,name,text,source_text,score,input,task,folder_name,model
0,segmentation.deeplabv3_resnet50,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,1.0,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1,video.mc3_18,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,0.169068731,1x32x32,video classification,Null,TV
2,mnasnet0_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,0.211013402,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
3,convnext_base,"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_vgg16,0.076413212,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
4,quantization.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_vgg16,0.12626302,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
5,inception_v3,"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,0.198571393,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
6,resnext50_32x4d,"resnext50_32x4d is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",resnext50_32x4d,1.0,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
7,detection.fasterrcnn_mobilenet_v3_large_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_18,0.13493404,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
8,vgg16_bn,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.111653236,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
9,regnet_y_400mf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.191344368,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
10,regnet_y_3_2gf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.104835988,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
11,segmentation.lraspp_mobilenet_v3_large,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,1.0,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
12,detection.ssdlite320_mobilenet_v3_large,"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_v3,0.138262773,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
13,vgg19_bn,spatial padding was used to preserve the spatial resolution of the image,vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
14,quantization.resnext101_32x8d,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.126232811,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
15,convnext_small,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.082486242,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
16,quantization.inception_v3,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.242054427,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
17,convnext_small,EfficientNet-B7 only has 66 million parameters,efficientnet_b7,0.191862892,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
18,vgg13_bn,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.165577521,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
19,convnext_tiny,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.078072398,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
20,regnet_y_32gf,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.182353776,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
21,regnet_x_3_2gf,ReLU non-linearity as activation functions,vgg11,0.160197474,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
22,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,0.183508546,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
23,densenet161,"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_vgg16,0.142625632,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
24,segmentation.fcn_resnet50,Avoiding the use of dense layers means less parameters (making the networks faster to train),segmentation.fcn_resnet50,1.0,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
25,quantization.mobilenet_v3_large,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.118356249,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
26,inception_v3,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.168139932,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
27,quantization.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_resnet50,0.182492353,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
28,detection.fasterrcnn_resnet50_fpn,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.048589262,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
29,detection.ssdlite320_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,1.0,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
30,regnet_x_3_2gf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.161851938,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
31,segmentation.fcn_resnet101,"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,0.19968776,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
32,vgg11_bn,adopted from BERT,vit_b_16,0.003360602,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
33,vgg19_bn,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.16787398,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
34,video.r3d_18,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.137464964,1x32x32,video classification,Null,TV
35,segmentation.deeplabv3_resnet101,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.07692579,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
36,convnext_large,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.068473766,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
37,squeezenet1_0,"(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_18,0.135422088,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
38,convnext_large,Constructs a DeepLabV3 model with a ResNet-50 backbone,segmentation.deeplabv3_resnet50,0.208541344,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
39,segmentation.fcn_resnet101,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.100816686,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
40,mobilenet_v2,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.153749708,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
41,detection.fasterrcnn_resnet50_fpn,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
42,vgg13,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,0.223966841,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
43,vit_b_16,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.133983968,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
44,mobilenet_v3_small,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.129594063,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
45,video.mc3_18,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.153516183,1x32x32,video classification,Null,TV
46,mnasnet1_3,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.127500099,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
47,detection.fasterrcnn_mobilenet_v3_large_320_fpn,Note that this model does not include any fine-tuned heads,vit_b_16,0.180279482,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
48,efficientnet_b1,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.200521004,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
49,regnet_y_800mf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.1571373,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
50,vit_l_16,The network design includes the use of a hard swish activation and squeeze-and-excitation modules in the MBConv blocks,mobilenet_v3_small,0.170187959,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
51,mnasnet1_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_18,0.175937229,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
52,segmentation.deeplabv3_mobilenet_v3_large,Replacing ReLU with GeLU,convnext_tiny,0.13952764,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
53,detection.keypointrcnn_resnet50_fpn,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.174462689,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
54,vgg13_bn,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.137498893,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
55,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,0.164697576,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
56,densenet169,VGG19 has 19.6 billion FLOPs,vgg19,0.138062938,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
57,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_vgg16,0.176188564,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
58,convnext_tiny,"The number of channels in outer 1x1 convolutions is the same, Wide ResNet-50-2 has 2048-1024-2048 channels",wide_resnet50_2,0.223743801,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
59,vit_b_32,Constructs a RegNetY_8GF architecture,regnet_y_8gf,0.185633374,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
60,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,0.1406077,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
61,regnet_y_8gf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_8gf,1.0,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
62,video.r2plus1d_18,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.200434679,1x32x32,video classification,Null,TV
63,googlenet,Note that this model does not include any fine-tuned heads,vit_b_16,0.223064929,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
64,mobilenet_v2,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.131341455,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
65,quantization.resnet18,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.141365051,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
66,efficientnet_b5,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.122315387,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
67,regnet_y_1_6gf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.165061903,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
68,vit_l_32,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.129908438,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
69,vit_b_16,MNASNet with depth multiplier of 1.0,mnasnet1_0,0.195247041,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
70,vit_l_16,"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_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
71,resnet34,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.133737719,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
72,vgg11_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_vgg16,0.125215635,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
73,convnext_large,Replacing ReLU with GeLU,convnext_large,1.0,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
74,regnet_x_800mf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.147796485,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
75,segmentation.lraspp_mobilenet_v3_large,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.187763652,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
76,shufflenet_v2_x1_0,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.160084746,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
77,resnet152,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",resnet152,1.0,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
78,convnext_large,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.189465061,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
79,mnasnet0_75,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.19643714,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
80,quantization.shufflenet_v2_x1_0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.207552163,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
81,shufflenet_v2_x1_0,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.182076159,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
82,regnet_x_800mf,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.103950027,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
83,detection.keypointrcnn_resnet50_fpn,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.122811305,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
84,quantization.shufflenet_v2_x1_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_vgg16,0.089045918,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
85,vgg16_bn,adopted from BERT,vit_b_16,0.004726145,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
86,convnext_base,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.081674315,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
87,shufflenet_v2_x0_5,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.105044777,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
88,regnet_y_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,0.162170472,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
89,regnet_y_8gf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.14705356,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
90,quantization.shufflenet_v2_x0_5,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.112172532,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
91,vgg13_bn,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.195721264,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
92,mobilenet_v3_large,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.208837342,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
93,segmentation.deeplabv3_resnet101,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.127709067,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
94,convnext_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_5,0.128541304,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
95,regnet_x_32gf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.172905268,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
96,regnet_x_400mf,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.047885204,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
97,inception_v3,Constructs a RegNetX_1.6GF architecture,regnet_x_1_6gf,0.222609254,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
98,regnet_x_400mf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.184038401,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
99,mnasnet1_3,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.158567669,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
100,regnet_y_800mf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_800mf,1.0,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
101,densenet201,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.143218542,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
102,resnet18,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.135807884,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
103,mobilenet_v3_small,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.132671275,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
104,mnasnet0_5,"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_5,1.0,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
105,convnext_large,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.159189717,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
106,segmentation.fcn_resnet101,Replacing ReLU with GeLU,convnext_tiny,0.143804338,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
107,convnext_base,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.081838737,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
108,densenet201,"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_vgg16,0.14738918,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
109,quantization.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",vgg19,0.184532201,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
110,segmentation.deeplabv3_resnet50,The original dataset contained images of size 512x512x1,resnet101,0.123818268,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
111,segmentation.lraspp_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",resnet18,0.216764814,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
112,detection.ssdlite320_mobilenet_v3_large,based on ResNeXt-50 as its backbone along with the feature Pyramid Network (FPN) for better accuracy and speed,detection.maskrcnn_resnet50_fpn,0.225563419,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
113,segmentation.lraspp_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_resnet50,0.205032006,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
114,wide_resnet50_2,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.190348893,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
115,segmentation.deeplabv3_resnet50,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.112389186,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
116,densenet121,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.149074076,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
117,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.204030547,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
118,detection.fasterrcnn_mobilenet_v3_large_fpn,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.155932972,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
119,mobilenet_v3_large,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",vgg19,0.179501535,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
120,shufflenet_v2_x1_5,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.16135927,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
121,regnet_x_800mf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.151531205,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
122,convnext_small,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.236472971,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
123,video.mc3_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_vgg16,0.154727133,1x32x32,video classification,Null,TV
124,vgg19,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.140560732,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
125,convnext_base,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.239855677,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
126,quantization.shufflenet_v2_x1_0,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.123846683,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
127,convnext_small,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.17528453,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
128,efficientnet_b1,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.157135157,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
129,segmentation.lraspp_mobilenet_v3_large,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.114351589,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
130,detection.ssdlite320_mobilenet_v3_large,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.163690818,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
131,regnet_x_3_2gf,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.207361338,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
132,convnext_tiny,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.116015293,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
133,segmentation.deeplabv3_resnet50,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.077819772,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
134,convnext_small,"The RegNet is composed of a stem, body and head",regnet_y_400mf,0.17641744,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
135,vit_b_16,Constructs a Faster R-CNN model with a ResNet-50-FPN backbone,detection.fasterrcnn_resnet50_fpn,0.186197685,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
136,regnet_y_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,0.148772483,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
137,mobilenet_v3_large,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.142677879,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
138,convnext_small,They stack residual blocks ontop of each other to form network,vgg19_bn,0.102552163,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
139,resnext50_32x4d,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.156171395,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
140,quantization.mobilenet_v3_large,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.124609489,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
141,quantization.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,0.194387154,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
142,quantization.resnet50,They use option 2 for increasing dimensions,quantization.resnet50,1.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
143,segmentation.lraspp_mobilenet_v3_large,"It has 11 weight layers in total, so the name VGG11",vgg11,0.188625047,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
144,regnet_x_800mf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.113320798,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
145,vit_b_16,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_400mf,0.128803262,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
146,convnext_base,SqueezeNet 1.0 achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters,squeezenet1_0,0.122840345,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
147,quantization.mobilenet_v2,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.166615618,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
148,segmentation.fcn_resnet101,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.199529952,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
149,detection.retinanet_resnet50_fpn,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.155964815,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
150,shufflenet_v2_x2_0,ReLU non-linearity as activation functions,vgg11,0.140335482,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
151,efficientnet_b0,efficientnet_b0 is used for image classification,efficientnet_b0,1.0,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
152,shufflenet_v2_x1_0,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.121226133,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
153,efficientnet_b2,"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_resnet50,0.181300781,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
154,efficientnet_b0,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.163335456,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
155,regnet_x_1_6gf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.144606292,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
156,resnext101_32x8d,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.101778946,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
157,segmentation.lraspp_mobilenet_v3_large,It is the simplest of all other vgg versions,vgg11,0.190305451,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
158,segmentation.fcn_resnet101,adopted from BERT,vit_b_16,0.003552053,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
159,mnasnet0_5,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.128943672,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
160,regnet_y_32gf,"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_16,0.154851924,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
161,wide_resnet101_2,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.142206093,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
162,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_vgg16,0.143987755,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
163,segmentation.lraspp_mobilenet_v3_large,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.155695567,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
164,regnet_x_16gf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.159386689,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
165,detection.ssdlite320_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,0.159019337,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
166,convnext_small,This network is a pretty large network and it has about 138 million parameters,vgg16,0.115591605,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
167,quantization.shufflenet_v2_x0_5,adopted from BERT,vit_b_16,0.001639866,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
168,convnext_small,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.104952407,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
169,regnet_x_32gf,regnet_x_32gf is used for image classification,regnet_x_32gf,1.0,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
170,regnet_x_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,0.140381369,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
171,vgg19,adopted from BERT,vit_b_16,0.001244321,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
172,mobilenet_v2,The number of parameters is 3.4 million,mobilenet_v2,1.0,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
173,detection.fasterrcnn_mobilenet_v3_large_fpn,The required minimum input size of the model is 17x17,squeezenet1_1,0.197586479,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
174,vit_l_16,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.140455452,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
175,segmentation.deeplabv3_resnet50,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,1.0,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
176,detection.fasterrcnn_mobilenet_v3_large_fpn,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.086289206,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
177,convnext_base,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.151433907,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
178,shufflenet_v2_x2_0,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.176526899,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
179,efficientnet_b2,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.182582629,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
180,mnasnet1_3,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.051401536,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
181,detection.fasterrcnn_mobilenet_v3_large_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_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
182,resnet50,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.155350948,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
183,shufflenet_v2_x1_5,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.154111709,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
184,densenet169,"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_vgg16,0.141672504,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
185,convnext_small,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_small,1.0,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
186,video.r3d_18,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.045762268,1x32x32,video classification,Null,TV
187,quantization.shufflenet_v2_x0_5,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.219514436,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
188,vgg13,Replacing ReLU with GeLU,convnext_tiny,0.144103618,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
189,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.171688949,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
190,densenet161,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.171257772,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
191,video.mc3_18,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.166356306,1x32x32,video classification,Null,TV
192,vgg11_bn,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.095155791,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
193,vgg19_bn,Implemented three fully connected layers from which first two were of size 4096 and after that a layer with 1000 channels for 1000-way ILSVRC classification and the final layer is a softmax function,vgg19_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
194,vit_b_32,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.138533117,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
195,quantization.shufflenet_v2_x0_5,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.174606381,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
196,video.mc3_18,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.081735505,1x32x32,video classification,Null,TV
197,mnasnet0_75,"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)",vgg19,0.247697666,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
198,regnet_x_800mf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.145999553,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
199,video.r3d_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_18,0.11986176,1x32x32,video classification,Null,TV
200,convnext_base,"The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks",efficientnet_b0,0.154959636,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
201,quantization.resnet50,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.102508662,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
202,wide_resnet101_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,0.232701339,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
203,mnasnet0_5,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.228261347,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
204,mnasnet1_3,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.071494528,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
205,regnet_x_400mf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.140770111,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
206,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,0.166532639,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
207,segmentation.fcn_resnet50,fcn_resnet50 is used for semantic segmentation,segmentation.fcn_resnet50,1.0,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
208,vit_l_16,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.138502641,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
209,vgg13,Constructs a RegNetY_1.6GF architecture,regnet_y_1_6gf,0.246047866,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
210,vgg13,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.164118404,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
211,regnet_x_400mf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.139550077,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
212,densenet201,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.054743445,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
213,segmentation.lraspp_mobilenet_v3_large,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.192322442,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
214,convnext_tiny,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,1.0,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
215,segmentation.lraspp_mobilenet_v3_large,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.189257243,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
216,quantization.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,0.213928867,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
217,efficientnet_b4,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.170105173,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
218,quantization.shufflenet_v2_x1_0,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.162076297,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
219,detection.ssdlite320_mobilenet_v3_large,"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,0.192861527,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
220,convnext_base,MobileNet v3-Small has 2.5 million parameters,mobilenet_v3_small,0.147240284,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
221,detection.maskrcnn_resnet50_fpn,It is the simplest of all other vgg versions,vgg11,0.167227046,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
222,vgg11,"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,0.141617162,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
223,detection.ssdlite320_mobilenet_v3_large,is a convolutional neural network that is 50 layers deep,wide_resnet50_2,0.16874247,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
224,regnet_y_800mf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_800mf,1.0,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
225,resnet34,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.230511625,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
226,efficientnet_b7,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b7,1.0,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
227,detection.retinanet_resnet50_fpn,The required minimum input size of the model is 29x29,densenet121,0.158962149,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
228,regnet_x_1_6gf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.19742676,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
229,detection.ssdlite320_mobilenet_v3_large,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.112035498,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
230,shufflenet_v2_x0_5,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.149797853,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
231,mobilenet_v3_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,0.18858094,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
232,convnext_base,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.057389308,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
233,video.r2plus1d_18,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,1.0,1x32x32,video classification,Null,TV
234,shufflenet_v2_x2_0,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.170370611,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
235,quantization.mobilenet_v3_large,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.131893493,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
236,vgg16,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.191504294,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
237,vgg13,quantization.resnet50 is the quantized model of resnet50,quantization.resnet50,0.215395682,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
238,regnet_x_16gf,in this section we use the 16 billion flops (16GF) regime,regnet_x_16gf,1.0,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
239,regnet_y_800mf,adopted from BERT,vit_b_16,0.010435705,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
240,detection.ssdlite320_mobilenet_v3_large,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.124473763,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
241,quantization.inception_v3,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.214880266,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
242,resnet34,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.115270042,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
243,alexnet,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.078010573,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
244,efficientnet_b4,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.15963169,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
245,convnext_small,convnext_small is used for image classification,convnext_small,1.0,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
246,resnext101_32x8d,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.104439196,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
247,vgg11_bn,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.149786703,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
248,segmentation.fcn_resnet50,VGG19 has 19.6 billion FLOPs,vgg19,0.032062913,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
249,regnet_x_8gf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.149111321,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
250,densenet161,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.131168676,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
251,quantization.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,0.154575473,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
252,shufflenet_v2_x0_5,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,shufflenet_v2_x0_5,1.0,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
253,mnasnet0_5,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.208508313,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
254,regnet_x_1_6gf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.148984899,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
255,vit_b_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,0.146824129,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
256,video.r3d_18,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.178966471,1x32x32,video classification,Null,TV
257,shufflenet_v2_x1_0,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.235570364,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
258,video.r3d_18,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.134230844,1x32x32,video classification,Null,TV
259,detection.fasterrcnn_mobilenet_v3_large_320_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_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
260,convnext_base,Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone,segmentation.lraspp_mobilenet_v3_large,0.14172994,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
261,convnext_small,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,1.0,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
262,convnext_tiny,The required minimum input size of the model is 17x17,squeezenet1_1,0.174228558,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
263,googlenet,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.087239042,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
264,quantization.shufflenet_v2_x2_0,shufflenet_v2_x2_0 is used for image classification,quantization.shufflenet_v2_x2_0,1.0,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
265,quantization.resnet18,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",quantization.resnet18,1.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
266,shufflenet_v2_x2_0,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.124885961,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
267,efficientnet_b0,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.053541005,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
268,vgg19,spatial padding was used to preserve the spatial resolution of the image,vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
269,detection.maskrcnn_resnet50_fpn,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.104969208,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
270,detection.fasterrcnn_resnet50_fpn,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.071753904,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
