google/t5-efficient-large-nl12,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0180841758847236,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
23705,stas/mt5-tiny-random,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0256653726100921,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23706,brad1141/Longformer-finetuned-norm,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.083549290895462,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
23707,rmihaylov/pegasus-base-cnn-dailymail-bg,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
23708,j-hartmann/purchase-intention-english-roberta-large,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0701842978596687,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
23709,google/t5-efficient-tiny-ff12000,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.0,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
23710,hfl/rbtl3,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0167522262781858,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
23711,google/t5-efficient-base-kv32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0504743810743092,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
23712,izumi-lab/bert-small-japanese-fin,Its original repository can be found here,tau/splinter-large-qass,0.0619481826821962,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
23713,google/t5-efficient-mini-nl8,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.0321037704125047,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
23714,mldmm/GlassBERTa,Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team,google/bigbird-roberta-large,0.0513350095134228,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
23715,hfl/rbt3,"This is part of a project from which resulted the following models:For more information, please see the accompanying article (See BibTeX entry and citation info below) and the project's github",liaad/srl-pt_xlmr-base,0.010669270530343,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23716,google/t5-efficient-large-nl20,"IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti",indobenchmark/indobert-base-p1,0.009100092574954,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
23717,malay-huggingface/t5-super-super-tiny-bahasa-cased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0144426450133323,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
23718,google/t5-efficient-small-nl40,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.070940650999546,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
23719,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh32,0.087400808930397,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
23720,allenai/wmt19-de-en-6-6-big,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
23721,junnyu/roformer_base_wwm_cluecorpussmall,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0593754174187779,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
23722,tartuNLP/gpt-4-est-large,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0041218493133783,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
23723,hfl/rbt3,T5-Efficient-SMALL-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl20,0.071111612021923,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23724,microsoft/deberta-v2-xxlarge-mnli,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0292785491328686,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
23725,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet",everdoubling/byt5-Korean-small,0.055113099515438,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
23726,google/t5-efficient-large-kv16,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0310028968378901,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
23727,razent/SciFive-base-PMC,The model architecture is custom for production so it only contains 70M parameters,nguyenvulebinh/envibert,0.0593679137527942,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
23728,stas/t5-very-small-random,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23729,soheeyang/rdr-ctx_encoder-single-trivia-base,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0242964066565036,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
23730,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0297254230827093,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
23731,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
23732,stas/pegasus-cnn_dailymail-tiny-random,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0624397099018096,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
23733,google/t5-efficient-small-el64,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
23734,stas/t5-very-small-random,ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-1.4B,0.0635278448462486,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23735,skt/ko-gpt-trinity-1.2B-v0.5,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0469500049948692,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
23736,stas/t5-very-small-random,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0448909327387809,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23737,amazon/bort,This makes the model more efficient since the output embeddings are discarded during fine-tuning,google/rembert,0.0288540748879313,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23738,google/t5-efficient-small-dm1000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0623856279999017,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
23739,yarongef/DistilProtBert,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.068440111974875,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
23740,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0666752308607101,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
23741,skt/ko-gpt-trinity-1.2B-v0.5,A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string,Parsa/BBB_prediction_classification_SMILES,0.0551235657185315,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
23742,google/t5-efficient-small-el64,This does not make a very readable text,pere/DeUnCaser,0.0635181888937949,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
23743,rinna/japanese-gpt-1b,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
23744,tau/splinter-large-qass,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,1.0,-1.0,NLP,3716fffd-17a6-4258-a3ca-042d3268e318___splinter-large-qass,HF
23745,patrickvonplaten/unispeech-sat-large-timit-ft,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0852310210466384,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
23746,rdenadai/BR_BERTo,T5-Efficient-SMALL-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm128,0.0349663874755302,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
23747,diper1998/distilgpt2-finetuned-AT,This is a sentence-transformers model: It maps sentences and paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search,sentence-transformers/paraphrase-MiniLM-L3-v2,0.0754712000489234,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23748,kyryl0s/gpt2-uk-xxs,"In order to facilitate users to reproduce the results, we used the publicly available corpus and provided all training details",uer/chinese_roberta_L-6_H-128,0.005756034515798,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