271,densenet161,Densenet161 is used for image classification,densenet161,1.0,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
272,segmentation.fcn_resnet101,vit_l_32 is used for image classification,vit_l_32,0.124831351,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
273,densenet201,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,vgg19,0.25490197,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
274,detection.maskrcnn_resnet50_fpn,The required minimum input size of the model is 21x21,squeezenet1_0,0.170311506,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
275,quantization.shufflenet_v2_x1_5,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.167580072,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
276,vit_l_32,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.133622228,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
277,video.r2plus1d_18,MobileNetv3-Large is 27% faster than MobileNetV2 while maintaining similar mAP,mobilenet_v3_large,0.172495079,1x32x32,video classification,Null,TV
278,segmentation.lraspp_mobilenet_v3_large,"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)",vgg19,0.232502468,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
279,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_vgg16,0.097946309,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
280,regnet_x_8gf,The required minimum input size of the model is 29x29,densenet121,0.197648067,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
281,detection.retinanet_resnet50_fpn,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.218064796,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
282,googlenet,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.099927255,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
283,densenet161,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.174362972,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
284,quantization.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,0.153248557,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
285,quantization.mobilenet_v2,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,quantization.mobilenet_v2,1.0,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
286,quantization.inception_v3,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.126172403,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
287,regnet_x_400mf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.163370426,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
288,video.mc3_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,0.17269669,1x32x32,video classification,Null,TV
289,quantization.shufflenet_v2_x0_5,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.184983291,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
290,quantization.googlenet,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.087720835,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
291,convnext_tiny,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.079781257,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
292,efficientnet_b4,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b4,1.0,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
293,regnet_x_1_6gf,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_1_6gf,1.0,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
294,regnet_y_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,0.130848738,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
295,vgg11_bn,Constructs a RegNetX_400MF architecture,regnet_x_400mf,0.19875793,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
296,wide_resnet101_2,adopted from BERT,vit_b_16,0.0,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
297,shufflenet_v2_x1_0,shufflenet_v2_x1_0 is used for image classification,shufflenet_v2_x1_0,1.0,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
298,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,0.176392012,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
299,vgg13_bn,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.144735322,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
300,regnet_x_400mf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.161410217,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
301,vit_b_32,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.099619476,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
302,segmentation.fcn_resnet50,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.185234227,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
303,efficientnet_b6,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.158455927,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
304,regnet_x_1_6gf,The required minimum input size of the model is 17x17,squeezenet1_1,0.197166759,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
305,vit_l_16,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.170676739,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
306,vgg16_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_vgg16,0.149120419,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
307,convnext_large,The number of parameters is 3.4 million,mobilenet_v2,0.081043166,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
308,googlenet,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.141157944,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
309,mnasnet0_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_v3,0.246235296,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
310,resnet152,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.168760757,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
311,detection.ssd300_vgg16,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.113117211,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
312,quantization.shufflenet_v2_x1_5,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.183837474,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
313,efficientnet_b7,The key building block is an Inception Module,inception_v3,0.198654999,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
314,regnet_x_32gf,2D max pooling in between the weight layers,vgg11,0.161790975,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
315,detection.fasterrcnn_mobilenet_v3_large_320_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_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
316,detection.fasterrcnn_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,0.123352826,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
317,video.r2plus1d_18,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.1857921,1x32x32,video classification,Null,TV
318,vit_b_32,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.134634615,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
319,vit_l_16,They stack residual blocks on top of each other to form network: for example  a ResNet-101 has 101 layers using these blocks,resnet101,0.116579662,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
320,regnet_x_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,0.134981645,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
321,quantization.shufflenet_v2_x2_0,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.133496311,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
322,shufflenet_v2_x2_0,The required minimum input size of the model is 32x32,vgg11,0.138398378,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
323,regnet_x_3_2gf,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.051510293,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
324,vgg19,Implemented three fully connected layers from which first two were of size 4096 and after that a layer with 1000 channels for 1000-way ILSVRC classification and the final layer is a softmax function,vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
325,regnet_y_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,0.152039025,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
326,quantization.shufflenet_v2_x1_5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.212838784,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
327,convnext_large,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.12447978,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
328,regnet_x_3_2gf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.184805498,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
329,segmentation.deeplabv3_resnet50,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.103863159,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
330,detection.keypointrcnn_resnet50_fpn,Replacing ReLU with GeLU,convnext_tiny,0.08359437,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
331,vgg13_bn,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.128063208,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
332,shufflenet_v2_x1_5,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.124599823,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
333,densenet201,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.103095266,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
334,squeezenet1_0,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.125021104,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
335,mnasnet1_3,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.215042291,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
336,convnext_small,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.148724371,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
337,segmentation.fcn_resnet50,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.104468552,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
338,resnext101_32x8d,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.122846134,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
339,video.mc3_18,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.152193489,1x32x32,video classification,Null,TV
340,regnet_y_16gf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.140437523,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
341,quantization.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,0.166053278,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
342,regnet_x_8gf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.161564001,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
343,mobilenet_v2,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.179258998,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
344,vit_b_32,The number of parameters is 3.4 million,mobilenet_v2,0.106230308,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
345,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,0.212544737,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
346,segmentation.deeplabv3_resnet101,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,0.204457216,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
347,detection.fasterrcnn_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_32,0.161633266,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
348,detection.keypointrcnn_resnet50_fpn,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.084083561,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
349,convnext_base,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.119460594,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
350,resnext50_32x4d,The required minimum input size of the model is 17x17,squeezenet1_1,0.213268731,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
351,convnext_small,The required minimum input size of the model is 29x29,densenet121,0.163034861,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
352,regnet_x_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,0.182888351,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
353,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_vgg16,0.123023271,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
354,regnet_x_16gf,Constructs a RegNetX_16GF architecture,regnet_x_16gf,1.0,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
355,convnext_base,"It has 11 weight layers in total, so the name VGG11",vgg11,0.211333069,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
356,mobilenet_v2,Note that this model does not include any fine-tuned heads,vit_b_16,0.174432099,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
357,shufflenet_v2_x1_0,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.126637348,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
358,regnet_y_800mf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.153159235,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
359,detection.maskrcnn_resnet50_fpn,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.062323151,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
360,quantization.shufflenet_v2_x1_5,"Training Techniques are Weight Decay, SGD with Momentum",quantization.shufflenet_v2_x1_5,1.0,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
361,vgg19_bn,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.094493536,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
362,regnet_x_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,0.179466614,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
363,quantization.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,vgg16,0.237080216,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
364,resnext101_32x8d,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.234378159,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
365,quantization.shufflenet_v2_x2_0,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.10761188,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
366,efficientnet_b2,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.2001131,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
367,mnasnet0_75,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.075807532,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
368,mobilenet_v3_small,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.110675324,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
369,regnet_y_400mf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.110450356,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
370,convnext_large,"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,0.228721408,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
371,regnet_x_800mf,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.182280382,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
372,convnext_base,"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_v3,0.184145066,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
373,shufflenet_v2_x1_0,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.048324595,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
374,quantization.shufflenet_v2_x1_0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.212831786,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
375,resnet50,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet50,1.0,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
376,regnet_y_800mf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_800mf,1.0,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
377,regnet_y_3_2gf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.205879774,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
378,mobilenet_v3_large,MobileNetv3-Large is 27% faster than MobileNetV2 while maintaining similar mAP,mobilenet_v3_large,1.0,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
379,vgg16_bn,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.172814272,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
380,vit_b_32,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.148990349,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
381,inception_v3,based on ResNeXt-50 as its backbone along with the feature Pyramid Network (FPN) for better accuracy and speed,detection.maskrcnn_resnet50_fpn,0.229323263,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
382,efficientnet_b0,"It has 11 weight layers in total, so the name VGG11",vgg11,0.200492586,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
383,quantization.googlenet,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.148137484,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
384,regnet_y_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_vgg16,0.186580247,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
385,regnet_x_8gf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.163760434,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
386,quantization.shufflenet_v2_x0_5,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.164146756,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
387,regnet_x_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_resnet50,0.153051188,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
388,segmentation.fcn_resnet50,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.087343344,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
389,regnet_y_8gf,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.199624634,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
390,video.mc3_18,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.061913594,1x32x32,video classification,Null,TV
391,densenet161,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.173582853,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
392,mobilenet_v2,The key building block is an Inception Module,inception_v3,0.156915461,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
393,regnet_x_32gf,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.202327394,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
394,detection.ssd300_vgg16,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
395,vit_b_32,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
396,regnet_y_16gf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.165361196,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
397,detection.fasterrcnn_mobilenet_v3_large_320_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_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
398,vgg11_bn,The required minimum input size of the model is 32x32,vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
399,vit_b_16,"Each 2-layer block is replaced in the 34-layer net with this 3-layer bottleneck block, resulting in a 50-layer ResNet",resnet50,0.128652015,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
400,convnext_base,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.168269028,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
401,mnasnet1_3,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.04739341,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
402,segmentation.deeplabv3_resnet101,"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,0.162028864,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
403,regnet_x_1_6gf,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.151221475,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
404,regnet_x_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_16,0.158325188,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
405,video.r2plus1d_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_16,0.17675972,1x32x32,video classification,Null,TV
406,quantization.shufflenet_v2_x2_0,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.223071229,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
407,shufflenet_v2_x1_0,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.110494406,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
408,efficientnet_b7,EfficientNet-B7 only has 66 million parameters,efficientnet_b7,1.0,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
409,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.113018686,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
410,resnext101_32x8d,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.213820208,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
411,vgg11,"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_vgg16,0.151402397,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
412,detection.maskrcnn_resnet50_fpn,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.129311857,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
413,vgg19_bn,"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,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
414,efficientnet_b0,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.162551641,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
415,regnet_x_32gf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.160121576,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
416,squeezenet1_0,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,0.221648682,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
417,vit_b_32,Constructs a RegNetX_1.6GF architecture,regnet_x_1_6gf,0.152847418,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
418,regnet_y_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,0.123015107,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
419,vgg13,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.181093104,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
420,segmentation.fcn_resnet50,The original dataset contained images of size 512x512x1,resnet101,0.088221888,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
421,regnet_x_400mf,"Improved version of DeepLab v2, with optimization of ASPP layer hyper parameters and without a Dense CRF layer, for faster operation",segmentation.deeplabv3_resnet50,0.175312695,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
422,convnext_small,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.126641477,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
423,wide_resnet101_2,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.186982024,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
424,resnet152,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.187789915,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
425,vgg19_bn,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.210950953,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
426,densenet121,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.173638975,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
427,regnet_x_1_6gf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.166235548,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
428,wide_resnet50_2,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.221839664,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
429,vgg16,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.215086771,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
430,regnet_x_400mf,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,1.0,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
431,regnet_y_3_2gf,"The RegNet is composed of a stem, body and head",regnet_y_3_2gf,1.0,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
432,squeezenet1_0,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.171365858,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
433,detection.keypointrcnn_resnet50_fpn,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.07820364,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
434,segmentation.fcn_resnet50,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.090149876,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
435,quantization.resnet50,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.189676475,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
436,convnext_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,0.161414412,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
437,vgg11,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.180210196,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
438,quantization.mobilenet_v2,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.137011605,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
439,regnet_y_800mf,"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_18,0.096279798,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
440,segmentation.fcn_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_32,0.199850076,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
441,resnet152,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.092359776,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
442,quantization.googlenet,It is the simplest of all other vgg versions,vgg11,0.155570023,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
443,resnet152,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.215310227,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
444,regnet_y_400mf,"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,1.0,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
445,quantization.googlenet,adopted from BERT,vit_b_16,0.013219319,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
446,googlenet,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_vgg16,0.181284713,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
447,convnext_base,Constructs a DeepLabV3 model with a MobileNetV3-Large backbone,segmentation.deeplabv3_mobilenet_v3_large,0.144959425,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
448,mnasnet0_5,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.051263887,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
449,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_5,0.133549442,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
450,regnet_x_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,0.152808626,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
451,convnext_tiny,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.069491401,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
452,vgg11,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.175807705,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
453,vit_l_32,quantization.resnet50 is the quantized model of resnet50,quantization.resnet50,0.170097537,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
454,segmentation.fcn_resnet50,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.234695558,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
455,segmentation.lraspp_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_32,0.190165447,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
456,efficientnet_b1,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.220660123,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
457,shufflenet_v2_x1_5,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.171504395,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
458,convnext_large,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.182384753,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
459,quantization.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_16,0.135303852,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
460,detection.keypointrcnn_resnet50_fpn,"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_v3,0.149887642,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
461,mobilenet_v3_small,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.127900892,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
462,detection.ssdlite320_mobilenet_v3_large,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.06459688,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
463,wide_resnet101_2,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.140127753,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
464,resnet34,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.055672565,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
465,mnasnet1_3,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.221442096,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
466,convnext_tiny,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.054233636,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
467,convnext_tiny,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,0.093974553,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
468,googlenet,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.176068977,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
469,detection.fasterrcnn_resnet50_fpn,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.082175748,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
470,resnet34,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.147268342,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
471,convnext_tiny,They stack residual blocks ontop of each other to form network,vgg19_bn,0.092554493,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
472,vgg16,"SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy",squeezenet1_1,0.186344674,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
473,quantization.resnet18,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.190551333,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
474,mobilenet_v3_large,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.188489974,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
475,regnet_y_800mf,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.160742978,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
476,resnext101_32x8d,adopted from BERT,vit_b_16,0.002349811,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
477,detection.fasterrcnn_mobilenet_v3_large_fpn,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.18073914,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
478,alexnet,"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_vgg16,0.187608272,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
479,convnext_base,The required minimum input size of the model is 32x32,vgg11,0.126664409,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
480,vit_l_16,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.111992605,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
481,regnet_y_32gf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.102831046,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
482,squeezenet1_0,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.163840308,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
483,quantization.mobilenet_v2,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.11768177,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
484,vgg11,Not all the convolutional layers are followed by max-pooling layers,vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
485,densenet201,The densenet-201 is larger at over 77MB in size vs the densenet-121 model's roughly 31MB size,densenet201,1.0,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
486,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_vgg16,0.162739205,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
487,regnet_x_32gf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.167797338,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
488,googlenet,ReLU non-linearity as activation functions,vgg11,0.134197447,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
489,video.r2plus1d_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_0,0.213440369,1x32x32,video classification,Null,TV
490,segmentation.lraspp_mobilenet_v3_large,"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,0.190524669,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
491,regnet_y_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_vgg16,0.099313627,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
492,regnet_y_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,vgg16,0.144219937,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
493,segmentation.deeplabv3_resnet101,vit_b_32 is used for image classification,vit_b_32,0.135790589,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
494,shufflenet_v2_x1_0,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16,0.227711368,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
495,quantization.shufflenet_v2_x1_0,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.157906356,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
496,regnet_x_400mf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.197837484,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
497,resnet18,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.037626739,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
498,segmentation.fcn_resnet101,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.195196828,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
499,regnet_x_16gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.169248143,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
500,video.r3d_18,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,1.0,1x32x32,video classification,Null,TV
501,detection.fasterrcnn_mobilenet_v3_large_320_fpn,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.174068554,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
502,convnext_tiny,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.143575614,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
503,convnext_small,obtained by scaling up the baseline network,efficientnet_b1,0.145935159,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
504,regnet_x_8gf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.154283054,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
505,quantization.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,0.218569773,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
506,shufflenet_v2_x1_5,Note that this model does not include any fine-tuned heads,vit_b_16,0.220318718,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
507,mnasnet1_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_vgg16,0.123760337,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
508,vgg13,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.131391197,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
509,quantization.resnet50,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.175117431,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
510,segmentation.deeplabv3_mobilenet_v3_large,This model has 3.8 billion FLOPs,resnet50,0.137211181,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
511,detection.retinanet_resnet50_fpn,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.073552576,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
512,convnext_large,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.17280956,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
513,regnet_y_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_resnet50,0.150933099,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
514,efficientnet_b7,"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,0.232373325,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
515,quantization.resnet50,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",quantization.resnet50,1.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
516,resnext50_32x4d,"Each region is then mapped to each reference anchor box, and thus detecting objects at different scales and aspect ratios",detection.fasterrcnn_resnet50_fpn,0.222815566,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
517,quantization.resnext101_32x8d,"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_vgg16,0.170060638,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
518,convnext_base,"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_0,0.123143599,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
519,wide_resnet50_2,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.174491419,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
520,resnet50,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.182268296,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
521,convnext_base,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.15880574,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
522,inception_v3,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.193740984,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
523,convnext_tiny,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.149090254,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
524,regnet_y_1_6gf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.158138091,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
525,efficientnet_b2,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.252781022,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
526,convnext_large,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,0.188214964,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
527,resnext101_32x8d,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.129008299,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
528,regnet_x_400mf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.1316344,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
529,efficientnet_b1,"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_16,0.185675755,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
530,mnasnet1_3,"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_3,1.0,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
531,video.r2plus1d_18,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.106859704,1x32x32,video classification,Null,TV
532,quantization.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_vgg16,0.175189682,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
533,wide_resnet101_2,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.184292865,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
534,resnext101_32x8d,VGG19 has 19.6 billion FLOPs,vgg19,0.123656504,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
535,alexnet,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.227811828,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
536,regnet_y_800mf,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.048180702,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
537,convnext_tiny,Constructs a RegNetY_3.2GF architecture,regnet_y_3_2gf,0.183655183,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
538,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,0.102782327,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
539,convnext_tiny,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.136156492,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
540,quantization.googlenet,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.111176133,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
541,mobilenet_v3_small,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.083191759,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
542,regnet_y_800mf,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.164175472,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
543,segmentation.deeplabv3_mobilenet_v3_large,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.096706241,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
544,googlenet,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.176068977,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
545,regnet_x_16gf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.175623445,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
546,regnet_x_32gf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.138182624,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
547,vgg16,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.181131759,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
548,efficientnet_b4,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.162184343,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
549,regnet_y_16gf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.189108723,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
550,regnet_x_3_2gf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.162043884,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
551,vit_l_16,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.125637699,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
552,squeezenet1_1,"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_vgg16,0.187498162,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
553,regnet_y_8gf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_8gf,1.0,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
554,convnext_tiny,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,1.0,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
555,detection.ssdlite320_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_5,0.142634261,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
556,resnet18,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.212426199,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
557,mnasnet0_75,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.204655165,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
558,efficientnet_b4,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.194603476,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
559,detection.fasterrcnn_resnet50_fpn,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.208603967,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
560,vgg13,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.189560881,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
561,vgg19,"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,0.144175955,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
562,quantization.mobilenet_v3_large,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.235036079,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
563,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.184684222,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
564,densenet121,"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",densenet121,1.0,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
565,regnet_y_800mf,It is the simplest of all other vgg versions,vgg11,0.179691353,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
566,wide_resnet101_2,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.109344097,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
567,vgg13,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.137587486,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
568,vgg11,The convolutional layers will have a 3x3 kernel size with a stride of 1 and padding of 1,vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
569,detection.keypointrcnn_resnet50_fpn,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,1.0,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
570,vit_b_16,"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_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
571,vgg16_bn,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.221558546,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
572,detection.ssdlite320_mobilenet_v3_large,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.188074625,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
573,detection.fasterrcnn_mobilenet_v3_large_320_fpn,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
574,convnext_small,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,0.132321774,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
575,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,0.140132753,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
576,regnet_x_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_resnet50,0.150164362,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
577,inception_v3,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.168518223,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
578,wide_resnet101_2,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.194153147,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
579,densenet161,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.204921006,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
580,efficientnet_b7,Replacing ReLU with GeLU,convnext_tiny,0.090357297,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
581,regnet_x_32gf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_x_32gf,1.0,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
582,detection.retinanet_resnet50_fpn,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.15675682,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
583,densenet201,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.140953407,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
584,inception_v3,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.205986566,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
585,mobilenet_v2,"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,0.153042622,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
586,segmentation.deeplabv3_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,0.160311849,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
587,convnext_large,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.102985174,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
588,convnext_small,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.083923391,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
589,regnet_y_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,0.114827928,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
590,regnet_y_16gf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.131583104,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
591,shufflenet_v2_x1_0,The required minimum input size of the model is 32x32,vgg11,0.138665429,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
592,regnet_y_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_resnet50,0.13600424,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
593,regnet_y_8gf,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_8gf,1.0,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
594,quantization.shufflenet_v2_x2_0,"It has 11 weight layers in total, so the name VGG11",vgg11,0.175917022,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
595,wide_resnet101_2,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.109008383,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
596,squeezenet1_0,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.122245704,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
597,vit_b_16,Constructs a RegNetX_1.6GF architecture,regnet_x_1_6gf,0.173867859,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
598,segmentation.fcn_resnet50,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.201907431,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
599,quantization.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_16,0.185787623,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
600,segmentation.deeplabv3_resnet101,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x0_5,0.138225887,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
601,segmentation.deeplabv3_mobilenet_v3_large,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.21418474,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
602,shufflenet_v2_x1_0,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.136004253,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
603,resnet101,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.135652518,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
604,vit_b_16,"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_vgg16,0.135206292,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
605,wide_resnet101_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,0.18755799,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
606,densenet121,Densenet121 is used for image classification,densenet121,1.0,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
607,quantization.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,0.194483339,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
608,video.mc3_18,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.036162645,1x32x32,video classification,Null,TV
609,quantization.mobilenet_v3_large,This is achieved through the use of wide residual blocks,wide_resnet50_2,0.179616155,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
610,quantization.inception_v3,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.115373895,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
611,resnext50_32x4d,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.235701563,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
612,mnasnet0_5,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.19633464,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
613,video.r3d_18,Network architecture of the 3D ResNet,video.r3d_18,1.0,1x32x32,video classification,Null,TV
614,regnet_y_16gf,This improves the speed and efficiency for object detection,detection.maskrcnn_resnet50_fpn,0.137361403,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
615,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.204778345,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
616,efficientnet_b7,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.136024726,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
617,resnet50,"VGG13 has the same architecture as VGG11, but with a different network size",vgg13,0.240977058,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
618,shufflenet_v2_x2_0,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.121225712,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
619,regnet_y_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_18,0.116299242,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
620,regnet_y_8gf,regnet_y_8gf is used for image classification,regnet_y_8gf,1.0,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
621,vgg16,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.106621927,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
622,resnext50_32x4d,"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_0,0.251883872,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
623,efficientnet_b6,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,0.193583624,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
624,efficientnet_b6,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.219469324,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
625,mnasnet0_75,"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,0.136500202,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
626,resnet152,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.220208004,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
627,detection.ssdlite320_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,1.0,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
628,convnext_tiny,They stack residual blocks on top of each other to form network: for example  a ResNet-101 has 101 layers using these blocks,resnet101,0.155477224,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
629,convnext_small,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.129214032,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
630,vit_b_32,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.115325826,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
631,vgg16_bn,In the end it has 2 FC (fully connected layers) followed by a softmax for output,vgg16_bn,1.0,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
632,mobilenet_v3_large,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.197265029,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
633,convnext_large,"Each 2-layer block is replaced in the 34-layer net with this 3-layer bottleneck block, resulting in a 50-layer ResNet",resnet50,0.181051846,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
634,vit_b_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,0.214224899,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
635,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.16479755,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
636,regnet_x_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_vgg16,0.147205958,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
637,alexnet,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.158990713,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
638,vgg11_bn,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.091282651,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
639,regnet_y_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,0.164937205,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
640,segmentation.fcn_resnet101,"Used kernels of (3 * 3) size with a stride size of 1 pixel, this enabled them to cover the whole notion of the image",vgg19,0.176479896,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
641,mnasnet0_75,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.148306447,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
642,convnext_base,"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_resnet50,0.174300985,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
643,segmentation.deeplabv3_resnet101,VGG19 has 19.6 billion FLOPs,vgg19,0.101119647,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
644,regnet_x_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,0.140551039,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
645,vit_l_16,Constructs a RegNetY_3.2GF architecture,regnet_y_3_2gf,0.171068444,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
646,quantization.inception_v3,based on ResNeXt-50 as its backbone along with the feature Pyramid Network (FPN) for better accuracy and speed,detection.maskrcnn_resnet50_fpn,0.212350076,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
647,efficientnet_b3,"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_vgg16,0.124940939,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
648,resnet18,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.148133823,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
649,mobilenet_v2,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.173196683,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
650,mobilenet_v3_large,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.137401129,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
651,segmentation.lraspp_mobilenet_v3_large,"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_18,0.22637133,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
652,efficientnet_b3,The key building block is an Inception Module,inception_v3,0.174484283,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
653,convnext_large,Note that this model does not include any fine-tuned heads,vit_b_16,0.226197709,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
654,vit_l_16,Constructs a Faster R-CNN model with a ResNet-50-FPN backbone,detection.fasterrcnn_resnet50_fpn,0.188099289,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
655,regnet_x_8gf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.11838825,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
656,vit_b_32,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.127535551,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
657,mnasnet0_5,"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_18,0.232969155,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
658,segmentation.lraspp_mobilenet_v3_large,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.188752592,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
659,shufflenet_v2_x2_0,The main building block is a ShuffleNet v2 Block,shufflenet_v2_x2_0,1.0,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
660,video.r2plus1d_18,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.110045522,1x32x32,video classification,Null,TV
661,vgg16,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,0.190859669,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
662,densenet161,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.157514444,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
663,efficientnet_b1,adopted from BERT,vit_b_16,0.0,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
664,vgg19_bn,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_bn,1.0,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
665,convnext_tiny,"Each layer from the FPN is passed to the subnets, enabling it to detect objects as various scales",detection.retinanet_resnet50_fpn,0.198469494,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
666,resnext50_32x4d,The required minimum input size of the model is 32x32,vgg11,0.180020649,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
667,regnet_y_32gf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.162797691,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
668,convnext_base,"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,0.150698494,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
669,efficientnet_b5,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.226078197,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
670,convnext_tiny,"1x1 Convolution, Average Pooling, Convolution, Dense Connections, Dropout, Inverted Residual Block, Batch Normalization, Squeeze-and-Excitation Block, Swish",efficientnet_b0,0.185521002,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
671,densenet169,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.198616871,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
672,regnet_y_16gf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.186580339,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
673,detection.fasterrcnn_resnet50_fpn,Replacing ReLU with GeLU,convnext_tiny,0.138080739,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
674,efficientnet_b5,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.118200363,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
675,quantization.shufflenet_v2_x0_5,Constructs a ShuffleNetV2 with 0.5x output channels,quantization.shufflenet_v2_x0_5,1.0,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
676,quantization.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_vgg16,0.148241492,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
677,regnet_x_800mf,"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_18,0.08755233,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
678,detection.fasterrcnn_mobilenet_v3_large_fpn,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.120843702,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
679,vit_l_16,Wide ResNet-50-2 Wide Residual Networks,wide_resnet50_2,0.11844512,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
680,video.r3d_18,ResNet 3D is a type of model for video that employs 3D convolutions,video.r3d_18,1.0,1x32x32,video classification,Null,TV
681,segmentation.deeplabv3_resnet50,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,1.0,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
682,detection.ssd300_vgg16,The number of parameters is 3.4 million,mobilenet_v2,0.079254556,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
683,detection.ssdlite320_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_vgg16,0.131240351,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
684,detection.retinanet_resnet50_fpn,"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_vgg16,0.191911602,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
685,vgg11_bn,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.141183891,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
686,regnet_y_32gf,2D max pooling in between the weight layers,vgg11,0.168257393,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
687,vgg11,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.148414072,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
688,regnet_y_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,0.155281879,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
689,regnet_x_16gf,"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_18,0.16519354,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
690,segmentation.lraspp_mobilenet_v3_large,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.185203978,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
691,segmentation.deeplabv3_resnet101,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.185459409,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
692,regnet_x_1_6gf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.131900587,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
693,efficientnet_b6,Replacing ReLU with GeLU,convnext_tiny,0.09696005,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
694,detection.fasterrcnn_mobilenet_v3_large_320_fpn,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.158232578,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
695,vit_b_32,MobileNetV3 is a convolutional neural network that is designed for mobile phone CPUs,mobilenet_v3_small,0.113949801,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
696,vgg16_bn,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.161832506,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
697,regnet_x_800mf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.169345662,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
698,regnet_y_1_6gf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.168436237,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
699,segmentation.fcn_resnet50,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.154328585,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
700,detection.ssd300_vgg16,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.091005899,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
701,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.216429047,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
702,quantization.mobilenet_v2,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.197654033,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
703,quantization.inception_v3,It works similarly to Faster R-CNN with ResNet-50 FPN backbone,detection.fasterrcnn_mobilenet_v3_large_fpn,0.172046002,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
704,detection.fasterrcnn_mobilenet_v3_large_320_fpn,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.1624045,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
705,wide_resnet50_2,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.155387662,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
706,mnasnet1_0,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,1.0,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
707,regnet_x_3_2gf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.064991487,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
708,vgg13,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.075436692,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
709,regnet_y_16gf,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_16gf,1.0,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
710,detection.retinanet_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,0.149468018,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
711,densenet201,adopted from BERT,vit_b_16,0.0,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
712,mnasnet0_75,MNASNet with depth multiplier of 0.75,mnasnet0_75,1.0,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
713,segmentation.deeplabv3_resnet50,vit_l_16 is used for image classification,vit_l_16,0.146565612,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
714,regnet_x_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,0.11700659,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
715,quantization.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,0.117761996,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
716,mobilenet_v3_large,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.097132109,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
717,segmentation.lraspp_mobilenet_v3_large,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.205544707,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
718,regnet_x_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,0.181127213,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
719,vgg13,"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_5,0.136999168,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
720,efficientnet_b6,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.17915161,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
721,inception_v3,quantization.resnet18 is the quantized model of resnet18,quantization.resnet18,0.180072236,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
722,shufflenet_v2_x1_5,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.088325863,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
723,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 method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.195059655,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
724,quantization.inception_v3,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.073259274,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
725,segmentation.lraspp_mobilenet_v3_large,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.149777658,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
726,convnext_small,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.168267857,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
727,squeezenet1_0,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.159362878,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
728,resnet34,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.16963989,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
729,vit_l_16,"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_vgg16,0.132871178,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
730,vgg16_bn,Constructs a RegNetX_3.2GF architecture,regnet_x_3_2gf,0.227383594,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
731,alexnet,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.154720966,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
732,segmentation.fcn_resnet50,vit_b_16 is used for image classification,vit_b_16,0.1405755,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
733,densenet169,The required minimum input size of the model is 29x29,densenet169,1.0,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
734,resnext101_32x8d,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.120591586,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
735,mnasnet0_75,The number of parameters is 3.4 million,mobilenet_v2,0.103380007,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
736,vit_b_16,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.140433279,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
737,vit_b_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_18,0.080691348,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
738,regnet_y_400mf,It is the simplest of all other vgg versions,vgg11,0.176928674,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
739,quantization.shufflenet_v2_x2_0,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.133823718,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
740,convnext_small,"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,0.171825497,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
741,vgg13,Constructs a ShuffleNetV2 with 1.5x output channels,shufflenet_v2_x1_5,0.169484308,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
742,wide_resnet50_2,Shallower network means: Number of layers can be reduced and Training time can be shorter as well,wide_resnet50_2,1.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
743,regnet_x_8gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.176452022,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
744,convnext_tiny,convnext_tiny is used for image classification,convnext_tiny,1.0,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
745,detection.retinanet_resnet50_fpn,"Dense blocks, Batch Normalization(BN)",densenet121,0.118939526,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
746,segmentation.deeplabv3_resnet101,"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,0.160481691,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
747,resnet152,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet152,1.0,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
748,quantization.resnext101_32x8d,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.113506349,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
749,segmentation.deeplabv3_mobilenet_v3_large,The required minimum input size of the model is 32x32,vgg11,0.184127662,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
750,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,0.127140967,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
751,resnext50_32x4d,This model has 3.8 billion FLOPs,resnet50,0.123569373,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
752,mobilenet_v3_large,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.230230254,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
753,mnasnet0_75,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.112155003,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
754,efficientnet_b2,The key building block is an Inception Module,inception_v3,0.183471237,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
755,convnext_base,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.154850525,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
756,vit_b_16,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
757,detection.maskrcnn_resnet50_fpn,Constructs a Mask R-CNN model with a ResNet-50-FPN backbone,detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
758,regnet_y_3_2gf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.156444243,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
759,quantization.resnet50,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.15533819,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
760,convnext_large,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.095971118,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
761,vgg19,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.129294163,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
762,regnet_x_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,0.186414153,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
763,regnet_y_8gf,adopted from BERT,vit_b_16,0.017690685,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
764,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 method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.188662119,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
765,googlenet,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.190007083,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
766,quantization.shufflenet_v2_x1_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_vgg16,0.086559085,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
767,segmentation.deeplabv3_resnet50,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.095925027,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
768,convnext_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_18,0.181658196,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
769,convnext_small,They stack residual blocks on top of each other to form network: for example  a ResNet-50 has fifty layers using these blocks,resnet50,0.155077455,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
770,alexnet,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.148538079,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
771,vit_b_32,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.192441924,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
772,quantization.inception_v3,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.11685408,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
773,resnet18,There are 4 convolutional layers in each module (excluding the 1x1 convolutional layer),resnet18,1.0,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
774,detection.fasterrcnn_mobilenet_v3_large_320_fpn,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.166357667,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
775,regnet_y_16gf,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.209059938,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
776,quantization.resnet18,VGG19 has 19.6 billion FLOPs,vgg19,0.116979739,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
777,regnet_x_16gf,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.114479926,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
778,inception_v3,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.094178249,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
779,detection.ssdlite320_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,0.114229696,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
780,mobilenet_v3_large,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.153542754,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
781,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"This model, called SSDLite, claimed to have 8x fewer parameters than the original SSD while achieving competitive accuracy",detection.ssdlite320_mobilenet_v3_large,0.119183577,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
782,resnet152,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.05669172,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
783,squeezenet1_1,"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_32,0.197082892,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
784,segmentation.fcn_resnet50,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.032023777,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
785,quantization.inception_v3,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.13356941,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
786,detection.keypointrcnn_resnet50_fpn,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.138358454,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
787,vit_l_32,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_400mf,0.121626204,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
788,squeezenet1_0,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.134868268,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
789,googlenet,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.20220153,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
790,vit_l_16,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
791,detection.fasterrcnn_mobilenet_v3_large_fpn,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.155370694,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
792,detection.ssdlite320_mobilenet_v3_large,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.174657831,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
793,detection.ssd300_vgg16,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.118507375,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
794,detection.keypointrcnn_resnet50_fpn,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.209558393,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
795,convnext_small,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.0428928,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
796,video.r2plus1d_18,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.057129275,1x32x32,video classification,Null,TV
797,efficientnet_b3,"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_vgg16,0.12752828,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
798,quantization.shufflenet_v2_x1_0,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.182210717,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
799,quantization.resnet18,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.126901397,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
800,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,0.197839465,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
801,video.r2plus1d_18,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.088291662,1x32x32,video classification,Null,TV
802,detection.fasterrcnn_mobilenet_v3_large_fpn,This model has 3.8 billion FLOPs,resnet50,0.159105216,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
803,efficientnet_b4,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.191921112,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
804,convnext_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_vgg16,0.090948937,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
805,shufflenet_v2_x1_5,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.153862598,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
806,video.r3d_18,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.215682455,1x32x32,video classification,Null,TV
807,quantization.resnet50,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.172683489,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
808,detection.keypointrcnn_resnet50_fpn,The required minimum input size of the model is 29x29,densenet121,0.201255942,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
809,convnext_tiny,"Mask R-CNN uses anchor boxes to detect multiple objects, objects of different scales, and overlapping objects in an image",detection.maskrcnn_resnet50_fpn,0.190838309,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