23749,google/t5-efficient-small-el32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0230456618592143,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
23750,qarib/bert-base-qarib_far_6500k,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0037323283031583,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
23751,superspray/electra_large_discriminator_squad2_custom_dataset,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0597793832421302,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
23752,saibo/random-roberta-mini,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0085882621351629,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
23753,tartuNLP/gpt-4-est-large,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0988566279411315,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
23754,google/bert_uncased_L-10_H-256_A-4,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0481574097648262,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
23755,mimicheng/codeparrot-ds,"Macaw (Multi-angle c(q)uestion answering) is a ready-to-use model capable of  general question answering,  showing robustness outside the domains it was trained on",allenai/macaw-answer-11b,0.0987260490655899,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
23756,google/t5-efficient-small-nl24,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.086737249046564,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
23757,google/t5-efficient-base-nl4,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0403507221490144,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
23758,dhpollack/distilbert-dummy-sentiment,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0678896941244601,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
23759,TJMUCH/transcriptome-iseeek,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23760,obss/mt5-base-3task-highlight-tquad2,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0651685092598199,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
23761,Palak/microsoft_deberta-base_squad,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0118150040507316,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
23762,google/t5-efficient-large-nh24,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0788233801722526,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
23763,sberbank-ai/rugpt3medium_based_on_gpt2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
23764,lassl/gpt2-ko-small,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0725942328572272,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
23765,microsoft/layoutlm-base-uncased,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,0.096048317849636,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
23766,stas/t5-very-small-random,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0262179560959339,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23767,piotr-rybak/poleval2021-task4-herbert-large-encoder,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0467116609215736,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
23768,tuner007/pegasus_qa,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0408006384968757,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
23769,google/t5-efficient-large-nl32,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0698572676628828,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
23770,google/bert_uncased_L-6_H-256_A-4,"However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher",google/bert_uncased_L-6_H-256_A-4,1.0,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
23771,uer/t5-v1_1-base-chinese-cluecorpussmall,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
23772,lgrobol/electra-minuscule-discriminator,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
23773,eli4s/Bert-L12-h384-A6,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.007086731493473,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
23774,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0636488199234008,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
23775,google/t5-efficient-base-el8,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
23776,google/t5-efficient-small-dm256,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0597159098833798,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
23777,google/t5-efficient-xl-nl2,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0517809949815273,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
23778,cosmoquester/bart-ko-small,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0497457782427469,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
23779,everdoubling/byt5-Korean-small,"Therefore, this model has to be fine-tuned before it is useable on a downstream task",google/byt5-xl,0.026762456446886,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
23780,google/t5-efficient-small-el16-dl4,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0761532895267008,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
23781,google/t5-efficient-large-nl16,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0045751500874757,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
23782,sberbank-ai/rugpt3large_based_on_gpt2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
23783,SetFit/deberta-v3-large__sst2__train-8-8,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0932510793209075,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
23784,google/t5-efficient-small-dm1000,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
23785,beston91/gpt2-xl_ft_logits_10k,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0651900172233581,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
23786,lgrobol/electra-minuscule-generator,"While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle",everdoubling/byt5-Korean-small,0.049917347729206,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
23787,google/t5-efficient-large-dm128,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.051027713343501,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
23788,google/t5-efficient-base-el8,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0437445100396871,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
23789,google/t5-efficient-large-dm768,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.053107239305973,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
23790,google/t5-efficient-base-nl32,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
23791,ml6team/mt5-small-german-finetune-mlsum,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0710624838247894,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
23792,sentence-transformers/paraphrase-MiniLM-L3-v2,This repository provides an extra-small-sized Japanese GPT-2 model,rinna/japanese-gpt2-xsmall,0.060536578297615,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
23793,google/t5-efficient-large-nh2,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0361719978973269,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
23794,dbmdz/convbert-base-german-europeana-cased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0576827935874461,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
23795,eli4s/Bert-L12-h240-A12,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
23796,cammy/led-large-16384-arxiv-100-MDS,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
23797,google/t5-efficient-small-dm768,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.035243397578597,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
23798,google/t5-efficient-base-nl2,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0877316929399967,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
23799,hfl/rbt3,"Macaw was built on top of T5 and comes in  three sizes: macaw-11b, macaw-3b,  and macaw-large, as well as an answer-focused version featured on  various leaderboards macaw-answer-11b",allenai/macaw-answer-11b,0.0814684964716433,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23800,google/t5-efficient-small-el16-dl4,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0131934080272912,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
23801,lassl/gpt2-ko-small,Evaulation results will be released soon,lassl/gpt2-ko-small,1.0,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
23802,stas/t5-very-small-random,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0844749882817268,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23803,google/t5-efficient-tiny-ff12000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0110934525728225,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
23804,mwesner/reformer-clm,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0671244338154792,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
23805,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0688423439860344,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
23806,aubmindlab/aragpt2-mega,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
23807,piotr-rybak/poleval2021-task4-herbert-large-encoder,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0671581625938415,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
23808,TJMUCH/transcriptome-iseeek,Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective,readerbench/RoBERT-small,0.0564596205949783,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23809,kyryl0s/gpt2-uk-xxs,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0556291881948709,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
23810,google/t5-efficient-large-dm512,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0004027057439088,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
23811,google/t5-efficient-large-dm512,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
23812,tau/tavbert-he,T5-Efficient-BASE-EL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el16,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
23813,mimi/Waynehills-NLP-doogie,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
23814,ductuan024/AimeLaw,"Then, it was compressed via progressive module replacing",rmihaylov/bert-base-pos-theseus-bg,0.0655972398817539,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
23815,DTAI-KULeuven/robbertje-1-gb-bort,"There is no structure of Zhouwenwang-Unified-1.3B in Transformers, you can run follow code to get structure of Zhouwenwang-Unified-1.3B from Fengshenbang-LMYou can use Zhouwenwang-1.3B to continue writing",IDEA-CCNL/Zhouwenwang-Unified-1.3B,0.0629048577199379,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
23816,deep-learning-analytics/triviaqa-t5-base,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
23817,google/t5-efficient-small-el4,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
23818,akdeniz27/deberta-v2-xlarge-cuad,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0748763978481292,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
23819,Palak/microsoft_deberta-base_squad,"Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee",lgris/distilxlsr_bp_8-12,0.0818475857377052,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
23820,mikaelsouza/msft-regular-model,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0965225100517273,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
23821,facebook/wmt21-dense-24-wide-en-x,It was introduced in this paper and first released in this repository,facebook/wmt21-dense-24-wide-en-x,1.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
23822,google/t5-efficient-large-nl36,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.048540050163865,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
23823,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0215857401490211,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
23824,google/t5-efficient-xl-nl4,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0715382993221283,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
23825,google/t5-efficient-base-kv128,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
23826,microsoft/layoutlm-base-uncased,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.094200924038887,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
23827,KBLab/megatron-bert-large-swedish-cased-165k,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
23828,google/t5-efficient-small-nl2,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0531440638005733,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
23829,rinna/japanese-gpt-1b,This is a sentence-transformers model: It maps sentences and paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search,sentence-transformers/paraphrase-MiniLM-L3-v2,0.0640524514019489,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
23830,lgrobol/electra-minuscule-generator,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
23831,microsoft/layoutlm-large-uncased,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0701058357954025,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
23832,rdenadai/BR_BERTo,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,0.0160307139158248,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
23833,google/t5-efficient-base-nh24,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0528963077813386,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
23834,VietAI/gpt-neo-1.3B-vietnamese-news,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,1.0,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23835,nghuyong/ernie-tiny,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0038881196329991,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
23836,GKLMIP/electra-myanmar-small-uncased,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.069508608430624,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
23837,allenai/wmt16-en-de-dist-6-1,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0649364255368709,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
23838,IDEA-CCNL/Erlangshen-Longformer-110M,I have made no attempts to censor,bigjoedata/rockbot,0.0305077540688216,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
23839,google/t5-efficient-small-kv16,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0127525385469198,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
23840,VietAI/gpt-neo-1.3B-vietnamese-news,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0394499842077493,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23841,hfl/chinese-legal-electra-base-generator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
23842,cammy/led-base-16384-100-MDS,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0247480981051921,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
23843,google/t5-efficient-tiny-nl16,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0772098824381828,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