810,segmentation.deeplabv3_resnet50,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.110377172,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
811,resnet101,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.176796889,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
812,vit_b_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_b0,0.185040257,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
813,mnasnet1_3,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.249716192,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
814,densenet201,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.195253763,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
815,mnasnet0_75,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.051402049,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
816,regnet_y_3_2gf,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,1.0,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
817,vit_b_32,"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,0.08770498,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
818,detection.fasterrcnn_mobilenet_v3_large_fpn,"fasterrcnn_mobilenet_v3_large_fpn is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
819,resnet18,The required minimum input size of the model is 17x17,squeezenet1_1,0.220470104,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
820,densenet169,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.172006914,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
821,detection.fasterrcnn_resnet50_fpn,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.070861633,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
822,quantization.shufflenet_v2_x0_5,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.162930708,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
823,quantization.shufflenet_v2_x1_5,VGG19 has 19.6 billion FLOPs,vgg19,0.112391039,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
824,vit_l_16,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.13819247,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
825,vit_b_32,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.104965812,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
826,shufflenet_v2_x1_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_vgg16,0.100459432,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
827,detection.keypointrcnn_resnet50_fpn,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.136411238,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
828,convnext_base,ResNet-152 can obtain 4.49% error rate,resnet152,0.2010599,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
829,quantization.shufflenet_v2_x1_0,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.229505171,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
830,regnet_x_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_18,0.116588823,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
831,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,0.203267597,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
832,regnet_y_16gf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.194065948,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
833,detection.fasterrcnn_mobilenet_v3_large_320_fpn,It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position,detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
834,regnet_x_1_6gf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.130017086,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
835,detection.ssd300_vgg16,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.08669756,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
836,regnet_y_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_18,0.167055734,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
837,efficientnet_b3,"It has 11 weight layers in total, so the name VGG11",vgg11,0.224684417,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
838,mobilenet_v3_small,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.075393283,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
839,quantization.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,0.19750409,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
840,shufflenet_v2_x2_0,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.111135152,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
841,resnext50_32x4d,"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_vgg16,0.171532245,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
842,regnet_x_1_6gf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.16013469,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
843,shufflenet_v2_x1_5,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.106738965,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
844,regnet_y_800mf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.189796771,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
845,densenet169,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.175716969,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
846,quantization.mobilenet_v2,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.132615225,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
847,detection.maskrcnn_resnet50_fpn,This model has 3.8 billion FLOPs,resnet50,0.141886958,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
848,convnext_tiny,obtained by scaling up the baseline network,efficientnet_b1,0.142971878,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
849,mnasnet0_75,The architecture of Keypoint RCNN resembles the Mask-RCNN,detection.keypointrcnn_resnet50_fpn,0.250891283,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
850,segmentation.lraspp_mobilenet_v3_large,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.154387245,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
851,detection.fasterrcnn_mobilenet_v3_large_320_fpn,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.134430537,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
852,regnet_x_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_16,0.152376143,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
853,shufflenet_v2_x2_0,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.174264307,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
854,quantization.inception_v3,"The number of channels in outer 1x1 convolutions is the same, Wide ResNet-50-2 has 2048-1024-2048 channels",wide_resnet50_2,0.117525891,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
855,efficientnet_b7,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.138447601,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
856,resnet34,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.177671464,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
857,quantization.inception_v3,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.174312339,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
858,regnet_x_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,0.144195281,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
859,video.r2plus1d_18,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.083462014,1x32x32,video classification,Null,TV
860,regnet_x_800mf,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.160255496,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
861,resnet101,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet101,1.0,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
862,quantization.resnet18,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.206442289,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
863,segmentation.deeplabv3_mobilenet_v3_large,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.178282288,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
864,regnet_y_16gf,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.166766777,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
865,vit_b_16,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.061792516,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
866,regnet_y_3_2gf,The required minimum input size of the model is 29x29,densenet121,0.174765784,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
867,resnet18,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.033816743,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
868,resnet34,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.138847191,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
869,efficientnet_b4,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.147001396,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
870,mobilenet_v3_large,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.155789056,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
871,regnet_x_400mf,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_x_400mf,1.0,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
872,densenet121,"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_vgg16,0.159009064,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
873,regnet_x_800mf,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.06389081,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
874,quantization.inception_v3,Relative fast due to no fully connected layers or a refinement block,segmentation.fcn_resnet50,0.136683929,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
875,squeezenet1_1,Replacing ReLU with GeLU,convnext_tiny,0.074189787,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
876,detection.ssdlite320_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,1.0,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
877,mnasnet0_75,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.098149544,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
878,resnet34,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.108736999,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
879,wide_resnet101_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,0.21547814,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
880,shufflenet_v2_x2_0,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.125832077,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
881,resnext50_32x4d,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.122598207,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
882,convnext_large,Constructs a ShuffleNetV2 with 2.0x output channels,shufflenet_v2_x2_0,0.109536073,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
883,convnext_small,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.197284239,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
884,quantization.mobilenet_v3_large,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.082124691,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
885,resnet18,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.182065949,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
886,segmentation.fcn_resnet101,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.0262261,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
887,efficientnet_b2,"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_18,0.180283549,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
888,shufflenet_v2_x1_5,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.150160776,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
889,vit_b_16,EfficientNet-B7 only has 66 million parameters,efficientnet_b7,0.133941354,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
890,regnet_x_32gf,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.178147862,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
891,mnasnet1_0,"It has 11 weight layers in total, so the name VGG11",vgg11,0.222133818,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
892,segmentation.lraspp_mobilenet_v3_large,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.19903363,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
893,quantization.resnet50,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.resnet50,1.0,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
894,quantization.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,0.155179964,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
895,resnext50_32x4d,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.138191333,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
896,resnext50_32x4d,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.126643348,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
897,densenet169,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.147427374,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
898,efficientnet_b5,It is the simplest of all other vgg versions,vgg11,0.171925141,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
899,vit_b_16,"The number of channels in outer 1x1 convolutions is the same, Wide ResNet-50-2 has 2048-1024-2048 channels",wide_resnet50_2,0.122856962,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
900,resnet18,"Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping",resnet18,1.0,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
901,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,0.140244792,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
902,mnasnet0_75,ReLU non-linearity as activation functions,vgg11,0.084064325,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
903,shufflenet_v2_x1_0,"It builds upon ShuffleNet v1, which utilised pointwise group convolutions, bottleneck-like structures, and a channel shuffle operation",shufflenet_v2_x1_0,1.0,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
904,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_vgg16,0.218614131,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
905,squeezenet1_1,"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_16,0.174662265,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
906,detection.keypointrcnn_resnet50_fpn,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,1.0,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
907,vit_b_16,Constructor for 18 layer Mixed Convolution network,video.r3d_18,0.166210668,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
908,efficientnet_b6,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.155277316,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
909,detection.fasterrcnn_resnet50_fpn,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.184933878,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
910,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 self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels",vit_b_16,0.165775524,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
911,mnasnet1_3,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.212046241,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
912,detection.ssdlite320_mobilenet_v3_large,2D max pooling in between the weight layers,vgg11,0.1002942,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
913,vgg13,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.139384348,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
914,segmentation.fcn_resnet50,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.027483146,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
915,squeezenet1_0,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.14109715,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
916,segmentation.fcn_resnet50,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.246939433,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
917,vit_l_32,"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_vgg16,0.091103197,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
918,squeezenet1_1,"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,0.221686686,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
919,vgg11,"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_vgg16,0.130664749,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
920,regnet_x_32gf,adopted from BERT,vit_b_16,0.012105761,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
921,vit_b_32,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_32,1.0,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
922,segmentation.fcn_resnet101,vit_b_16 is used for image classification,vit_b_16,0.143205043,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
923,quantization.resnext101_32x8d,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.181774509,224,classification,0b7c9de0-251d-4572-a98e-7715f7cfd635___quantization.resnext101_32x8d,TV
924,resnext50_32x4d,Note that this model does not include any fine-tuned heads,vit_b_16,0.152073139,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
925,densenet201,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.208542864,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
926,densenet169,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.114172052,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
927,vgg16,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.143643632,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
928,vgg13,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.156086538,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
929,segmentation.deeplabv3_resnet101,The required minimum input size of the model is 17x17,squeezenet1_1,0.19772286,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
930,regnet_y_8gf,"Fully convolutional encoder structure ( , no fully connected layers) with skip connections that fuse multiscale activations at the final decision layer",segmentation.fcn_resnet50,0.185620485,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
931,detection.fasterrcnn_mobilenet_v3_large_320_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_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
932,squeezenet1_1,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.15515109,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
933,convnext_small,"Dense blocks, Batch Normalization(BN)",densenet121,0.099115314,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
934,regnet_y_800mf,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.155654979,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
935,convnext_tiny,This network is a pretty large network and it has about 138 million parameters,vgg16,0.117386861,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
936,convnext_base,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.179009071,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
937,regnet_x_1_6gf,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.172730588,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
938,mnasnet0_75,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.103572723,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
939,alexnet,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.071372241,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
940,mnasnet0_5,"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_0,0.142266522,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
941,vit_l_16,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.11620181,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
942,resnet152,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.163898255,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
943,regnet_x_32gf,ReLU non-linearity as activation functions,vgg11,0.149241789,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
944,vit_b_16,MobileNetV3 is a convolutional neural network that is designed for mobile phone CPUs,mobilenet_v3_small,0.150085822,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
945,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_vgg16,0.196807854,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
946,video.mc3_18,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.14094499,1x32x32,video classification,Null,TV
947,squeezenet1_0,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.145551586,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
948,vgg13_bn,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.164712329,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
949,efficientnet_b5,The number of parameters is 3.4 million,mobilenet_v2,0.149660073,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
950,mobilenet_v3_small,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.140356489,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
951,convnext_large,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.139101069,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
952,video.r3d_18,ssd is a method for detecting objects in images using a single deep neural network,detection.ssd300_vgg16,0.173645591,1x32x32,video classification,Null,TV
953,regnet_x_16gf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.141564726,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
954,shufflenet_v2_x1_0,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.12112557,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
955,vgg11_bn,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,1.0,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
956,segmentation.deeplabv3_resnet50,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.080500326,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
957,mnasnet0_5,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.198124369,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
958,inception_v3,Avoiding the use of dense layers means less parameters (making the networks faster to train),segmentation.fcn_resnet50,0.148459539,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
959,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.212317988,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
960,mobilenet_v2,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.119166189,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
961,densenet121,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.224098717,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
962,resnext50_32x4d,It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position,detection.fasterrcnn_resnet50_fpn,0.203804404,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
963,quantization.resnet18,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.179896779,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
964,shufflenet_v2_x2_0,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.120563525,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
965,resnet152,resnet152 is used for image classification,resnet152,1.0,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
966,wide_resnet101_2,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.098537857,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
967,quantization.inception_v3,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.120081565,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
968,mnasnet1_3,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.140848162,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
969,detection.retinanet_resnet50_fpn,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.089030522,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
970,mnasnet0_75,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,vgg19,0.23212411,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
971,quantization.inception_v3,MNASNet with depth multiplier of 0.75,mnasnet0_75,0.244242266,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
972,vgg16,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.166257687,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
973,regnet_y_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_18,0.128056528,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
974,shufflenet_v2_x1_0,Note that this model does not include any fine-tuned heads,vit_b_16,0.217058606,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
975,regnet_x_16gf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.092052258,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
976,regnet_x_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,0.147137935,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
977,wide_resnet101_2,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.156385344,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
978,resnext101_32x8d,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.162860194,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
979,vgg11,The key building block is an Inception Module,inception_v3,0.12988033,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
980,convnext_base,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.1481133,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
981,regnet_x_32gf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.152816273,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
982,densenet169,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.155573796,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
983,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,0.120404171,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
984,vgg13_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_vgg16,0.213038539,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
985,squeezenet1_1,VGG19 has 19.6 billion FLOPs,vgg19,0.107837614,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
986,googlenet,One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks,vit_b_16,0.244869401,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
987,segmentation.deeplabv3_mobilenet_v3_large,The required minimum input size of the model is 29x29,densenet121,0.209102599,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
988,densenet161,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.191692799,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
989,efficientnet_b4,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.144218983,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
990,vgg11_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,0.15141054,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
991,segmentation.fcn_resnet50,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.175299938,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
992,mnasnet0_5,The required minimum input size of the model is 17x17,squeezenet1_1,0.226446693,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
993,squeezenet1_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_vgg16,0.077012599,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
994,detection.ssdlite320_mobilenet_v3_large,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.156114328,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
995,segmentation.deeplabv3_mobilenet_v3_large,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.098811133,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
996,regnet_x_1_6gf,"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_0,0.204442011,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
997,quantization.mobilenet_v3_large,11 weight layers (convolutional + fully connected),vgg11,0.208638434,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
998,regnet_y_32gf,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.09597557,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
999,densenet201,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.150844907,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1000,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_vgg16,0.10910997,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1001,segmentation.fcn_resnet101,They use option 2 for increasing dimensions,resnet50,0.223368205,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1002,mnasnet0_75,It is the same as vgg11 but it also contains a batch normalization layer,vgg11_bn,0.228827144,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1003,squeezenet1_1,b The architecture of a residual group composed of 2 residual blocks,video.r3d_18,0.191211482,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1004,video.mc3_18,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.mc3_18,1.0,1x32x32,video classification,Null,TV
1005,segmentation.fcn_resnet101,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet101,1.0,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1006,quantization.mobilenet_v3_large,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.133005126,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1007,regnet_y_1_6gf,Constructs a RegNetY_1.6GF architecture,regnet_y_1_6gf,1.0,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1008,segmentation.deeplabv3_mobilenet_v3_large,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_mobilenet_v3_large,1.0,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1009,regnet_y_16gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.174364583,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1010,vgg16,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.158941293,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1011,convnext_base,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_base,1.0,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1012,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,0.176335581,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1013,shufflenet_v2_x0_5,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.170043504,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1014,segmentation.fcn_resnet101,This model has 3.8 billion FLOPs,resnet50,0.042899979,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1015,shufflenet_v2_x1_0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.204051873,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1016,convnext_tiny,"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_18,0.168595318,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1017,video.r3d_18,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.139281962,1x32x32,video classification,Null,TV
1018,densenet201,The required minimum input size of the model is 29x29,densenet201,1.0,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1019,vgg16_bn,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.165769162,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1020,efficientnet_b6,"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_16,0.2190101,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1021,resnet101,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.189306989,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
1022,regnet_y_1_6gf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.101720342,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1023,regnet_x_32gf,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.158478148,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1024,quantization.shufflenet_v2_x2_0,in this section we use the 1.6 billion flops (1.6GF) regime,regnet_y_1_6gf,0.181310062,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1025,detection.fasterrcnn_resnet50_fpn,The RPN implements the terminology of neural network with attention to tell the object detection (Fast R-CNN) where to look,detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1026,mnasnet1_3,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.19649974,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1027,regnet_x_400mf,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,1.0,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1028,mnasnet1_3,"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_18,0.060517284,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1029,convnext_tiny,Constructs a DeepLabV3 model with a ResNet-50 backbone,segmentation.deeplabv3_resnet50,0.199821734,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1030,detection.fasterrcnn_mobilenet_v3_large_fpn,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.200943675,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
1031,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.173797733,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
1032,convnext_base,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.039614243,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1033,shufflenet_v2_x2_0,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.1281173,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1034,shufflenet_v2_x1_5,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.107481422,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1035,vgg19,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.166417147,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1036,detection.fasterrcnn_mobilenet_v3_large_fpn,adopted from BERT,vit_b_16,0.003134111,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
1037,vgg11_bn,quantization.googlenet is the quantized model of googlenet,quantization.googlenet,0.150130897,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
1038,resnet34,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.056408456,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1039,detection.keypointrcnn_resnet50_fpn,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.120266133,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1040,quantization.shufflenet_v2_x1_5,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.115425124,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1041,segmentation.lraspp_mobilenet_v3_large,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.178834775,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1042,inception_v3,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.130511402,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1043,googlenet,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.136483831,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1044,vgg16,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.109829525,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1045,wide_resnet101_2,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.14648136,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1046,vit_b_16,The densenet-169 is larger at just about 55MB in size vs the densenet-121 model's roughly 31MB size,densenet169,0.089277355,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1047,regnet_x_400mf,It is the simplest of all other vgg versions,vgg11,0.181186606,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1048,resnet50,It is the simplest of all other vgg versions,vgg11,0.140161032,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
1049,regnet_x_800mf,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.143010401,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1050,vit_b_32,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg11,0.180639399,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1051,detection.ssdlite320_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_32,0.119249664,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1052,mnasnet0_5,"The only preprocessing that was done is that they subtracted the mean RGB value from each pixel, computed over the whole training set",vgg19,0.228805507,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1053,efficientnet_b6,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.189632272,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1054,vgg13,"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,0.141038923,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1055,quantization.googlenet,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.096983582,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1056,segmentation.deeplabv3_mobilenet_v3_large,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.216573107,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1057,segmentation.fcn_resnet101,Note that this model does not include any fine-tuned heads,vit_b_16,0.181192498,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1058,shufflenet_v2_x2_0,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.105340872,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1059,mnasnet1_0,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.181026414,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
1060,resnext50_32x4d,The key building block is an Inception Module,inception_v3,0.145443054,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1061,quantization.shufflenet_v2_x0_5,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.184823481,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1062,mobilenet_v2,Replacing ReLU with GeLU,convnext_tiny,0.123495621,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1063,detection.keypointrcnn_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,0.15767143,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1064,vgg11_bn,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.07977958,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
1065,mobilenet_v3_large,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.125188445,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1066,regnet_y_400mf,"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,1.0,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1067,resnet152,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.199231333,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1068,vit_b_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_5,0.150491174,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1069,efficientnet_b4,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.174021913,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1070,mobilenet_v3_small,Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples,detection.retinanet_resnet50_fpn,0.148500043,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1071,detection.fasterrcnn_resnet50_fpn,Faster R-CNN builds a network that has only a single stage,detection.fasterrcnn_resnet50_fpn,1.0,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1072,video.mc3_18,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.047075839,1x32x32,video classification,Null,TV
1073,vgg11,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.08365032,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1074,regnet_y_1_6gf,The required minimum input size of the model is 21x21,squeezenet1_0,0.208100975,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1075,inception_v3,The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes,regnet_y_400mf,0.148043344,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1076,mnasnet0_5,MNASNet with depth multiplier of 0.5,mnasnet0_5,1.0,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1077,efficientnet_b3,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.157295625,32,classification,7844244d-9c32-4d34-a9c6-57903c28bde0___efficientnet_b3,TV