23844,amazon/bort,"CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationYunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu",fnlp/cpt-base,0.0648003928363323,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23845,google/t5-efficient-small-dm1000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0502275275066494,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
23846,mwesner/reformer-clm,T5-Efficient-BASE-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff9000,0.0658017098903656,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
23847,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0838122479617595,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
23848,stas/mt5-tiny-random,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23849,microsoft/layoutlm-base-cased,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0351738780736923,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23850,DeepPavlov/distilrubert-tiny-cased-conversational,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0082311940689881,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
23851,google/t5-efficient-tiny-ff12000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0132985292002558,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
23852,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0786423534154892,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
23853,hfl/chinese-electra-small-ex-discriminator,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0087397769093513,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
23854,google/t5-efficient-large-el2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
23855,google/t5-efficient-base-dm256,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0475868619978427,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
23856,rdenadai/BR_BERTo,This model has been traind with a MarianNMT v1.10.23; commit: 42f0b8b7 transformer-big typed environment,NYTK/translation-marianmt-en-hu,0.0223829572399457,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
23857,google/t5-efficient-large-nl12,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0711725652217864,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
23858,allenai/macaw-3b,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.047779702891906,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
23859,microsoft/layoutlm-base-uncased,"CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationYunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu",fnlp/cpt-base,0.0389067530632019,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
23860,rahulacj/bertweet-base-finetuned-sentiment-analysis,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0600083209574222,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
23861,google/t5-efficient-large-kv128,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
23862,andrejmiscic/simcls-scorer-billsum,ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token,everdoubling/byt5-Korean-small,0.0471824029968543,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
23863,microsoft/layoutlm-base-cased,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,0.056502029299736,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23864,google/t5-efficient-small-nl2,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
23865,google/t5-efficient-mini-nl12,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0661305747926234,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
23866,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0118859753012657,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
23867,akdeniz27/deberta-v2-xlarge-cuad,This is ELECTRA Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of YACIS blog corpus,ptaszynski/yacis-electra-small-japanese,0.0450625792145729,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
23868,hfl/rbt3,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0594105087220667,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23869,ncduy/phobert-large-finetuned-vietnamese_students_feedback,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0748267993330955,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
23870,akdeniz27/deberta-v2-xlarge-cuad,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0753467977046966,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
23871,1one5ome/gpt2-chinese-gulong,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0955112874507904,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
23872,google/rembert,This version of the checkpoint is lightweight since it is meant to be finetuned for classification and excludes the output embedding weights,google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
23873,bigjoedata/rockbot,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,1.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
23874,mikaelsouza/msft-regular-model,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,0.0815137103199958,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
23875,stas/mt5-tiny-random,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.0839120969176292,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23876,SupriyaArun/squeezebert-uncased-finetuned-squad,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0922389104962349,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
23877,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
23878,microsoft/layoutlm-base-cased,"This is one of the smaller pre-trained BERT variants, together with bert-tiny, bert-mini and bert-small",prajjwal1/bert-medium,0.0120594892650842,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23879,google/t5-efficient-small-kv32,Its original repository can be found here,tau/splinter-large-qass,0.077117396518588,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
23880,SajjadAyoubi/distil-bigbird-fa-zwnj,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0305529470954622,-1.0,NLP,93bf4f14-6c7a-41bc-854c-061751656605___distil-bigbird-fa-zwnj,HF
23881,google/t5-efficient-small-ff6000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0298321275040507,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
23882,cosmoquester/bart-ko-base,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0148665172358353,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
23883,google/t5-efficient-base-dm1000,T5-Efficient-BASE-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm1000,1.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
23884,jweb/japanese-soseki-gpt2-1b,This is a sentence-transformers model: It maps sentences and paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search,piotr-rybak/poleval2021-task4-herbert-large-encoder,0.076123314599196,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
23885,bookbot/distil-wav2vec2-adult-child-cls-37m,Training metrics were logged via Tensorboard,bookbot/distil-wav2vec2-adult-child-cls-37m,1.0,-1.0,NLP,51a35638-b085-4ae0-97e6-85e288a0f2d1___distil-wav2vec2-adult-child-cls-37m,HF
23886,tartuNLP/gpt-4-est-large,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
23887,ian0delond/model-test,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
23888,fnlp/bart-base-chinese,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0492656510323285,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
23889,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0576797984540462,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
23890,mrm8488/mobilebert-uncased-finetuned-squadv2,"In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns",pere/DeUnCaser,0.0,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
23891,google/t5-efficient-xl-nl16,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0665781199932098,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
23892,mikaelsouza/msft-regular-model,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0659148246049881,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
23893,crabz/distil-slovakbert-ner,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0945953950285911,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
23894,google/t5-efficient-large-dm128,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0025774482637643,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
23895,google/t5-efficient-small-el4,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
23896,KoichiYasuoka/roberta-base-ukrainian,T5-Efficient-LARGE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el6,0.0885723978281021,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
23897,google/t5-efficient-large-dm128,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0389649625867604,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
23898,google/t5-efficient-tiny-ff9000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.047025834210217,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
23899,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3d869c42-a605-48ad-b38a-746c8056eb1b___t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
23900,wukevin/tcr-bert,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0507871350273489,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
23901,microsoft/layoutlm-base-uncased,The model was pretrained on Libri-Light,facebook/hubert-xlarge-ll60k,0.00856757722795,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
23902,TJMUCH/transcriptome-iseeek,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0578970573842525,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
23903,kyryl0s/gpt2-uk-xxs,The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our Stack Overflow Dataset repository,UWB-AIR/MQDD-duplicates,0.0366880607325583,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
23904,amazon/bort,Pretrained XLNET large language model for Malay,malay-huggingface/xlnet-large-bahasa-cased,0.0240112962201237,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23905,IDEA-CCNL/Zhouwenwang-Unified-110M,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0613289453089236,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
23906,google/t5-efficient-large-dm2000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
23907,microsoft/layoutlm-base-cased,"The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.013784185051918,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
23908,PlanTL-GOB-ES/gpt2-large-bne,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0263055509421974,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
23909,KoichiYasuoka/roberta-base-ukrainian,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT)",eli4s/Bert-L12-h384-A6,0.0260529182851314,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
23910,google/t5-efficient-small-nl22,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0439686505123972,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
23911,tuner007/pegasus_qa,The three provided models are randomly selected from 10 that were trained and reported in the paper,phueb/BabyBERTa-2,0.0999613404273986,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
23912,tau/tavbert-he,T5-Efficient-LARGE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh32,0.0170227456837892,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
23913,google/t5-efficient-small-dm256,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0528414221480488,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
23914,sentence-transformers/paraphrase-MiniLM-L3-v2,This model used the pre-trained weights of distilbert-base-cased as starting point (only 4 layers) and was able to achieve 84% accuracy on the test set,khizon/distilbert-unreliable-news-eng-4L,0.048244833946228,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
23915,aditeyabaral/sentencetransformer-distilbert-hinglish-big,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.056479625403881,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
23916,cointegrated/rubert-tiny-bilingual-nli,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,1.0,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
23917,google/t5-efficient-small-kv32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
23918,cammy/led-large-16384-arxiv-100-MDS,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.047416552901268,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
23919,fnlp/bart-large-chinese,LayoutLM archives the SOTA results on multiple datasets,microsoft/layoutlm-large-uncased,0.0400117300450801,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
23920,hfl/rbt3,T5-Efficient-BASE-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm1000,0.032241841778159,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
23921,tobiaslee/bert-2l-768h-uncased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
23922,google/t5-efficient-large-el2,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
23923,google/t5-efficient-xl-nl16,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0673460066318512,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
23924,google/t5-efficient-base-nl32,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0387361133471131,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
23925,Recognai/selectra_medium,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0674425843171775,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
23926,google/t5-efficient-large-nh2,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0149016454815864,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
23927,google/t5-efficient-base-ff9000,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0577694848179816,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
23928,hfl/rbtl3,The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider max_seq_length (4096 vs,yorko/scibert_scivocab_uncased_long_4096,0.0572312511503696,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
23929,google/t5-efficient-large-dm256,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0572892669588326,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
23930,google/t5-efficient-tiny-ff3000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0375378970056771,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
23931,minimaxir/magic-the-gathering,This model is cased: it makes a difference between english and English,google/fnet-large,0.0329571226611733,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
23932,google/t5-efficient-small-kv32,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0552358124405145,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
23933,google/t5-efficient-base-nl48,T5-Efficient-BASE-NL48 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl48,1.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