1078,regnet_x_16gf,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.090476502,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1079,regnet_y_3_2gf,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.093676337,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1080,resnext101_32x8d,The required minimum input size of the model is 17x17,squeezenet1_1,0.211587638,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1081,convnext_tiny,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.101038203,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1082,vit_l_32,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.115364537,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1083,mobilenet_v2,mobilenet_v2 is used for image classification,mobilenet_v2,1.0,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1084,regnet_x_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_vgg16,0.076140676,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1085,quantization.shufflenet_v2_x0_5,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,0.226414321,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1086,convnext_small,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.173873327,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1087,segmentation.deeplabv3_resnet101,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.072713748,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1088,regnet_x_16gf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.130940148,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1089,regnet_y_3_2gf,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.169149753,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1090,vit_l_32,"Each 2-layer block is replaced in the 34-layer net with this 3-layer bottleneck block, resulting in a 50-layer ResNet",resnet50,0.157171788,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1091,mnasnet1_3,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.131391498,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1092,squeezenet1_1,"RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks",detection.retinanet_resnet50_fpn,0.167008435,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1093,vit_l_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,0.112706073,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1094,wide_resnet101_2,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.123661523,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1095,vgg19,"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_vgg16,0.135176707,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1096,segmentation.fcn_resnet50,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.140743006,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1097,regnet_y_3_2gf,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.160330839,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1098,convnext_small,"Optimization techniques for googlenet: Inception module, Global Average Pooling, Auxiliary classifiers, Dropout, Multi-crop evaluation",googlenet,0.109697176,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1099,densenet201,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.172243939,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1100,vit_l_16,"The number of channels in outer 1x1 convolutions is the same, Wide ResNet-50-2 has 2048-1024-2048 channels",wide_resnet50_2,0.123150743,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1101,detection.fasterrcnn_mobilenet_v3_large_320_fpn,This model has 3.8 billion FLOPs,resnet50,0.157826126,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1102,vit_l_32,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.062371996,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1103,convnext_base,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.127495326,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1104,shufflenet_v2_x1_0,adopted from BERT,vit_b_16,0.001540621,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1105,inception_v3,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.133677936,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1106,resnet152,quantization.shufflenet_v2_x1_0 is the quantized model of shufflenet_v2_x1_0,quantization.shufflenet_v2_x1_0,0.220333582,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1107,regnet_x_16gf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.1669024,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1108,detection.fasterrcnn_resnet50_fpn,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.057116698,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1109,efficientnet_b6,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.177944284,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1110,shufflenet_v2_x2_0,Note that this model does not include any fine-tuned heads,vit_b_16,0.216792379,224,classification,13cdcf0c-4e44-434b-879a-3cbd1c72d561___shufflenet_v2_x2_0,TV
1111,segmentation.deeplabv3_resnet101,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_resnet50_fpn,0.172239819,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1112,mnasnet0_5,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.055179091,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1113,mobilenet_v3_large,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.195659289,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1114,convnext_large,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.052512046,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1115,efficientnet_b0,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_b0,1.0,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1116,convnext_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,0.118302579,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1117,vgg16_bn,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.179990475,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1118,convnext_base,This model has 3.8 billion FLOPs,resnet50,0.078318353,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1119,regnet_x_3_2gf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.164348422,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1120,wide_resnet101_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,0.235021996,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1121,convnext_base,Replacing ReLU with GeLU,convnext_base,1.0,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1122,mnasnet0_75,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.053123431,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1123,detection.keypointrcnn_resnet50_fpn,quantization.shufflenet_v2_x1_5 is the quantized model of shufflenet_v2_x1_5,quantization.shufflenet_v2_x1_5,0.118992425,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1124,squeezenet1_1,This network is a pretty large network and it has about 138 million parameters,vgg16,0.185536201,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1125,resnet34,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet34,1.0,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1126,efficientnet_b2,obtained by scaling up the baseline network,efficientnet_b2,1.0,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
1127,regnet_x_16gf,adopted from BERT,vit_b_16,0.011266863,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1128,vgg13_bn,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.115023974,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1129,vit_l_16,They stack residual blocks on top of each other to form network: for example  a ResNet-18 has 18 layers using these blocks,resnet18,0.128544123,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1130,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",shufflenet_v2_x0_5,1.0,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1131,vit_l_32,The number of parameters is 3.4 million,mobilenet_v2,0.090197438,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1132,inception_v3,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.098588835,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1133,resnext101_32x8d,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.151900856,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1134,detection.retinanet_resnet50_fpn,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1135,vit_b_16,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.153386035,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1136,regnet_y_800mf,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.157154306,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1137,segmentation.deeplabv3_resnet101,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.094085124,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1138,efficientnet_b6,"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_18,0.225784887,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1139,mobilenet_v2,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.082854108,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1140,quantization.resnet50,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.165470395,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
1141,mnasnet0_75,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.129769546,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1142,quantization.mobilenet_v3_large,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.178645651,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1143,vgg13,SqueezeNet 1.0 achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters,squeezenet1_0,0.221732121,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1144,quantization.shufflenet_v2_x2_0,Replacing ReLU with GeLU,convnext_tiny,0.089114734,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1145,detection.keypointrcnn_resnet50_fpn,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.169358308,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1146,efficientnet_b5,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.13194879,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1147,quantization.shufflenet_v2_x0_5,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.111832516,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1148,regnet_y_400mf,Constructs a RegNetY_400MF architecture,regnet_y_400mf,1.0,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1149,efficientnet_b6,"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_resnet50,0.224108305,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1150,detection.maskrcnn_resnet50_fpn,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.166026937,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1151,video.r3d_18,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.117913044,1x32x32,video classification,Null,TV
1152,convnext_base,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.101564792,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1153,convnext_base,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,0.151594501,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1154,quantization.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_vgg16,0.118584316,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1155,efficientnet_b0,"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_vgg16,0.125764194,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1156,regnet_x_3_2gf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_x_3_2gf,1.0,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1157,detection.retinanet_resnet50_fpn,"Each layer from the FPN is passed to the subnets, enabling it to detect objects as various scales",detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1158,segmentation.deeplabv3_mobilenet_v3_large,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.16682442,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1159,detection.ssdlite320_mobilenet_v3_large,They use option 2 for increasing dimensions,resnet50,0.222782362,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1160,mnasnet1_3,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.145544733,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1161,regnet_x_800mf,ReLU non-linearity as activation functions,vgg11,0.142362743,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1162,regnet_x_400mf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.159532713,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1163,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.191218892,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1164,convnext_large,convnext_large is based on the large version of Swin Transformer architecture,convnext_large,1.0,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1165,mnasnet0_75,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.025727034,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1166,shufflenet_v2_x0_5,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.198709001,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1167,quantization.shufflenet_v2_x1_0,spatial padding was used to preserve the spatial resolution of the image,vgg19,0.071657122,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1168,quantization.mobilenet_v3_large,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.mobilenet_v3_large,1.0,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1169,regnet_y_16gf,"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_v3,0.17687132,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1170,vit_b_16,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.062267504,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1171,quantization.shufflenet_v2_x0_5,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.138409927,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1172,densenet121,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.207647269,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1173,mnasnet1_0,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.173616108,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
1174,detection.fasterrcnn_resnet50_fpn,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.089562046,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1175,regnet_y_8gf,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_8gf,1.0,32,classification,f1fecb43-6b05-4b3f-8cac-e9a1382418a2___regnet_y_8gf,TV
1176,regnet_x_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,0.186862532,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1177,convnext_tiny,"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,0.209497602,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1178,vit_l_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,0.094116641,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1179,quantization.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,0.253946563,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1180,regnet_x_8gf,The required minimum input size of the model is 17x17,squeezenet1_1,0.207507393,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1181,quantization.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,0.1629499,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1182,efficientnet_b2,EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient,efficientnet_b2,1.0,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
1183,regnet_y_800mf,"It is simple to train, flexible and generalizes well in applications like keypoint detection, human pose estimation",detection.maskrcnn_resnet50_fpn,0.132660639,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1184,vit_b_32,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.12516454,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1185,densenet201,Replacing ReLU with GeLU,convnext_tiny,0.07393294,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1186,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_vgg16,0.107307492,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1187,regnet_x_800mf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.172442645,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1188,densenet121,ReLU non-linearity as activation functions,vgg11,0.151740169,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1189,efficientnet_b6,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.179782592,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1190,mnasnet1_0,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.16078344,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
1191,wide_resnet50_2,ReLU non-linearity as activation functions,vgg11,0.183592712,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1192,regnet_x_800mf,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.173754267,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1193,regnet_x_3_2gf,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.16007839,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1194,regnet_x_1_6gf,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.063868822,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1195,mnasnet0_75,"(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_18,0.099619469,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1196,quantization.inception_v3,MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation,segmentation.lraspp_mobilenet_v3_large,0.166342475,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1197,convnext_small,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.128938832,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1198,vgg13_bn,"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_b7,0.22344627,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1199,efficientnet_b2,"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)",vgg19,0.203662488,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
1200,convnext_tiny,"Together with the first 7x7 convolutional layer and the final fully-connected layer, there are 18 layers in total",resnet18,0.193108784,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1201,efficientnet_b5,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.178491022,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1202,vgg19_bn,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.143249954,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
1203,video.r3d_18,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.173962697,1x32x32,video classification,Null,TV
1204,mobilenet_v3_small,MobileNetv3-Large is 27% faster than MobileNetV2 while maintaining similar mAP,mobilenet_v3_small,1.0,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1205,regnet_y_3_2gf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.134358738,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1206,vgg16_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,0.091756731,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1207,quantization.inception_v3,SqueezeNet 1.0 achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters,squeezenet1_0,0.220125558,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1208,quantization.googlenet,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.161401596,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1209,wide_resnet50_2,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.116822811,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1210,vit_l_32,GoogLeNet is a type of convolutional neural network based on the Inception architecture,googlenet,0.164866422,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1211,vit_l_16,MobileNetV2 is a convolutional neural network architecture that seeks to perform well on mobile devices,mobilenet_v2,0.149180908,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1212,efficientnet_b0,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.117011207,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1213,vit_l_16,quantization.mobilenet_v3_large is the quantized model of mobilenet_v3_large,quantization.mobilenet_v3_large,0.125599888,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1214,regnet_y_16gf,"Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs",regnet_y_16gf,1.0,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1215,efficientnet_b0,Note that this model does not include any fine-tuned heads,vit_b_16,0.166544023,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1216,mnasnet0_5,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.149209636,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1217,quantization.resnet18,There are 4 convolutional layers in each module (excluding the 1x1 convolutional layer),quantization.resnet18,1.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1218,convnext_large,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.212457829,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1219,vit_b_16,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,1.0,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1220,inception_v3,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.138079812,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1221,squeezenet1_1,"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_18,0.162060687,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1222,regnet_y_400mf,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.198806894,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1223,quantization.resnet50,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.172972964,32,classification,44a968b2-3e66-4b06-b2b2-49b181776b93___quantization.resnet50,TV
1224,vgg13,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.227424379,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1225,convnext_large,The required minimum input size of the model is 29x29,densenet121,0.171766905,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1226,segmentation.lraspp_mobilenet_v3_large,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.100978241,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1227,densenet201,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.190949398,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1228,quantization.shufflenet_v2_x0_5,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.192029063,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1229,detection.ssdlite320_mobilenet_v3_large,"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_18,0.159450159,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1230,detection.keypointrcnn_resnet50_fpn,ReLU non-linearity as activation functions,vgg11,0.163105785,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1231,mnasnet0_75,adopted from BERT,vit_b_16,0.0,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1232,resnext50_32x4d,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.220644824,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1233,vit_l_16,quantization.mobilenet_v2 is the quantized model of mobilenet_v2,quantization.mobilenet_v2,0.130698469,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1234,convnext_base,The densenet-201 is larger at over 77MB in size vs the densenet-121 model's roughly 31MB size,densenet201,0.142681416,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1235,convnext_base,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.178032272,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1236,quantization.mobilenet_v3_large,MobileNetv3-Large is 27% faster than MobileNetV2 while maintaining similar mAP,quantization.mobilenet_v3_large,1.0,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1237,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.230128753,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1238,vgg13_bn,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.130453644,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1239,mnasnet0_5,"Dense blocks, Batch Normalization(BN)",densenet121,0.104160543,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1240,video.r3d_18,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.123398989,1x32x32,video classification,Null,TV
1241,mobilenet_v3_small,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.2320148,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1242,video.r3d_18,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,1.0,1x32x32,video classification,Null,TV
1243,vit_l_32,"Build depth network by reusing simple convolution blocks (small (3x3) convolution filters), Dense evaluation, Multi-crop evaluation",vgg11,0.172754506,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1244,quantization.shufflenet_v2_x1_0,VGG19 has 19.6 billion FLOPs,vgg19,0.112967244,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1245,segmentation.deeplabv3_resnet101,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_vgg16,0.154565115,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1246,resnext50_32x4d,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.105027366,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1247,detection.ssd300_vgg16,Replacing ReLU with GeLU,convnext_tiny,0.073283634,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1248,regnet_y_800mf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.191821742,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1249,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_vgg16,0.167528052,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
1250,quantization.shufflenet_v2_x1_5,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.207719433,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1251,vgg19,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.143794058,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1252,detection.ssd300_vgg16,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.13453028,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1253,segmentation.deeplabv3_mobilenet_v3_large,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.115362475,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1254,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_vgg16,0.124038155,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
1255,convnext_tiny,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,1.0,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1256,efficientnet_b0,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.160742036,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1257,video.r2plus1d_18,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.181366983,1x32x32,video classification,Null,TV
1258,alexnet,adopted from BERT,vit_b_16,0.030905712,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1259,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_resnet50,0.148861764,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1260,detection.fasterrcnn_mobilenet_v3_large_fpn,"With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region",detection.fasterrcnn_mobilenet_v3_large_fpn,1.0,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
1261,quantization.mobilenet_v2,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.136702143,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
1262,resnext101_32x8d,"Let's take a slight detour to understand how the keypoints are encoded, with a visual example",detection.keypointrcnn_resnet50_fpn,0.208718341,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1263,efficientnet_b0,One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder,vit_b_16,0.18691467,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1264,regnet_x_400mf,The required minimum input size of the model is 32x32,vgg11,0.190128021,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1265,regnet_x_16gf,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.163992915,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1266,detection.maskrcnn_resnet50_fpn,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.069907952,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1267,alexnet,"It generalizes well over on other datasets, is easy to implement and hence, was well-received by the community",detection.ssdlite320_mobilenet_v3_large,0.180921345,64,classification,3e3c9344-6eee-452d-a612-d2c82279930f___alexnet,TV
1268,quantization.mobilenet_v2,"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,0.149588024,224,classification,1af2dae5-1659-4c48-ae01-8e04e216c14d___quantization.mobilenet_v2,TV
1269,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_vgg16,0.094821798,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1270,resnet34,"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_vgg16,0.166755295,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1271,efficientnet_b0,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,1.0,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1272,densenet121,Replacing ReLU with GeLU,convnext_tiny,0.096534006,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1273,regnet_x_32gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder,vit_l_16,0.174857608,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1274,efficientnet_b2,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.090163133,32,classification,f0f6589d-1cb5-412c-b115-b26e4b60cb66___efficientnet_b2,TV
1275,inception_v3,The Deeplab applies atrous convolution for up-sample,segmentation.deeplabv3_resnet101,0.125697481,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1276,mobilenet_v3_small,quantization.resnext101_32x8d is the quantized model of resnext101_32x8d,quantization.resnext101_32x8d,0.181815801,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1277,regnet_x_800mf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.13346509,32,classification,a781f9d1-529b-4ef5-85a4-8c10847a24ce___regnet_x_800mf,TV
1278,segmentation.fcn_resnet50,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.202750952,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1279,video.r2plus1d_18,This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model,vit_b_32,0.189276548,1x32x32,video classification,Null,TV
1280,segmentation.deeplabv3_mobilenet_v3_large,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.16705836,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1281,convnext_base,They use option 2 for increasing dimensions,resnet50,0.201327218,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1282,segmentation.deeplabv3_resnet101,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.087103629,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1283,convnext_tiny,"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,0.222667896,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1284,vgg11_bn,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.183001303,32,classification,8f22434a-a213-4e41-ba92-09e65975a727___vgg11_bn,TV
1285,resnet34,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.178399794,32,classification,932ffbfc-9b7f-442d-9e48-4c70eb85d610___resnet34,TV
1286,quantization.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_vgg16,0.143385735,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1287,segmentation.deeplabv3_mobilenet_v3_large,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.104056485,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1288,wide_resnet101_2,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.079845192,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1289,regnet_x_3_2gf,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.164555632,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1290,detection.ssdlite320_mobilenet_v3_large,Constructs an SSDlite model with input size 320x320 and a MobileNetV3 Large backbone,detection.ssdlite320_mobilenet_v3_large,1.0,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1291,quantization.shufflenet_v2_x2_0,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.11235541,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1292,quantization.shufflenet_v2_x1_5,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.182866847,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1293,shufflenet_v2_x1_0,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.156530668,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1294,quantization.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_vgg16,0.166865205,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1295,resnext101_32x8d,They use option 2 for increasing dimensions,resnet50,0.265715539,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1296,detection.retinanet_resnet50_fpn,"retinanet_resnet50_fpn is used for object detection, Instance Segmentation and Person Keypoint Detection",detection.retinanet_resnet50_fpn,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1297,wide_resnet101_2,The required minimum input size of the model is 21x21,squeezenet1_0,0.174201322,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1298,quantization.shufflenet_v2_x2_0,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.114811298,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1299,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,0.197820126,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1300,convnext_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,0.153257351,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1301,regnet_y_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_vgg16,0.185693893,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1302,convnext_tiny,Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone,segmentation.lraspp_mobilenet_v3_large,0.130682333,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1303,vit_b_16,Constructs a RegNetY_1.6GF architecture,regnet_y_1_6gf,0.18725579,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1304,regnet_x_8gf,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.171077087,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1305,regnet_y_32gf,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.145837387,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1306,quantization.inception_v3,convnext_tiny is based on the tiny version of Swin Transformer architecture,convnext_tiny,0.229718797,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1307,quantization.resnet18,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.114758619,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1308,regnet_y_400mf,"The network is composed of multiple stage consisting of multiple blocks, forming a stem (start), body (main part) and head (end)",regnet_y_400mf,1.0,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1309,segmentation.fcn_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_32,0.213621604,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1310,segmentation.fcn_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_vgg16,0.166171911,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1311,efficientnet_b7,"The code is written in Pytorch, using the Torchvision library",detection.keypointrcnn_resnet50_fpn,0.10629135,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
1312,regnet_x_8gf,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.150423433,32,classification,8bdbcff5-c7d8-471d-8da0-b3a0970a4318___regnet_x_8gf,TV
1313,quantization.shufflenet_v2_x1_5,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.shufflenet_v2_x1_5,1.0,224,classification,fba40b04-5503-40ca-b63e-684590c75b01___quantization.shufflenet_v2_x1_5,TV
1314,convnext_small,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.03544886,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1315,quantization.googlenet,The squeezing (bottoleneck layer) and expansion behavior is common in neural architectures,squeezenet1_0,0.224123249,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1316,mobilenet_v3_large,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.216369092,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1317,regnet_x_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_vgg16,0.081544891,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1318,shufflenet_v2_x0_5,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.123842978,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1319,regnet_y_16gf,quantization.inception_v3 is the quantized model of inception_v3,quantization.inception_v3,0.15648547,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1320,regnet_y_800mf,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.136003361,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1321,vit_l_16,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,0.18613243,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1322,detection.ssdlite320_mobilenet_v3_large,"8 of them are convolutional layers, and 3 are fully connected layers",vgg11,0.115420492,300,detedtion,85117627-6fc2-4b82-8afe-83d3b7285ad0___detection.ssdlite320_mobilenet_v3_large,TV
1323,detection.keypointrcnn_resnet50_fpn,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.065198103,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1324,shufflenet_v2_x1_5,The required minimum input size of the model is 32x32,vgg11,0.139254392,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1325,vit_l_16,obtained by scaling up the baseline network,efficientnet_b1,0.158489429,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1326,efficientnet_b0,"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_resnet50,0.191452098,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1327,detection.fasterrcnn_resnet50_fpn,The required minimum input size of the model is 32x32,vgg11,0.15265727,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1328,wide_resnet50_2,Wide ResNet-50-2 Wide Residual Networks,wide_resnet50_2,1.0,224,classification,5088f214-7651-4d97-8c4f-6808636752d2___wide_resnet50_2,TV
1329,convnext_small,quantization.resnet18 is the quantized model of resnet18,quantization.resnet18,0.241438482,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1330,segmentation.fcn_resnet101,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.14870973,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1331,wide_resnet101_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_vgg16,0.157258891,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1332,vgg16,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,1.0,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1333,resnext101_32x8d,"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_vgg16,0.188974276,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1334,vit_l_16,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.066233317,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1335,convnext_base,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.143218903,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1336,efficientnet_b4,"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)",vgg19,0.198202793,32,classification,17559e94-556c-4719-8a1c-ead5c8cc6902___efficientnet_b4,TV