23934,abdusahmbzuai/ft-tatoeba-ar-en,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0254373867064714,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
23935,TransQuest/monotransquest-hter-en_lv-it-nmt,The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation,TransQuest/monotransquest-hter-en_lv-it-nmt,1.0,-1.0,NLP,23887677-bc09-4b03-8c34-709db032084a___monotransquest-hter-en_lv-it-nmt,HF
23936,google/t5-efficient-small-ff3000,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
23937,diper1998/distilgpt2-finetuned-AT,We train the model on a very large and heterogeneous French corpus,asi/gpt-fr-cased-base,0.0852705240249633,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
23938,hfl/chinese-electra-180g-small-ex-discriminator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
23939,jonatasgrosman/wav2vec2-xls-r-1b-english,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0584262472887833,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
23940,mikaelsouza/msft-regular-model,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0915164276957511,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
23941,VietAI/gpt-neo-1.3B-vietnamese-news,I have made no attempts to censor,bigjoedata/rockbot,0.0083332303911447,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
23942,Iskaj/hf-challenge-test,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0460192710161209,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
23943,aubmindlab/aragpt2-mega,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
23944,mwesner/reformer-clm,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.0254504587501287,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
23945,stas/t5-very-small-random,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.038841962814331,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
23946,monsoon-nlp/dv-muril,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,1.0,-1.0,NLP,5b450d07-4459-4808-8691-381f16b1106c___dv-muril,HF
23947,google/t5-efficient-large-dm768,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0444701388478278,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
23948,google/bert_uncased_L-4_H-128_A-2,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-4_H-128_A-2,1.0,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
23949,groar/gpt-neo-1.3B-finetuned-escape2,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0114888623356819,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
23950,google/t5-efficient-mini,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0868261829018592,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
23951,DemangeJeremy/4-sentiments-with-flaubert,"512) which is handy in case of working with long texts, e",yorko/scibert_scivocab_uncased_long_4096,0.041976436972618,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23952,dbmdz/convbert-base-german-europeana-cased,"Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,  but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times",nghuyong/ernie-tiny,0.0486283426483471,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
23953,google/t5-efficient-tiny-nl32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0697639249265193,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
23954,allenai/wmt19-de-en-6-6-base,A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-tiny,0.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
23955,ml6team/distilbart-tos-summarizer-tosdr,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0424035983160138,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
23956,j-hartmann/purchase-intention-english-roberta-large,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0290753735850254,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
23957,google/t5-efficient-large-nl10,"Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational,0.035992406308651,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
23958,google/t5-efficient-small-nl40,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0315494015812873,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
23959,mrm8488/xlm-multi-finetuned-xquadv1,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0678738206624984,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
23960,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0563103854656219,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
23961,amazon/bort,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23962,suzuki/distilbert-base-uncased-finetuned-squad,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0425868034362793,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
23963,google/t5-efficient-small-nl22,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0658104605972766,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
23964,google/t5-efficient-large-nl12,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
23965,aditeyabaral/sentencetransformer-bert-hinglish-small,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,0.0061150193214416,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
23966,google/bert_uncased_L-6_H-256_A-4,Its original repository can be found here,tau/splinter-large-qass,0.0176675698409477,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
23967,DemangeJeremy/4-sentiments-with-flaubert,We release a small and medium version with the following configuration:SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section below),Recognai/selectra_medium,0.0422703642398118,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
23968,google/t5-efficient-large-nh24,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.047333875671029,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
23969,DTAI-KULeuven/robbertje-1-gb-bort,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0060758783171574,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
23970,suzuki/distilbert-base-uncased-finetuned-squad,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0642731711268425,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
23971,google/t5-efficient-small-ff9000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0303325187414884,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
23972,IDEA-CCNL/Erlangshen-Longformer-330M,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0172702334821224,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
23973,pulp/CHILDES-ParentBERTo,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0333756990730762,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
23974,cammy/led-base-16384-100-MDS,"Pre-Training with Whole Word Masking for Chinese BERTYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping HuThis repository is developed based on：https://github",hfl/rbtl3,0.0703074410557746,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
23975,Palak/microsoft_deberta-base_squad,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0496054515242576,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
23976,stas/mt5-tiny-random,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
23977,google/t5-efficient-small-nl40,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0485918968915939,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
23978,eli4s/Bert-L12-h384-A6,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
23979,malay-huggingface/xlnet-large-bahasa-cased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0621689104785521,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
23980,IDEA-CCNL/Erlangshen-Longformer-110M,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.0012565031647682,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
23981,gilparmentier/pokemon_gptj_model,Amazon's BORT,amazon/bort,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
23982,mmaguero/gn-bert-tiny-cased,"As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly",thu-coai/LongLM-large,0.0593053195625543,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
23983,malay-huggingface/t5-super-super-tiny-bahasa-cased,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0461601912975311,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
23984,google/t5-efficient-large-el2,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0380954009015113,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
23985,amazon/bort,The hyperparameters for training followed the setting for RoBERTa,KBLab/megatron-bert-large-swedish-cased-165k,0.0083150947466492,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
23986,lassl/gpt2-ko-small,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0522727551870047,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
23987,indobenchmark/indobert-base-p1,This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin,Vilnius-Lithuania-iGEM/Albumin,0.0517522245645522,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
23988,lgrobol/electra-minuscule-generator,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0608603525906801,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
23989,google/t5-efficient-small-el64,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.0053752260282635,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
23990,google/t5-efficient-xl-nl28,"A sentence initial language token is required in the form of &gt;&gt;id&lt;&lt; (id = valid target language ID), e",Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,0.0632599629461765,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
23991,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0092966053634881,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
23992,google/t5-efficient-base-kv128,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0678235962986945,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
23993,akdeniz27/deberta-v2-xlarge-cuad,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0836436003446579,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
23994,google/t5-efficient-small-el12,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0464951228350401,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
23995,brad1141/Longformer-finetuned-norm,"This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:The model was fine-tuned on 2,000 manually annotated social media posts",j-hartmann/purchase-intention-english-roberta-large,0.057980440557003,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
23996,jpcorb20/pegasus-large-reddit_tifu-samsum-512,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0397311784327029,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
23997,gilparmentier/pokemon_gptj_model,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0012132362462579,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
23998,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral",dexhrestha/Nepali-DistilBERT,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
23999,google/t5-efficient-base-dm1000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0011300090700387,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
24000,gchhablani/fnet-base-finetuned-wnli,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0346062108874321,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
24001,google/t5-efficient-small-nl2,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
24002,google/reformer-enwik8,"enwik8 is a dataset based on Wikipedia and is often used to measure the model's ability to compress data, e",google/reformer-enwik8,1.0,-1.0,NLP,6aa23d61-51e8-44d6-b6b7-b68cce7cd0c1___reformer-enwik8,HF
24003,malay-huggingface/t5-super-tiny-bahasa-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0580194192007183,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
24004,piotr-rybak/poleval2021-task4-herbert-large-encoder,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
24005,superspray/electra_large_discriminator_squad2_custom_dataset,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0149340825155377,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
24006,google/t5-efficient-base-dm2000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.025398944504559,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
24007,google/t5-efficient-small-dm2000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0595018928870558,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
24008,google/t5-efficient-tiny-nl32,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0471984464675188,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
24009,amazon/bort,"Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)",facebook/mbart-large-50-many-to-one-mmt,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
24010,google/t5-efficient-xl-nl2,"This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts",macedonizer/al-roberta-base,0.0404206719249486,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
24011,negfir/distilbert-base-uncased-finetuned-squad,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
24012,google/t5-efficient-mini-nl6,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0393739361315965,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
24013,suzuki/distilbert-base-uncased-finetuned-squad,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
24014,google/t5-efficient-xl-nl6,"However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training",dexhrestha/Nepali-DistilBERT,0.0596256107091903,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
24015,jcblaise/distilbert-tagalog-base-cased,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,1.0,-1.0,NLP,29e88f0d-47b4-4733-8b59-3cbe0f4bb77e___distilbert-tagalog-base-cased,HF
24016,anirudh21/xlnet-base-cased-finetuned-rte,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0058888159692287,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
24017,mrm8488/xlm-multi-finetuned-xquadv1,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0458471411839127,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
24018,hfl/rbtl3,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
24019,google/t5-efficient-tiny-nh16,"The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda",jean-paul/kinyaRoberta-small,0.0448572738096117,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
24020,razent/SciFive-base-PMC,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0419739885255694,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
24021,google/t5-efficient-small-kv256,"Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT)",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0746029783040284,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
24022,google/t5-efficient-base-nh16,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0418457137420773,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