1337,densenet121,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,0.193833147,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1338,resnext50_32x4d,"Compared with ResNet and PolyNet, ResNeXt has 3.03% Top-5 error rate, which is a large relative improvement of about 15%",resnext50_32x4d,1.0,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1339,regnet_y_800mf,"employed using ReLU6 as the non-linearity function, instead of simple ReLU, to limit computations",detection.ssdlite320_mobilenet_v3_large,0.167191774,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1340,resnext101_32x8d,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.12705651,224,classification,045afd34-c124-4b01-a566-b36935de0f5d___resnext101_32x8d,TV
1341,vit_b_16,quantization.mobilenet_v3_large is the quantized model of mobilenet_v3_large,quantization.mobilenet_v3_large,0.117300098,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1342,quantization.resnet18,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.085091564,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1343,vit_l_32,"1x1 Convolution, Bottleneck Residual Block, Batch Normalization, 3D Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax",video.r3d_18,0.138997188,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1344,detection.keypointrcnn_resnet50_fpn,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.12984758,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1345,densenet169,"h-wish, Squeeze-and-excitation (SE), NetAdapt, Neural Architecture Search (NAS)",mobilenet_v3_large,0.180867106,32,classification,031a42f6-b965-4445-a2a3-93f8fa01ac68___densenet169,TV
1346,regnet_y_32gf,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.125310715,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1347,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.221069264,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1348,regnet_y_400mf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.110450356,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1349,shufflenet_v2_x1_0,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.197845151,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1350,regnet_y_32gf,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,1.0,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1351,efficientnet_b6,ReLU non-linearity as activation functions,vgg11,0.203249261,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1352,quantization.shufflenet_v2_x1_0,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.113629917,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1353,quantization.shufflenet_v2_x0_5,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.162028657,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1354,detection.retinanet_resnet50_fpn,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.060064747,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1355,efficientnet_b5,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.224142171,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1356,mnasnet0_5,"In particular, in this section we use the 800 million flops (800MF) regime",regnet_x_800mf,0.06278049,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1357,convnext_tiny,"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_5,0.189047628,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1358,regnet_y_32gf,Final output is obtained by performing non maximum suppression,detection.ssd300_vgg16,0.124575496,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1359,resnext50_32x4d,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.166286252,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1360,vgg16,RegNetY models come from a design space that also includes the Squeeze-and-Excitation (SE) operation (RegNetY = RegNetX + SE),regnet_y_400mf,0.172164114,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1361,regnet_x_16gf,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.166963868,32,classification,74e223a5-7171-4e91-b5fa-81e25dda3f5f___regnet_x_16gf,TV
1362,video.r3d_18,"As a result, you get an text embedding model that works for 50+ languages",vit_b_32,0.113741051,1x32x32,video classification,Null,TV
1363,vit_l_16,Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone,segmentation.lraspp_mobilenet_v3_large,0.159697467,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1364,densenet201,"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,0.19628582,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1365,quantization.shufflenet_v2_x2_0,One also adds absolute position embedding before feeding the sequence to the layers of the Transformer encoder.,vit_l_32,0.208056667,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1366,mobilenet_v2,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.205271062,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1367,quantization.mobilenet_v3_large,MobileNet v3-Large has 5.4 million parameters,quantization.mobilenet_v3_large,1.0,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1368,segmentation.deeplabv3_resnet101,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.108016063,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1369,convnext_small,They stack residual blocks on top of each other to form network: for example  a ResNet-101 has 101 layers using these blocks,resnet101,0.162563433,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1370,quantization.shufflenet_v2_x2_0,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.125710288,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1371,vit_b_16,"RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks",detection.retinanet_resnet50_fpn,0.117565595,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1372,wide_resnet101_2,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.246389074,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1373,regnet_x_400mf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_x_400mf,1.0,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1374,googlenet,"66 multiply adds,2900000 parameters and top accuracy is 68.1",mobilenet_v3_small,0.072485152,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1375,densenet121,The required minimum input size of the model is 29x29,densenet121,1.0,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1376,squeezenet1_0,This can be thought of as a pyramid of reference anchor boxes,detection.fasterrcnn_resnet50_fpn,0.142113017,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1377,vit_l_16,MNASNet with depth multiplier of 0.75,mnasnet0_75,0.184082534,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1378,vgg13,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.210183293,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1379,vgg19_bn,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.208188927,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
1380,mnasnet1_0,"inception_v3 is Based on inceptionV2 architecture, re-add some special optimization tricks with less than 25 million parameters",inception_v3,0.218129173,32,classification,7056295f-b7ae-419f-8f36-c77faafa9755___mnasnet1_0,TV
1381,efficientnet_b0,Anchor boxes are a set of predefined bounding boxes of a certain height and width,detection.maskrcnn_resnet50_fpn,0.154887552,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1382,regnet_y_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_resnet50,0.15143893,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1383,regnet_y_16gf,"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_18,0.16695362,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1384,quantization.shufflenet_v2_x0_5,"BN-auxiliary, Multi-crop evaluation, Label Smoothing Regularization (LSR), RMSProp",inception_v3,0.15997913,224,classification,77469138-8937-49a4-a6f0-078875c401c0___quantization.shufflenet_v2_x0_5,TV
1385,regnet_y_3_2gf,Constructs a RegNetY_3.2GF architecture,regnet_y_3_2gf,1.0,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1386,efficientnet_b0,"SSD was built on VGG-16, with additional auxiliary structures to improve performance",detection.ssd300_vgg16,0.170431104,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1387,efficientnet_b1,EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient,efficientnet_b1,1.0,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
1388,detection.fasterrcnn_mobilenet_v3_large_320_fpn,217 multiply adds 5400000 parameters and top accuracy is 75.6,mobilenet_v3_large,0.160558446,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1389,convnext_base,quantization.mobilenet_v3_large is the quantized model of mobilenet_v3_large,quantization.mobilenet_v3_large,0.135034823,32,classification,929087e2-f995-4f67-af1b-96730ac8281e___convnext_base,TV
1390,video.mc3_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_resnet50,0.200606256,1x32x32,video classification,Null,TV
1391,vit_l_16,Faster R-CNN builds a network that has only a single stage,detection.fasterrcnn_resnet50_fpn,0.159065979,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1392,quantization.resnet18,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.158960059,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1393,vit_b_32,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.076500482,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1394,quantization.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",quantization.shufflenet_v2_x2_0,1.0,224,classification,ab2a23e3-8b8e-4a2f-bdd3-eed9f1957e37___quantization.shufflenet_v2_x2_0,TV
1395,convnext_large,"with 69 Million Parameters, 11 Billion FLOPs the file size is 131.82 MB",wide_resnet50_2,0.086373313,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1396,regnet_x_32gf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.181864199,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1397,video.r3d_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,0.06672376,1x32x32,video classification,Null,TV
1398,regnet_x_32gf,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,vgg16,0.142220678,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1399,detection.retinanet_resnet50_fpn,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.05592803,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1400,vit_l_32,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.076374578,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1401,resnext50_32x4d,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.176951677,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1402,video.r3d_18,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.099943,1x32x32,video classification,Null,TV
1403,vit_b_32,A SqueezeNet stacks a bunch of fire modules and a few pooling layers,squeezenet1_0,0.104936791,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1404,regnet_y_800mf,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.154471762,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1405,detection.ssd300_vgg16,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.110849355,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1406,regnet_x_400mf,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.163238818,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1407,shufflenet_v2_x0_5,Vision Transformer (ViT) model trained using the DINO method,vit_b_16,0.160456744,224,classification,5dffe2c9-8200-4486-8060-fdc452069e3c___shufflenet_v2_x0_5,TV
1408,mobilenet_v3_large,"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_18,0.211147305,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1409,mobilenet_v3_small,"Inverted residual structure, Linear Bottleneck",mobilenet_v2,0.187969637,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1410,efficientnet_b7,EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient,efficientnet_b7,1.0,32,classification,7d62134a-5181-4bfe-9348-4a275d6707f4___efficientnet_b7,TV
1411,resnet152,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.071098883,32,classification,f37b765e-adc3-4613-8b9c-097ef572e6b4___resnet152,TV
1412,mnasnet0_75,"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_75,1.0,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1413,detection.fasterrcnn_resnet50_fpn,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.114456841,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1414,densenet201,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.206377804,32,classification,dd5ae764-321c-46e3-82b7-77931b949b9a___densenet201,TV
1415,mnasnet0_75,in this section we use the 16 billion flops (16GF) regime,regnet_y_16gf,0.041137638,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1416,mobilenet_v2,adopted from BERT,vit_b_16,0.0,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1417,quantization.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,0.162519242,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1418,vit_b_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,0.14494141,224,classification,8df67ed4-71f3-4987-90ae-41c6ffb0b37d___vit_b_16,TV
1419,vit_b_32,"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_vgg16,0.076943485,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1420,googlenet,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.059350042,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1421,vgg16,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.233965826,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1422,regnet_y_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,0.131296276,32,classification,f5542fb9-8246-4ff8-9ec1-5f1b29e884ba___regnet_y_1_6gf,TV
1423,mnasnet0_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,0.137400654,32,classification,acbe2219-85e7-44bd-8bcb-173e1a266c07___mnasnet0_5,TV
1424,googlenet,in this section we use the 3.2 billion flops (3.2GF) regime,regnet_y_3_2gf,0.06369236,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1425,vit_l_16,adopted from BERT,vit_l_16,1.0,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1426,shufflenet_v2_x1_0,"It has 11 weight layers in total, so the name VGG11",vgg11,0.179173141,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1427,vgg11,Constructs a ShuffleNetV2 with 0.5x output channels,shufflenet_v2_x0_5,0.148445032,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1428,mobilenet_v3_large,It is the same as vgg19 but it also contains a batch normalization layer,vgg19_bn,0.208595258,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1429,vgg11,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.116376963,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1430,regnet_y_3_2gf,Not all the convolutional layers are followed by max-pooling layers,vgg11,0.156124818,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1431,inception_v3,DenseNet-121 has 120 Convolutions and 4 AvgPool,densenet121,0.206680313,128,classification,378a1903-c7c0-4a6c-ba63-e70391a3d013___inception_v3,TV
1432,resnet50,They stack residual blocks on top of each other to form network: for example  a ResNet-50 has fifty layers using these blocks,resnet50,1.0,32,classification,3e296cbb-e588-4b1e-af87-71f4e37b0353___resnet50,TV
1433,efficientnet_b0,"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)",vgg19,0.216280562,32,classification,0f31f271-643e-4825-9fff-613bb8ccc459___efficientnet_b0,TV
1434,efficientnet_b1,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.11523205,32,classification,a6c9a04c-353f-47ef-8c0a-abf9009b4b47___efficientnet_b1,TV
1435,squeezenet1_1,"ViT-B-32 means the ""base"" variant with 32x32 input patch size",vit_b_32,0.188289034,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1436,vgg16,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,0.087850785,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1437,vgg13_bn,Constructs a RegNetX_400MF architecture,regnet_x_400mf,0.195238089,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1438,segmentation.deeplabv3_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_vgg16,0.178181195,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1439,quantization.resnet18,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.104005336,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1440,regnet_y_800mf,2D max pooling in between the weight layers as explained in the paper,vgg11_bn,0.178164274,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1441,shufflenet_v2_x1_0,ShuffleNet v2 is a convolutional neural network optimized for a direct metric (speed) rather than indirect metrics like FLOPs,shufflenet_v2_x1_0,1.0,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1442,convnext_large,DeepLab is a state-of-the-art semantic segmentation model designed and open-sourced by Google,segmentation.deeplabv3_resnet50,0.165806081,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1443,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 paper introduced the concept of anchor boxes",detection.fasterrcnn_mobilenet_v3_large_fpn,0.128419891,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1444,vgg13_bn,The key building block is an Inception Module,inception_v3,0.165978884,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1445,mobilenet_v3_small,An anchor box is a reference box of a specific scale and aspect ratio,detection.fasterrcnn_resnet50_fpn,0.171592941,224,classification,51fa421a-18f4-46c1-bcf8-bdf9bfa7773a___mobilenet_v3_small,TV
1446,detection.retinanet_resnet50_fpn,"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,1.0,32,detedtion,2b36844d-3c79-48f2-9be0-d217c2e9b8a4___detection.retinanet_resnet50_fpn,TV
1447,resnext50_32x4d,"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_v3,0.2199504,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1448,detection.fasterrcnn_resnet50_fpn,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.070132027,32,detedtion,676af582-2358-4d40-9b62-fc7088a72910___detection.fasterrcnn_resnet50_fpn,TV
1449,mobilenet_v2,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.200262894,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1450,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 method introduced the concept of anchor boxes",detection.fasterrcnn_resnet50_fpn,0.188423587,32,classification,128857fc-34df-4ab5-a484-31963b81815c___regnet_y_800mf,TV
1451,shufflenet_v2_x1_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_vgg16,0.101751267,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1452,resnext50_32x4d,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.189779265,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1453,regnet_x_32gf,"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_16,0.143945581,32,classification,3852f08e-6ecd-4476-931e-85410b536a2f___regnet_x_32gf,TV
1454,vit_l_32,"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_l_32,1.0,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1455,wide_resnet101_2,Note that this model does not include any fine-tuned heads,vit_b_16,0.187315705,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1456,quantization.mobilenet_v3_large,There are 4 convolutional layers in each module (excluding the 1x1 convolutional layer),resnet18,0.185128126,224,classification,416c6d81-cd9b-4e0c-a618-0c86cbe1199c___quantization.mobilenet_v3_large,TV
1457,convnext_tiny,"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)",vgg19,0.134130833,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1458,segmentation.deeplabv3_resnet101,This model has 3.8 billion FLOPs,resnet50,0.118753321,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1459,segmentation.deeplabv3_resnet101,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.179268809,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1460,quantization.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,0.16067607,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1461,segmentation.deeplabv3_mobilenet_v3_large,vit_b_32 is used for image classification,vit_b_32,0.128356392,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1462,resnext50_32x4d,this architectures provides support for INT8 quantized models. You can get a model with random weights by calling its constructor,quantization.googlenet,0.182618154,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1463,detection.fasterrcnn_mobilenet_v3_large_fpn,"ViT-L-16 means the ""Large"" variant with 16x16 input patch size",vit_l_16,0.133210137,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
1464,vit_b_32,"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_v3,0.155334556,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1465,detection.fasterrcnn_mobilenet_v3_large_fpn,Replacing ReLU with GeLU,convnext_tiny,0.133681508,32,detedtion,0dcbf093-089e-4981-a79d-b3bbb25aa4bb___detection.fasterrcnn_mobilenet_v3_large_fpn,TV
1466,convnext_small,It is the same as vgg16 but it also contains a batch normalization layer,vgg16_bn,0.230442154,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1467,vgg11,vgg is used for image classification,vgg11,1.0,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1468,vgg16_bn,Constructs a ShuffleNetV2 with 1.0x output channels,shufflenet_v2_x1_0,0.183322648,32,classification,b51916a0-806f-48ad-a264-3379ca394033___vgg16_bn,TV
1469,regnet_x_3_2gf,"Residual Block, Shortcut connection, Zero-padding, projection shortcut",resnet18,0.124942362,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1470,quantization.resnet18,They stack residual blocks on top of each other to form network: for example  a ResNet-18 has 18 layers using these blocks,quantization.resnet18,1.0,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1471,resnext50_32x4d,"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,0.160750218,224,classification,ffd33677-601e-4cc8-919e-183ff5b44849___resnext50_32x4d,TV
1472,squeezenet1_1,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.115387319,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1473,shufflenet_v2_x1_0,"9 Million Parameters, 1 Billion FLOPs and File Size is 30.08  MB",efficientnet_b2,0.103864568,224,classification,00a89650-b632-4193-a06f-2593b4d1479c___shufflenet_v2_x1_0,TV
1474,mobilenet_v2,"Training Techniques are Weight Decay, SGD with Momentum",shufflenet_v2_x0_5,0.133229706,224,classification,7539d1dc-35a6-44bd-9df8-5e0ccd19691d___mobilenet_v2,TV
1475,resnet101,in this section we use the 8 billion flops (8GF) regime,regnet_y_8gf,0.115577508,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
1476,segmentation.fcn_resnet101,in this section we use the 32 billion flops (32GF) regime,regnet_y_32gf,0.028614147,224,segmentation,ddb681a8-10e3-463a-9b31-7a385e91bca1___segmentation.fcn_resnet101,TV
1477,detection.ssd300_vgg16,"Similar to DPM, it utilized weighted sum of the localization and confidence loss to train the model",detection.ssd300_vgg16,1.0,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1478,detection.ssd300_vgg16,implemented using Atrous Convolution,segmentation.deeplabv3_resnet50,0.122811358,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1479,convnext_small,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.217886938,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1480,mnasnet1_3,Using Focal loss parameter reduces the loss contribution from easy examples,detection.retinanet_resnet50_fpn,0.125016732,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1481,detection.ssd300_vgg16,"The image encoder from CLIP is unchanged, i.e. you can use the original CLIP image encoder to encode images",vit_b_32,0.092257242,300,detedtion,86bac9ef-9591-4874-be1a-919f65410724___detection.ssd300_vgg16,TV
1482,vgg16,This model has been created using Multilingual Knowledge Distillation,vit_b_32,0.128348629,32,classification,8c05c8fc-1f82-4ef8-b0ac-685f9c7b2ee0___vgg16,TV
1483,regnet_y_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_16,0.179614521,32,classification,e769048a-ac86-42f5-9a5a-237a1ccdc05d___regnet_y_400mf,TV
1484,regnet_x_400mf,The required minimum input size of the model is 17x17,squeezenet1_1,0.212108592,32,classification,c2c64695-7fc0-40d6-80a1-cb65aac7a0ab___regnet_x_400mf,TV
1485,segmentation.deeplabv3_resnet101,"4 Million Parameters, 325 Million FLOPs and File Size is 16.92 MB",mnasnet1_0,0.111539472,224,segmentation,a04dba2c-2b6d-4837-8f89-9a5b0dc2eae4___segmentation.deeplabv3_resnet101,TV
1486,densenet121,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.09206725,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1487,vgg13,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.067182997,32,classification,b1cc0891-ce41-4a7f-87e1-bdc0acad76bb___vgg13,TV
1488,densenet121,"Using parallel data, the multilingual student model learns to align the teachers vector space across many languages",vit_b_32,0.188615583,32,classification,5f796743-20c0-46a9-8987-979b75729623___densenet121,TV
1489,squeezenet1_0,ReLU non-linearity as activation functions,vgg11,0.054384268,32,classification,3f6321e6-bafa-4d39-a5fc-cb62d1cef98e___squeezenet1_0,TV
1490,regnet_x_1_6gf,"They employ solely locally connected layers, such as convolution, pooling and upsampling",segmentation.fcn_resnet50,0.188171842,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1491,vit_b_32,"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,0.159457133,224,classification,5328f12f-d0b7-4d07-ba88-66622231cff6___vit_b_32,TV
1492,detection.keypointrcnn_resnet50_fpn,"ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them",convnext_tiny,0.185847974,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1493,segmentation.deeplabv3_resnet50,"CNN with dilated convolutions, succeeded by a fully-connected (  Dense) CRF",segmentation.deeplabv3_resnet50,1.0,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1494,segmentation.deeplabv3_resnet50,Replacing ReLU with GeLU,convnext_tiny,0.17062464,224,segmentation,f201252b-7782-4852-9606-2ace9c8faeaa___segmentation.deeplabv3_resnet50,TV
1495,segmentation.lraspp_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,0.172221911,32,segmentation,a96e27ee-f0e8-403a-b68a-58f61613f722___segmentation.lraspp_mobilenet_v3_large,TV
1496,video.r2plus1d_18,"ViT-b-16 means the ""base"" variant with 16x16 input patch size",vit_b_16,0.106887189,1x32x32,video classification,Null,TV
1497,resnet18,"8 Million Parameters, 910 Million FLOPs and File Size is 30.04  MB",efficientnet_b1,0.049577744,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1498,detection.fasterrcnn_mobilenet_v3_large_320_fpn,"As a whole, Faster R-CNN consists of two modules",detection.fasterrcnn_mobilenet_v3_large_320_fpn,1.0,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1499,quantization.googlenet,"In particular, in this section we use the 400 million flops (400MF) regime",regnet_x_400mf,0.081438356,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1500,vit_l_16,quantization.shufflenet_v2_x2_0 is the quantized model of shufflenet_v2_x2_0,quantization.shufflenet_v2_x2_0,0.125209254,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1501,quantization.resnet18,It is the simplest of all other vgg versions,vgg11,0.132703058,32,classification,63f0d2d2-873e-4610-a07f-24cf329c7df1___quantization.resnet18,TV
1502,regnet_y_3_2gf,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_3_2gf,1.0,32,classification,bebc54eb-6c97-4614-95e6-9bf6679e9b8d___regnet_y_3_2gf,TV
1503,quantization.shufflenet_v2_x1_0,They also used hard negative mining and heavy data augmentation,detection.ssd300_vgg16,0.169876426,224,classification,32f76bbb-17fd-4e47-987a-aa9ac5cf4950___quantization.shufflenet_v2_x1_0,TV
1504,mnasnet0_75,The 16 in VGG16 refers to it has 16 layers that have weights,vgg16,0.20536539,32,classification,85fed7c6-2afc-4e2a-a0e2-51b3423fd841___mnasnet0_75,TV
1505,regnet_x_3_2gf,"These auxiliary convolution layers, added to the end of the model, decrease progressively in size",detection.ssd300_vgg16,0.202182262,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1506,mnasnet1_3,Factorizing traditional convolutions to 1x1 convolutions,mobilenet_v2,0.139917621,32,classification,ebeed259-2d7d-432d-84c5-35d123ebc710___mnasnet1_3,TV
1507,video.mc3_18,"5 Million Parameters, 511 Million FLOPs and File Size is 20.39 MB",efficientnet_b0,0.082986247,1x32x32,video classification,Null,TV
1508,vgg13_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,0.174149615,32,classification,f348c642-44c6-43c5-bd52-0e2e9e51f336___vgg13_bn,TV
1509,segmentation.fcn_resnet50,"Inside the body, multiple stages are defined, and each of the stages is composed of multiple blocks",regnet_y_400mf,0.17542775,224,segmentation,1f4b8754-2a67-459e-8ec9-92f4060d5671___segmentation.fcn_resnet50,TV
1510,quantization.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,0.159609523,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1511,video.r2plus1d_18,This model has 3.8 billion FLOPs,resnet50,0.152906646,1x32x32,video classification,Null,TV
1512,vit_l_32,"By widening Residual Network (ResNet), the network can be shallower with the same accuracy or improved accuracy",wide_resnet50_2,0.15680255,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1513,resnet101,This model has 3.8 billion FLOPs,resnet50,0.118212636,32,classification,04158a1b-a22e-4bd3-b852-6cebc110648c___resnet101,TV
1514,vit_l_32,Fast and optimized computation leads to near real time performance,segmentation.deeplabv3_resnet50,0.077300574,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1515,vit_l_32,obtained by scaling up the baseline network,efficientnet_b1,0.163529591,224,classification,08539c7c-055b-48a1-8458-8b7bb55a6d0f___vit_l_32,TV
1516,shufflenet_v2_x1_5,"12 Million Parameters, 2 Billion FLOPs and File Size is 47.08  MB",efficientnet_b3,0.128958738,224,classification,4d7b558b-1c9b-4634-b030-d6a1e5bc202c___shufflenet_v2_x1_5,TV
1517,detection.maskrcnn_resnet50_fpn,"It uses similar basic Faster R-CNN architecture for object proposal, but adds a mask head parallel to classification and bounding box regressor head",detection.maskrcnn_resnet50_fpn,1.0,32,detedtion,3d43683a-34a7-4e87-b828-e499e6ca4441___detection.maskrcnn_resnet50_fpn,TV
1518,detection.keypointrcnn_resnet50_fpn,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.121482943,32,detedtion,8ce6e56f-16fa-4f42-b2b2-195be943adaf___detection.keypointrcnn_resnet50_fpn,TV
1519,quantization.inception_v3,"For RegNetY we make one change, which is to include Squeeze-and-Excitation blocks",regnet_y_400mf,0.172321155,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1520,googlenet,"They just differ in the output size, and the way the keypoints are encoded in the keypoint mask",detection.keypointrcnn_resnet50_fpn,0.17476959,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1521,wide_resnet101_2,It is the same as vgg13 but it also contains a batch normalization layer,vgg13_bn,0.257155786,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1522,resnet18,ConvNext is similar to any other ConvNet in the sense that no new design is implemented,convnext_tiny,0.189384604,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1523,wide_resnet101_2,Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks,wide_resnet101_2,1.0,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1524,mobilenet_v3_large,convnext_base is based on the base version of Swin Transformer architecture,convnext_base,0.193597981,224,classification,b954a84c-087b-4096-afaa-da33475bc619___mobilenet_v3_large,TV
1525,vit_l_16,"It is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions",resnext101_32x8d,0.13730015,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1526,resnet18,"However, it was still below the real time performance (>30 fps)",detection.maskrcnn_resnet50_fpn,0.091355156,32,classification,aa8471ad-62e8-4ad2-b234-ffe9add34bb9___resnet18,TV
1527,googlenet,convnext_small is based on the small version of Swin Transformer architecture,convnext_small,0.196172874,32,classification,8967d9df-6581-4adc-af64-b32ff33449d8___googlenet,TV
1528,quantization.googlenet,Overall training of Mask R-CNN is similar to faster R-CNN,detection.maskrcnn_resnet50_fpn,0.201044399,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1529,segmentation.deeplabv3_mobilenet_v3_large,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.187200797,224,segmentation,f0c04ad1-e3e2-47aa-9775-52686b19137b___segmentation.deeplabv3_mobilenet_v3_large,TV
1530,squeezenet1_1,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.158269272,32,classification,972df28a-31d3-4c32-b71e-d8d1c7e08432___squeezenet1_1,TV
1531,efficientnet_b6,The required minimum input size of the model is 32x32,vgg11,0.223706938,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1532,efficientnet_b5,in this section we use the 800 million flops (800MF) regime,regnet_y_800mf,0.135106187,32,classification,72cf1af5-bf7b-4353-8685-1a4cbfd36ef0___efficientnet_b5,TV
1533,vit_l_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_b1,0.186029012,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1534,convnext_small,"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)",vgg19,0.130134184,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1535,convnext_large,"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,0.161847062,32,classification,0aa2d9d6-adc0-4b5e-b745-ac5e075c9ad6___convnext_large,TV
1536,densenet161,"the first single stage detector that matched accuracy of contemporary two stage detectors like Faster R-CNN, while maintaining real time speed",detection.ssd300_vgg16,0.117903372,32,classification,1d8fa56d-2022-4c11-b7c3-cb094b7c746b___densenet161,TV
1537,wide_resnet101_2,This model has 3.8 billion FLOPs,resnet50,0.143848108,224,classification,04028cb8-db68-4dd5-b106-2f3c23a3b462___wide_resnet101_2,TV
1538,vgg19,"Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions",vgg19,1.0,32,classification,32b2efd3-3b25-4eed-b587-47e760844538___vgg19,TV
1539,detection.fasterrcnn_mobilenet_v3_large_320_fpn,The first advantage is an additional nonlinear rectification between these two operations,video.r2plus1d_18,0.202074333,32,detedtion,6465a30a-dcbd-4867-ac10-6e76acffd939___detection.fasterrcnn_mobilenet_v3_large_320_fpn,TV
1540,vit_l_16,deeplabv3_mobilenet_v3_large is used for semantic segmentation,segmentation.deeplabv3_mobilenet_v3_large,0.186300153,224,classification,d88a4fe7-d921-49cc-9bdd-eb1ce35ddf9e___vit_l_16,TV
1541,convnext_tiny,max pooling was performed over a 2 * 2 pixel windows with stride 2,vgg19,0.110496628,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1542,vgg19_bn,RegNetX models come from a design space that does not include the Squeeze-and-Excitation (SE) operation (RegNetX = RegNetY - SE),regnet_x_400mf,0.168821876,32,classification,e3af00e5-a278-4ed7-a88f-2df330ad861d___vgg19_bn,TV
1543,convnext_tiny,Note that the COCO Dataset offers keypoints only for the person class,detection.keypointrcnn_resnet50_fpn,0.097323071,32,classification,eeda954f-86c5-4428-850a-50279a0ae42b___convnext_tiny,TV
1544,regnet_y_32gf,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,vgg16,0.150963029,32,classification,acb6125b-d90e-4f31-bfe7-e045f9647977___regnet_y_32gf,TV
1545,video.mc3_18,"ViT-L-32 means the ""Large"" variant with 32x32 input patch size",vit_l_32,0.112180287,1x32x32,video classification,Null,TV
1546,regnet_x_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_18,0.110048389,32,classification,f9dc2fc5-4ae6-404b-89e7-329732d4ec31___regnet_x_1_6gf,TV
1547,regnet_x_3_2gf,"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_0,0.207122234,32,classification,0022b3e8-9fc2-49c2-82b3-04fca534fd16___regnet_x_3_2gf,TV
1548,convnext_small,It is based on an inverted residual structure where the residual connections are between the bottleneck layers,mobilenet_v2,0.198208779,32,classification,49adf8f0-abf2-4656-a0bc-6b03afc60fac___convnext_small,TV
1549,quantization.googlenet,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_vgg16,0.174967457,32,classification,6fc3d37b-f6ba-4faa-a962-44104f9aa5e9___quantization.googlenet,TV
1550,regnet_y_16gf,The Deeplab applies atrous convolution for up-sample ,segmentation.deeplabv3_resnet50,0.106134031,32,classification,8da29d50-2bab-4fd5-a9f5-83db42e7b543___regnet_y_16gf,TV
1551,quantization.inception_v3,"The training accuracy, the test accuracy and the gradients of their training processes show similar stages",vgg13_bn,0.098200893,128,classification,0a5135f0-572a-495f-9560-cc3dc6b66f47___quantization.inception_v3,TV
1552,efficientnet_b6,"As teacher model, we used the original clip-ViT-B-32 and then trained a multilingual DistilBERT model as student model",vit_b_32,0.152945783,32,classification,9c10947b-840c-4ec6-86b6-1bdebcb5365e___efficientnet_b6,TV
1553,vgg11,quantization.shufflenet_v2_x0_5 is the quantized model of shufflenet_v2_x0_5,quantization.shufflenet_v2_x0_5,0.162406536,32,classification,05d5802a-4443-4afe-ae59-b7bd4d1fa803___vgg11,TV
1554,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl32,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
1555,google/t5-efficient-base-nh8,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.052425555884838,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
1556,google/t5-efficient-small-el8,This does not make a very readable text,pere/DeUnCaser,0.068779595196247,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
1557,clue/xlnet_chinese_large,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.031184408813715,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
1558,groar/gpt-neo-1.3B-finetuned-escape2,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
1559,google/t5-efficient-tiny-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,0.054674295708537,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
1560,google/t5-efficient-xl-nl6,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0225469730794429,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
1561,stas/pegasus-cnn_dailymail-tiny-random,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0322135463356971,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
1562,rahulacj/bertweet-base-finetuned-sentiment-analysis,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0253246799111366,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
1563,aditeyabaral/sentencetransformer-distilbert-hinglish-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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
1564,brad1141/Longformer-finetuned-norm,I have made no attempts to censor,bigjoedata/rockbot,0.0500585883855819,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
1565,diper1998/distilgpt2-finetuned-AT,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0376408845186233,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
1566,tucan9389/kcbert-base-finetuned-squad,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
1567,mimicheng/codeparrot-ds,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0997172147035598,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
1568,microsoft/layoutlm-base-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,0.0588635019958019,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
1569,brad1141/Longformer-finetuned-norm,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.075522594153881,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
1570,junnyu/roformer_base_wwm_cluecorpussmall,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0158163147668044,-1.0,NLP,4b8c1fe6-3a18-4652-8492-d55f2100d14b___roformer_base_wwm_cluecorpussmall,HF
1571,akdeniz27/deberta-v2-xlarge-cuad,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0632055252790451,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
1572,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
1573,lgrobol/electra-minuscule-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,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
1574,google/t5-efficient-tiny-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,0.0074096787720918,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
1575,google/t5-efficient-large-el6,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0036933040246367,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
1576,google/t5-efficient-small-el32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0095866862684487,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
1577,amazon/bort,"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,0.0272069033235311,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
1578,yorko/scibert_scivocab_uncased_long_4096,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0684189582243561,-1.0,NLP,433e7c6c-2bf3-464d-874f-4c1d3fb0365e___scibert_scivocab_uncased_long_4096,HF
1579,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,b9ef8354-f986-45c7-b886-a25b2b261896___transformers-ud-japanese-electra-base-ginza-510,HF
1580,fnlp/bart-large-chinese,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
1581,google/t5-efficient-small-kv256,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0578032489866017,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
1582,superspray/electra_large_discriminator_squad2_custom_dataset,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0236358568072319,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
1583,sentence-transformers/paraphrase-MiniLM-L3-v2,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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
1584,google/t5-efficient-base-nl4,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
1585,allenai/wmt19-de-en-6-6-big,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0858024880290031,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
1586,google/t5-efficient-xl-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,0.0624515935778617,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
1587,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"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,0.0949911028146743,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
1588,cmarkea/distilcamembert-base-qa,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,1.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
1589,cammy/led-large-16384-arxiv-100-MDS,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0208495613187551,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
1590,VietAI/gpt-neo-1.3B-vietnamese-news,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0196714894846081,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
1591,aditeyabaral/sentencetransformer-bert-hinglish-small,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,0.078870989382267,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
1592,hfl/chinese-electra-180g-small-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,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
1593,diper1998/distilgpt2-finetuned-AT,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0522007271647453,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
1594,uer/t5-v1_1-base-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,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
1595,google/t5-efficient-small-dm128,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0339236911386251,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
1596,google/t5-efficient-tiny-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,0.0477653518319129,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
1597,minimaxir/magic-the-gathering,It can be considered as small copy of Conversational DistilRuBERT-base,DeepPavlov/distilrubert-tiny-cased-conversational,0.0186579637229443,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
1598,google/t5-efficient-base-ff6000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
1599,google/t5-efficient-xxl-nl4,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
1600,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
1601,akdeniz27/deberta-v2-xlarge-cuad,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.075438916683197,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
1602,stas/mt5-tiny-random,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0939711183309555,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
1603,DemangeJeremy/4-sentiments-with-flaubert,This particular model is trained on python only code approaching 4GB in size,NovelAI/genji-python-6B,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
1604,google/t5-efficient-large-dm768,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0174785666167736,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
1605,Iskaj/hf-challenge-test,"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,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
1606,google/t5-efficient-xl-nl16,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0784952491521835,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
1607,google/t5-efficient-large-nl32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0639889985322952,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
1608,aditeyabaral/sentencetransformer-bert-hinglish-small,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0160932037979364,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
1609,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
1610,google/byt5-large,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0071240440011024,-1.0,NLP,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
1611,cross-encoder/ms-marco-MiniLM-L-2-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,0.0182632077485323,-1.0,NLP,c2d1bb44-2e13-4b2e-8b5d-23fa4c9a88e9___ms-marco-MiniLM-L-2-v2,HF
1612,google/t5-efficient-xl-nl6,"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,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
1613,google/t5-efficient-large-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,0.028991705738008,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
1614,DemangeJeremy/4-sentiments-with-flaubert,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,0.0104770530015229,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
1615,IDEA-CCNL/Zhouwenwang-Unified-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,0.0521561510860919,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
1616,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0404637878139813,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
1617,GKLMIP/electra-myanmar-small-uncased,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
1618,google/t5-efficient-small-kv256,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0576467271894216,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
1619,stas/pegasus-cnn_dailymail-tiny-random,"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,0.0374418711289763,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
1620,google/t5-efficient-tiny-nh8,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0506736915558575,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
1621,google/t5-efficient-large-nl32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0235897861421108,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
1622,MoritzLaurer/MiniLM-L6-mnli,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0586793857316175,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
1623,google/t5-efficient-large-el4,This does not make a very readable text,pere/DeUnCaser,0.0574280656874179,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
1624,google/t5-efficient-tiny-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,0.0590289328247308,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
1625,UWB-AIR/MQDD-duplicates,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0198027469838658,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
1626,dbmdz/convbert-base-german-europeana-cased,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0838717035949229,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
1627,superspray/electra_large_discriminator_squad2_custom_dataset,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0727300196886062,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
1628,google/t5-efficient-small-dm128,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0584565475583076,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
1629,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-XXL-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xxl-nl4,0.0967703014612197,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
1630,ian0delond/model-test,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0399010106921196,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
1631,google/bert_uncased_L-12_H-256_A-4,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0170423748592535,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
1632,google/bert_uncased_L-10_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,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
1633,google/t5-efficient-xl-nl16,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0710850656032562,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
1634,castorini/afriberta_small,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0709321144968271,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
1635,google/t5-efficient-small-dm256,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.023385090753436,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
1636,GKLMIP/electra-myanmar-small-uncased,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0001159855164587,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
1637,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0999500080943107,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
1638,ml6team/distilbart-tos-summarizer-tosdr,"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,0.0661256848834454,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
1639,google/t5-efficient-small-el64,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,0.0822245068848133,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
1640,facebook/wmt21-dense-24-wide-en-x,"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,1.0,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
1641,google/t5-efficient-base-kv32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0642555486410855,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
1642,negfir/distilbert-base-uncased-finetuned-squad,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0751318037509918,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
1643,jnz/electra-ka,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.029091041535139,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
1644,amazon/bort,"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,0.0046308226883411,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
1645,google/t5-efficient-tiny-ff6000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0474224155768751,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
1646,google/t5-efficient-base-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,0.0861128456890582,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
1647,uer/bart-chinese-4-768-cluecorpussmall,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.013116905465722,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
1648,minimaxir/magic-the-gathering,And there's a lot of English in this dataset too,ErykWdowiak/GPTalian,0.0461220033466815,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
1649,diper1998/distilgpt2-finetuned-AT,"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,0.0371968261897563,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
1650,google/t5-efficient-small-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-v1,0.0640343446284532,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
1651,wukevin/tcr-bert,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
1652,google/t5-efficient-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,0.0,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
1653,groar/gpt-neo-1.3B-finetuned-escape2,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0478455275297164,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
1654,google/t5-efficient-xl-nl8,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0359544847160577,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
1655,microsoft/layoutlm-large-uncased,"!!!  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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
1656,google/t5-efficient-base-dm2000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0020164269953966,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
1657,negfir/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,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
1658,thu-coai/LongLM-large,T5-Efficient-SMALL-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm768,0.0373070901259779,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
1659,DemangeJeremy/4-sentiments-with-flaubert,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,0.0219188360497355,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
1660,google/t5-efficient-small-dm2000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0431670257821678,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
1661,mrm8488/xlm-multi-finetuned-xquadv1,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0494316406548022,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
1662,mwesner/bart-mlm,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0510940849781036,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
1663,google/t5-efficient-small-nl2,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0234966659918427,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
1664,qarib/bert-base-qarib_far_6500k,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0004398657474667,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
1665,mrm8488/mobilebert-uncased-finetuned-squadv2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0255261696875095,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
1666,google/t5-efficient-tiny-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,0.0485687628388404,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
1667,google/t5-efficient-tiny-nh8,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0929220058023929,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
1668,saibo/random-roberta-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,0.0513984714634716,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
1669,google/t5-efficient-small-ff3000,T5-Efficient-SMALL-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff3000,1.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
1670,kyryl0s/gpt2-uk-xxs,See our full codebase and our preprint for more information,wukevin/tcr-bert,0.0862065441906451,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
1671,uer/pegasus-base-chinese-cluecorpussmall,"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,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
1672,google/t5-efficient-small-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,0.0634636748582124,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
1673,ian0delond/model-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,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
1674,google/t5-efficient-large-nl10,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0149963367730379,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
1675,ErykWdowiak/GPTalian,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,1.0,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
1676,1one5ome/gpt2-chinese-gulong,It is the first pretrained model to leverage transfer learning for downtream tasks for Fon,Gilles/FongBERT,0.0924215838313102,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
1677,hfl/chinese-xlnet-mid,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0213972888886928,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
1678,amazon/bort,"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,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
1679,KoichiYasuoka/roberta-base-ukrainian,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,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
1680,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"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,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
1681,tau/tavbert-he,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,0.0560287293046712,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
1682,rahulacj/bertweet-base-finetuned-sentiment-analysis,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0745239108800888,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
1683,google/t5-efficient-mini-nl24,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0546876937150954,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
1684,google/t5-efficient-small-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,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
1685,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
1686,GKLMIP/electra-myanmar-small-uncased,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0500291623175143,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
1687,google/t5-efficient-base-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,0.0652462001889943,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
1688,akdeniz27/deberta-v2-xlarge-cuad,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0922383964061737,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
1689,hfl/chinese-electra-small-ex-generator,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
1690,krevas/finance-koelectra-base-discriminator,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0259060654789209,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
1691,google/t5-efficient-tiny-nl32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0624359771609306,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
1692,mimicheng/codeparrot-ds,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
1693,mwesner/reformer-clm,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,0.0939392521977424,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
1694,google/t5-efficient-mini-nl24,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.026013871654868,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
1695,lgrobol/electra-minuscule-generator,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0778215080499648,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
1696,brad1141/Longformer-finetuned-norm,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0904783308506012,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
1697,google/t5-efficient-tiny-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,0.0605673920363187,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
1698,akdeniz27/deberta-v2-xlarge-cuad,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-v2,0.0915182009339332,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
1699,malay-huggingface/t5-tiny-bahasa-cased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0113088414072989,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
1700,google/t5-efficient-small-dm1000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0042951041832566,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
1701,NeuML/bert-small-cord19,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0501764050374428,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
1702,allenai/macaw-3b,"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,0.0513666234910487,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
1703,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el6,0.0559333749115467,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
1704,hfl/rbtl3,Total parameters 1.5B,microsoft/deberta-v2-xxlarge-mnli,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
1705,google/t5-efficient-xl-nl6,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0566721744835376,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
1706,jonatasgrosman/wav2vec2-xls-r-1b-english,"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,0.0399778311451275,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
1707,google/t5-efficient-large-nl10,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0663045160472392,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
1708,allenai/macaw-3b,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
1709,saibo/random-roberta-mini,Amazon's BORT,amazon/bort,0.0334133207798003,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
1710,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0794533342123031,-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
1711,superspray/electra_large_discriminator_squad2_custom_dataset,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0449801236391067,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
1712,kyryl0s/gpt2-uk-xxs,Boris was trained on around 78B tokens of French text from the C4 dataset,Cedille/fr-boris,0.073171867057681,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
1713,rinna/japanese-gpt2-xsmall,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
1714,google/t5-efficient-large-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,0.0239919386804103,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
1715,google/t5-efficient-small-nl22,"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,0.0576886646449565,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
1716,nghuyong/ernie-tiny,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
1717,google/t5-efficient-large-nl12,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0143260369077324,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
1718,ai4bharat/MultiIndicHeadlineGeneration,"MultiIndicHeadlineGeneration  is a multilingual, sequence-to-sequence pre-trained model focusing only on Indic languages",ai4bharat/MultiIndicHeadlineGeneration,1.0,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
1719,google/t5-efficient-small-el12,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.045894768089056,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
1720,google/t5-efficient-large-nh2,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0492947902530431,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
1721,hf-internal-testing/tiny-random-rembert,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0706093162298202,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
1722,microsoft/layoutlm-base-uncased,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0270363669842481,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
1723,mimicheng/codeparrot-ds,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0448299944400787,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
1724,castorini/afriberta_large,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0526340682990848,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
1725,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-NL22 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl22,0.0074746869504451,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
1726,google/t5-efficient-large-kv16,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0530947120860218,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
1727,facebook/blenderbot-90M,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
1728,sentence-transformers/paraphrase-MiniLM-L3-v2,"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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
1729,abdusahmbzuai/aradia-ctc-distilhubert-ft,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.027300838381052,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
1730,stas/mt5-tiny-random,"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,0.0641330927610397,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
1731,VietAI/gpt-neo-1.3B-vietnamese-news,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0316509390249848,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
1732,kyryl0s/gpt2-uk-xxs,"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-12,0.046413967385888,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
1733,TJMUCH/transcriptome-iseeek,For other languages this will be mainly experimental,pere/DeUnCaser,0.0759022980928421,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
1734,google/t5-efficient-xxl-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,0.0349126793444155,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
1735,negfir/distilbert-base-uncased-finetuned-cola,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0405972525477409,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
1736,tuner007/pegasus_qa,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
1737,IDEA-CCNL/Erlangshen-Longformer-110M,"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,0.0391060225665569,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
1738,tau/tavbert-he,T5-Efficient-BASE-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff6000,0.0073380935937165,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
1739,mgreenbe/bertlet-base-uncased-for-sequence-classification,This model is a fine-tuned version of  on an unknown dataset,mgreenbe/bertlet-base-uncased-for-sequence-classification,1.0,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
1740,google/t5-efficient-small-kv32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
1741,stas/mt5-tiny-random,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0790499597787857,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
1742,sonoisa/byt5-small-japanese,"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,0.0154372453689575,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
1743,google/t5-efficient-small-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,0.0456865555606782,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
1744,gilparmentier/pokemon_gptj_model,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0350242499262093,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
1745,rinna/japanese-gpt-1b,"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,0.0277564873298009,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
1746,allenai/wmt19-de-en-6-6-big,"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,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
1747,google/t5-efficient-large-nh32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0664121396839618,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
1748,izumi-lab/bert-small-japanese-fin,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.02438510581851,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
1749,google/t5-efficient-large-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,0.0458762859925628,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
1750,jpcorb20/pegasus-large-reddit_tifu-samsum-512,"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-v1,0.0560407790665824,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
1751,google/t5-efficient-mini-nl8,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0150691200979053,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
1752,uer/pegasus-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,0.0015157712623476,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
1753,mrm8488/deberta-v3-small-finetuned-mrpc,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0775680691003799,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
1754,google/t5-efficient-large-nl10,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
1755,sentence-transformers/paraphrase-MiniLM-L3-v2,Disclaimer: This model card has been written by gchhablani,google/fnet-large,0.0961946249008178,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
1756,google/t5-efficient-base-dm2000,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0844118073582649,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
1757,aditeyabaral/sentencetransformer-bert-hinglish-small,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0462872795760631,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
1758,microsoft/layoutlm-base-cased,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,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
1759,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.065980851650238,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
1760,tau/tavbert-he,Zero-shot SELECTRA is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset,Recognai/zeroshot_selectra_small,0.0733885262161493,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
1761,everdoubling/byt5-Korean-small,"This model is the scorer trained for summarization of BillSum (paper, datasets)",andrejmiscic/simcls-scorer-billsum,0.0334806470200419,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
1762,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
1763,khizon/distilbert-unreliable-news-eng-4L,"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,0.0316727166064083,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
1764,lannelin/bert-imdb-1hidden,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0172438453882932,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
1765,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,This does not make a very readable text,pere/DeUnCaser,0.0276256874203681,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
1766,google/t5-efficient-tiny-nl6,"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-v1,0.0681776106357574,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
1767,dbmdz/electra-base-turkish-cased-discriminator,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0353221129626035,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
1768,dhpollack/distilbert-dummy-sentiment,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
1769,google/t5-efficient-large-kv256,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0108351726084947,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
1770,abdusahmbzuai/ft-tatoeba-ar-en,"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,0.0914799347519874,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
1771,wukevin/tcr-bert,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0745303556323051,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
1772,google/t5-efficient-mini-nl12,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0447287261486053,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
1773,tartuNLP/gpt-4-est-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,0.0474028065800666,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
1774,stas/t5-very-small-random,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0001719184219837,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
1775,hfl/chinese-electra-180g-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,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
1776,pulp/CHILDES-ParentBERTo,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0702759698033332,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
1777,stas/t5-very-small-random,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
1778,google/t5-efficient-small-el64,"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,0.0542520508170127,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
1779,google/t5-efficient-xl-nl4,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0479549057781696,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
1780,google/t5-efficient-small-ff12000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0407011676579713,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
1781,google/t5-efficient-small-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,0.0217609982937573,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
1782,piotr-rybak/poleval2021-task4-herbert-large-encoder,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
1783,google/t5-efficient-xl-nl4,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0611225292086601,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
1784,google/t5-efficient-xl-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,0.0697367005050182,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
1785,google/t5-efficient-large-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,0.0847690515220165,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
1786,google/t5-efficient-base-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,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
1787,TJMUCH/transcriptome-iseeek,"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,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
1788,bigjoedata/rockbot,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,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
1789,Milos/slovak-gpt-j-405M,Amazon's BORT,amazon/bort,0.0781089353064695,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
1790,google/t5-efficient-tiny-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,0.0605174060910939,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
1791,anirudh21/xlnet-base-cased-finetuned-rte,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0410718582570552,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
1792,jhu-clsp/bibert-ende,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0355931588759024,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
1793,NovelAI/genji-python-6B,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0955355117718378,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
1794,mgreenbe/bertlet-base-uncased-for-sequence-classification,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
1795,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
1796,KoichiYasuoka/roberta-base-ukrainian,I have made no attempts to censor,bigjoedata/rockbot,0.0551234558224678,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
1797,google/t5-efficient-base-kv16,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
1798,hfl/rbtl3,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0557276681065559,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
1799,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv32,0.0960871875286102,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
1800,google/reformer-crime-and-punishment,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-12,0.0191792910918593,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
1801,facebook/mbart-large-50-many-to-one-mmt,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,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
1802,google/t5-efficient-small-kv16,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0892650596797466,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
1803,google/t5-efficient-tiny-ff3000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0699575580656528,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
1804,google/t5-efficient-base-el16,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0264523290097713,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
1805,mwesner/reformer-clm,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0932763665914535,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
1806,superb/hubert-large-superb-sid,"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,0.0653032422997057,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
1807,google/t5-efficient-xl-nl8,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0782245211303233,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
1808,bigjoedata/rockbot,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0005587538083394,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
1809,mmaguero/gn-bert-tiny-cased,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,1.0,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
1810,lassl/gpt2-ko-small,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
1811,google/t5-efficient-base-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,0.0573787949979304,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
1812,sentence-transformers/paraphrase-MiniLM-L3-v2,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0390293896198272,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
1813,suzuki/distilbert-base-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,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
1814,google/t5-efficient-small-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,0.0873995907604694,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
1815,google/t5-efficient-base-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,0.038240198045969,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