24023,allenai/wmt16-en-de-12-1,"This  model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon  admission",mnaylor/bigbird-base-mimic-mortality,0.0,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
24024,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-MINI-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl24,0.0363885406404734,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
24025,kssteven/ibert-roberta-base,"This model, ibert-roberta-base, is an integer-only quantized version of RoBERTa, and was introduced in this paper",kssteven/ibert-roberta-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
24026,google/t5-efficient-base-nh24,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
24027,google/t5-efficient-small-nl2,"Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters",microsoft/unihanlm-base,0.0592927467077969,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
24028,jorge-henao/gpt2-small-spanish-disco-poetry-wt,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0454786382615566,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
24029,google/t5-efficient-small-nl48,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0550238462164998,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
24030,tau/tavbert-he,T5-Efficient-LARGE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm256,0.0125759206712245,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
24031,hf-internal-testing/tiny-random-rembert,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0611806958913803,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
24032,mwesner/reformer-clm,T5-Efficient-SMALL-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el4,0.0779643952846527,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
24033,google/t5-efficient-tiny-nl16,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0445941686630248,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
24034,google/t5-efficient-small-el64,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0090263215824961,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
24035,microsoft/layoutlm-base-uncased,Disclaimer: This model card has been written by gchhablani,google/fnet-large,0.0939957126975059,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
24036,KoichiYasuoka/roberta-base-ukrainian,It has less than 1% difference in performance compared to the BERT based model while having 2.0x the speed,khizon/distilbert-unreliable-news-eng-4L,0.0919523909687995,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
24037,rdenadai/BR_BERTo,"The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 256",eli4s/Bert-L12-h256-A4,0.03076398683091,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
24038,RabotaRu/HRBert-mini,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0490441480651497,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
24039,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0839160457253456,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
24040,Milos/slovak-gpt-j-405M,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.032112349756062,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
24041,ml6team/distilbart-tos-summarizer-tosdr,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.009468438103795,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
24042,google/t5-efficient-large-nl16,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0816994979977607,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
24043,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
24044,google/t5-efficient-small-dm128,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0655673649162053,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
24045,malay-huggingface/t5-tiny-bahasa-cased,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0120111033320427,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
24046,google/t5-efficient-base-dm2000,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0904021635651588,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
24047,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0211731325834989,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
24048,google/t5-efficient-small-dm2000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0569129921495914,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
24049,mimicheng/codeparrot-ds,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
24050,SetFit/deberta-v3-large__sst2__train-8-8,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
24051,google/t5-efficient-large-kv32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0396785829216241,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
24052,Iskaj/hf-challenge-test,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0740745961666107,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
24053,google/t5-efficient-tiny-nl32,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0121552408672869,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
24054,IDEA-CCNL/Erlangshen-Longformer-330M,A debatable advantage of pulling random-roberta-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time,saibo/random-roberta-mini,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
24055,clue/roberta_chinese_3L312_clue_tiny,"The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages",jean-paul/kinyaRoberta-small,0.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
24056,google/t5-efficient-large-dm768,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
24057,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
24058,stas/mt5-tiny-random,"The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture",jonfd/convbert-small-igc-is,0.0541297867894172,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
24059,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl16,0.0964963808655738,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
24060,google/t5-efficient-tiny-nh8,The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts,thu-coai/LongLM-large,0.0352841336280106,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
24061,google/t5-efficient-tiny-ff9000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
24062,jhu-clsp/bibert-ende,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0314627798895041,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
24063,google/t5-efficient-base-nl32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0386115722358226,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
24064,google/t5-efficient-base-kv32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
24065,bigjoedata/rockbot,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,1.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
24066,microsoft/layoutlm-base-cased,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
24067,soheeyang/rdr-ctx_encoder-single-trivia-base,We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition,mldmm/GlassBERTa,0.0,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
24068,hfl/chinese-legal-electra-base-generator,"If it infringes on your rights and interests or violates social morality, please do not propagate it, but contact the authors and the authors will deal with it promptly",TsinghuaAI/CPM-Generate,0.025896710033218,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