1816,google/t5-efficient-tiny-nh1,"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,0.069056749343872,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
1817,lgrobol/electra-minuscule-generator,"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_8-12,0.0492103761062025,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
1818,google/t5-efficient-base-ff12000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
1819,google/t5-efficient-xl-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,0.0420559784397482,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
1820,rdenadai/BR_BERTo,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,0.0169424979637067,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
1821,google/t5-efficient-base-el6,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.079590119421482,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
1822,TJMUCH/transcriptome-iseeek,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0735398679971695,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
1823,google/t5-efficient-tiny-nh1,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0373886357992887,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
1824,GKLMIP/electra-myanmar-small-uncased,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
1825,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl2,0.0873683243989944,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
1826,google/t5-efficient-base-ff9000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0486626625061034,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
1827,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0726687498390674,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
1828,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0337782092392444,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
1829,lgrobol/electra-minuscule-generator,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.021454581990838,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
1830,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
1831,skplanet/dialog-koelectra-small-generator,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,1.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
1832,google/t5-efficient-large-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-v1,0.0569381797686218,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
1833,nherve/flaubert-oral-asr,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
1834,zoha/wav2vec2-base-timit-demo-colab,Its original repository can be found here,tau/splinter-large-qass,0.095766082406044,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
1835,google/t5-efficient-base-dm256,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0113110784441232,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
1836,sonoisa/byt5-small-japanese,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0945760160684585,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
1837,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
1838,Norod78/hebrew_poetry-gpt_neo-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,0.0618748404085635,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
1839,jorge-henao/gpt2-small-spanish-disco-poetry-wt,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.024889500811696,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
1840,negfir/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,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
1841,google/t5-efficient-large-dm512,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0934057645499706,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
1842,google/t5-efficient-large-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,0.0615573860704898,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
1843,allenai/wmt16-en-de-dist-6-1,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0721070952713489,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
1844,rmihaylov/bert-base-pos-theseus-bg,Pretrained model on Bulgarian language using a masked language modeling (MLM) objective,rmihaylov/bert-base-pos-theseus-bg,1.0,-1.0,NLP,428758d5-6177-42ba-a879-c30586fca3ab___bert-base-pos-theseus-bg,HF
1845,dhpollack/distilbert-dummy-sentiment,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
1846,mrp/simcse-model-wangchanberta,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0597335109487175,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
1847,rmihaylov/pegasus-base-cnn-dailymail-bg,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0319568151608109,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
1848,google/t5-efficient-large-nh2,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0847134068608284,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
1849,stas/mt5-tiny-random,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0424649305641651,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
1850,ToToKr/kobigbird-bert-base-finetuned-klue,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0926292762160301,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
1851,Milos/slovak-gpt-j-1.4B,"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,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
1852,negfir/distilbert-base-uncased-finetuned-cola,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0708813741803169,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
1853,Milos/slovak-gpt-j-405M,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,1.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
1854,aditeyabaral/sentencetransformer-distilbert-hinglish-big,A direct export of the model checkpoint was first made available in this repository,google/rembert,0.0064817927777767,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
1855,google/t5-efficient-tiny-ff12000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0705211758613586,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
1856,tucan9389/kcbert-base-finetuned-squad,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0685864463448524,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
1857,microsoft/unispeech-sat-base,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0485415562987327,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
1858,stas/t5-very-small-random,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0574845373630523,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
1859,microsoft/layoutlm-large-uncased,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0476888976991176,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
1860,clue/roberta_chinese_3L312_clue_tiny,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0832536667585372,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
1861,google/t5-efficient-tiny-nh1,"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,0.0666306260973214,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
1862,microsoft/layoutlm-large-uncased,For other languages this will be mainly experimental,pere/DeUnCaser,0.0630133599042892,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
1863,prajjwal1/bert-mini,These models are supposed to be trained on a downstream task,prajjwal1/bert-mini,1.0,-1.0,NLP,d524d2bf-0c8c-4e35-817b-27b8261b2166___bert-mini,HF
1864,dexhrestha/Nepali-DistilBERT,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,1.0,-1.0,NLP,7fd51b19-d759-4aaa-b0d6-e19deece4d25___Nepali-DistilBERT,HF
1865,google/t5-efficient-base-el6,"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,0.0603587012737989,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
1866,google/t5-efficient-small-nl22,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0650528483092784,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
1867,DemangeJeremy/4-sentiments-with-flaubert,"Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context",xlnet-large-cased,0.0655275955796241,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
1868,akdeniz27/deberta-v2-xlarge-cuad,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
1869,google/t5-efficient-tiny-ff12000,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0443243291229009,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
1870,IlyaGusev/rugpt3medium_sum_gazeta,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,0.0360158681869507,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
1871,indobenchmark/indobert-base-p1,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0200635095437367,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
1872,cammy/led-large-16384-arxiv-100-MDS,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
1873,google/t5-efficient-tiny-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,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
1874,mimicheng/codeparrot-ds,"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_8-12,0.0861716344952583,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
1875,google/t5-efficient-base-el8,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0735161863267421,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
1876,ductuan024/AimeLaw,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0767087563872337,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
1877,google/t5-efficient-base-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,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
1878,google/t5-efficient-base-nl4,Its original repository can be found here,tau/splinter-large-qass,0.0565262977033853,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
1879,TJMUCH/transcriptome-iseeek,As a consequence the model still has good performance in English language,Cedille/fr-boris,0.0796465575695037,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
1880,google/t5-efficient-small-nl22,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0705735869705676,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
1881,jnz/electra-ka,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0228772759437561,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
1882,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0368101578205823,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
1883,cammy/led-large-16384-arxiv-100-MDS,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,0.0673083737492561,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
1884,google/t5-efficient-large-nl12,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
1885,google/t5-efficient-tiny-nl6,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0669570919126271,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
1886,google/t5-efficient-base-kv256,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0007015131413937,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
1887,hfl/chinese-electra-small-ex-generator,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
1888,google/t5-efficient-xl-nl28,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0241428362205624,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
1889,ToToKr/kobigbird-bert-base-finetuned-klue,"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,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
1890,stas/t5-very-small-random,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.015253322198987,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
1891,uer/t5-v1_1-base-chinese-cluecorpussmall,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0567291723564267,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
1892,allenai/macaw-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,0.0457531741509835,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
1893,ncduy/phobert-large-finetuned-vietnamese_students_feedback,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0900291875004768,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
1894,rinna/japanese-gpt-1b,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0367129407823085,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
1895,google/t5-efficient-small-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,0.0610126983374357,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
1896,google/t5-efficient-large-nh2,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
1897,hf-internal-testing/tiny-random-rembert,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0857786536216735,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
1898,google/t5-efficient-base-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,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
1899,google/t5-efficient-large-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,0.0068820929154753,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
1900,stas/t5-very-small-random,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0419093668460845,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
1901,aditeyabaral/sentencetransformer-distilbert-hinglish-big,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,0.078870989382267,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
1902,google/t5-efficient-large-kv16,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
1903,Palak/microsoft_deberta-base_squad,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0958988144993782,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
1904,stas/t5-very-small-random,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0490487143397331,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
1905,ebagdasa/propaganda_positive_bart_large,"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,0.0552330054342746,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
1906,google/t5-efficient-xl-nl6,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0493560917675495,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
1907,eli4s/Bert-L12-h240-A12,"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,0.0703600179404019,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
1908,microsoft/layoutlm-base-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,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
1909,google/t5-efficient-small-dm768,"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,0.0574751086533069,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
1910,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0773276984691619,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
1911,google/bert_uncased_L-6_H-256_A-4,They can be fine-tuned in the same manner as the original BERT models,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
1912,qarib/bert-base-qarib_far_6500k,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0310383045580237,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
1913,google/t5-efficient-base-ff1000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0143897673115134,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
1914,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0701933056116104,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
1915,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
1916,flax-community/bengali-t5-base,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
1917,KoichiYasuoka/roberta-base-ukrainian,This is a tiny random t5 model used for testingSee t5-make-very-small-model,stas/t5-very-small-random,0.087869606912136,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
1918,dhpollack/distilbert-dummy-sentiment,"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,0.0479441219940781,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
1919,amazon/bort,The models were optimized for the original T5 denoising target,allegro/plt5-large,0.0556470341980457,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
1920,cmarkea/distilcamembert-base-qa,"Indeed, inference cost can be a technological issue especially as in a context of cross-encoding like for this task",cmarkea/distilcamembert-base-qa,1.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
1921,TJMUCH/transcriptome-iseeek,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0531664192676544,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
1922,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,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,0.0996219962835311,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
1923,google/t5-efficient-base-el16,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
1924,zoha/wav2vec2-base-timit-demo-colab,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0326011143624782,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
1925,google/t5-efficient-small-el12,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0351942721754312,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
1926,google/t5-large-ssm-nq,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0380302096406618,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
1927,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,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-v2,1.0,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
1928,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,This does not make a very readable text,pere/DeUnCaser,0.0053820572793483,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
1929,google/t5-efficient-small-nl20,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0250870622694492,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
1930,sonoisa/byt5-small-japanese,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.033109501004219,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
1931,hajime9652/xlnet-japanese,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0720876008272171,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
1932,google/t5-efficient-mini-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,0.0711363442242145,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
1933,cammy/led-base-16384-100-MDS,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.062513418495655,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
1934,ctoraman/RoBERTa-TR-medium-char,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
1935,lgrobol/electra-minuscule-generator,"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-12,0.0625590980052947,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
1936,dhpollack/distilbert-dummy-sentiment,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0545763187110423,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
1937,tau/tavbert-he,T5-Efficient-TINY-FF3000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff3000,0.0331216137856244,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
1938,abdusahmbzuai/aradia-ctc-distilhubert-ft,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0236800443381071,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
1939,google/t5-efficient-large-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,0.0022681942209601,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
1940,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-EL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el16,0.0686960965394973,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
1941,amazon/bort,"Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example",facebook/m2m100-12B-avg-10-ckpt,0.0303254760801792,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
1942,google/t5-efficient-tiny-ff6000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0303169805556535,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
1943,Fujitsu/pytorrent,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0118631519532451,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
1944,j-hartmann/purchase-intention-english-roberta-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,0.0510510684301455,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
1945,IDEA-CCNL/Erlangshen-Longformer-110M,The extractive  summarization shortens the TandC to be further summarized by this model,ml6team/distilbart-tos-summarizer-tosdr,0.0537673067301511,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
1946,google/t5-efficient-base-kv16,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0350955985486507,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
1947,patrickvonplaten/unispeech-sat-large-timit-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,0.0905651450157165,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
1948,mrm8488/deberta-v3-small-finetuned-mrpc,"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,0.0739044100046157,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
1949,mwesner/reformer-clm,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0141595304012298,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
1950,negfir/distilbert-base-uncased-finetuned-cola,The model was created for selective question answering in Polish,enelpol/czywiesz-context,0.0686707273125648,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
1951,google/t5-efficient-base-ff6000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0056345951743424,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
1952,mikaelsouza/msft-regular-model,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0759364888072013,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
1953,crabz/distil-slovakbert-ner,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0806783884763717,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
1954,hfl/rbt3,The model achieves the following results on evaluation:,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0630303416401147,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
1955,google/t5-efficient-tiny-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,0.0019941590726375,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
1956,indobenchmark/indobert-base-p1,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0274534461398919,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
1957,hakurei/gpt-j-random-tinier,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0310577480122447,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
1958,google/t5-efficient-mini,"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,0.048742365092039,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
1959,allegro/plt5-large,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0389308035373687,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
1960,google/t5-efficient-base-ff1000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0348427183926105,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
1961,tau/tavbert-he,T5-Efficient-TINY-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl24,0.0356528693810105,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
1962,google/t5-efficient-large-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,0.0782100595533847,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
1963,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
1964,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
1965,superspray/electra_large_discriminator_squad2_custom_dataset,"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,0.0582175105810165,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
1966,beston91/gpt2-xl_ft_logits_10k,"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,0.0589017197489738,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
1967,gayanin/bart-mlm-pubmed-medterm,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0852387174963951,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
1968,google/t5-efficient-small-nl2,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0572760738432406,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
1969,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"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,0.083442635834217,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
1970,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"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,0.0092518776655197,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
1971,google/t5-efficient-small-dm768,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0773822255432605,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
1972,GKLMIP/electra-myanmar-small-uncased,Note: flaubert-small-cased is partially trained so performance is not guaranteed,flaubert/flaubert_small_cased,0.0672730430960655,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
1973,SetFit/deberta-v3-large__sst2__train-8-8,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0228457264602184,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
1974,piotr-rybak/poleval2021-task4-herbert-large-encoder,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0741717740893364,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
1975,google/t5-efficient-base-nl24,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0636152550578117,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
1976,sarnikowski/convbert-medium-small-da-cased,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0292859300971031,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
1977,skt/ko-gpt-trinity-1.2B-v0.5,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0385022806003689,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
1978,prajjwal1/bert-medium,"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,0.0620719935745,-1.0,NLP,34c0c0a8-7e6a-420b-9ebd-bb0cc78a7a25___bert-medium,HF
1979,negfir/distilbert-base-uncased-finetuned-cola,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.06256765127182,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
1980,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Source language: English,NYTK/translation-marianmt-en-hu,0.0038879611529409,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
1981,allenai/wmt19-de-en-6-6-big,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0788229703903197,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
1982,stas/pegasus-cnn_dailymail-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,0.0576690137386322,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
1983,google/t5-efficient-base-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-v1,0.0368974152952432,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
1984,google/t5-efficient-large-dm128,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0526169314980506,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
1985,diper1998/distilgpt2-finetuned-AT,"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,0.0968248546123504,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
1986,j-hartmann/purchase-intention-english-roberta-large,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0682600649694601,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
1987,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"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,0.0111832255497574,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
1988,flax-community/gpt2-medium-persian,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0067733253041903,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
1989,google/t5-efficient-base-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,0.049451932311058,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
1990,google/t5-efficient-tiny-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,0.0510728284716605,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
1991,google/t5-efficient-large-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,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
1992,google/t5-efficient-small-el32,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0955235734581947,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
1993,mnaylor/bigbird-base-mimic-mortality,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0208650147542357,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
1994,cammy/led-large-16384-arxiv-100-MDS,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0375423580408096,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
1995,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,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,0.0568519836912552,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
1996,clue/roberta_chinese_3L312_clue_tiny,"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,0.0167278479784726,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
1997,google/t5-efficient-base-ff9000,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0516401510685682,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
1998,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0399991460144519,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
1999,google/bert_uncased_L-8_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-8_H-128_A-2,1.0,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
2000,google/t5-efficient-xl-nl6,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.022170478478074,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
2001,google/t5-efficient-small-nl48,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0312510160729288,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
2002,ToToKr/kobigbird-bert-base-finetuned-klue,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0920781791210174,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
2003,gayanin/bart-mlm-pubmed-medterm,This model is a fine-tuned version of facebook/bart-base on an unknown dataset,gayanin/bart-mlm-pubmed-medterm,1.0,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
2004,flax-community/gpt2-medium-persian,You can use this model directly with a pipeline for text generation,flax-community/gpt2-medium-persian,1.0,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
2005,google/t5-efficient-large-nl16,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0437997216358781,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
2006,DemangeJeremy/4-sentiments-with-flaubert,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,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2007,google/t5-efficient-base-kv32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
2008,skt/ko-gpt-trinity-1.2B-v0.5,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.001299968920648,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
2009,gilparmentier/pokemon_gptj_model,"""GPT-J"" refers to the class of model, while ""6B"" represents the number of trainable parameters",gilparmentier/pokemon_gptj_model,1.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
2010,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
2011,sberbank-ai/rugpt3large_based_on_gpt2,"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,0.0036195940338075,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
2012,google/t5-efficient-large-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-v1,0.06528502702713,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
2013,rinna/japanese-gpt2-xsmall,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-v2,0.0755977208415666,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
2014,mwesner/reformer-clm,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.096789464354515,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
2015,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,It was introduced in this paper,readerbench/RoBERT-small,0.0870739817619323,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
2016,rinna/japanese-gpt2-xsmall,"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,0.0726489946246146,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
2017,negfir/distilbert-base-uncased-finetuned-cola,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0722614228725433,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
2018,kyryl0s/gpt2-uk-xxs,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,0.0952145121991634,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
2019,sonoisa/byt5-small-japanese,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0704105645418167,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
2020,Parsa/BBB_prediction_classification_SMILES,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0747156453629334,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
2021,DemangeJeremy/4-sentiments-with-flaubert,Google's mT5-small fine-tuned on TyDi QA (secondary task) for multingual QandA downstream task,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,0.0273939203470945,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2022,ToToKr/kobigbird-bert-base-finetuned-klue,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0569587685167789,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
2023,google/t5-efficient-tiny-ff6000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0162750054150819,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
2024,lgrobol/electra-minuscule-discriminator,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
2025,mwesner/bart-mlm,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
2026,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0152650782838463,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
2027,google/t5-efficient-small-nl22,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0560187455266713,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
2028,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
2029,google/t5-efficient-small-el32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0675462968647479,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
2030,diper1998/distilgpt2-finetuned-AT,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0691260918974876,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
2031,google/t5-efficient-small-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,0.0496165053918957,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
2032,macedonizer/al-roberta-base,"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,1.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
2033,anirudh21/xlnet-base-cased-finetuned-rte,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0565113425254821,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
2034,IDEA-CCNL/Erlangshen-Longformer-110M,"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,0.0605341531336307,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
2035,nyu-mll/roberta-med-small-1M-3,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
2036,skt/ko-gpt-trinity-1.2B-v0.5,"Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example",facebook/m2m100-12B-avg-10-ckpt,0.040046278387308,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
2037,tobiaslee/bert-2l-768h-uncased,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0757265165448188,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
2038,google/t5-efficient-small-el12,"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,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
2039,mwesner/bart-mlm,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.076432004570961,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
2040,1one5ome/gpt2-chinese-gulong,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.0517130829393863,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
2041,google/t5-efficient-large-nh32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0456839147955178,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
2042,google/t5-efficient-small-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-v1,0.068490620702505,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
2043,kyryl0s/gpt2-uk-xxs,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,0.0162233579903841,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
2044,google/t5-efficient-small-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,0.0414135791361331,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
2045,SetFit/deberta-v3-large__sst2__train-8-8,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0741311684250831,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
2046,amazon/bort,Pretrained T5 super-tiny language model for Malay,malay-huggingface/t5-super-tiny-bahasa-cased,0.0556870829313993,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
2047,akdeniz27/deberta-v2-xlarge-cuad,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0612552240490913,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
2048,google/t5-efficient-small-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,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
2049,DemangeJeremy/4-sentiments-with-flaubert,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,0.0805510357022285,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2050,uer/bart-chinese-4-768-cluecorpussmall,"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,0.0294469031505286,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
2051,mrm8488/xlm-multi-finetuned-xquadv1,"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,0.0863223373889922,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
2052,google/t5-efficient-large-el2,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,0.0934670083224773,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
2053,tucan9389/kcbert-base-finetuned-squad,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0673494189977645,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
2054,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-EL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el2,0.0279647707939147,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
2055,google/t5-efficient-large-dm512,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
2056,DemangeJeremy/4-sentiments-with-flaubert,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.044884193688631,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2057,NovelAI/genji-python-6B,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0195331076780955,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
2058,TJMUCH/transcriptome-iseeek,"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,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
2059,razent/SciFive-large-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,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
2060,TJMUCH/transcriptome-iseeek,ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-1.4B,0.093337632715702,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
2061,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,Its original repository can be found here,tau/splinter-large-qass,0.0955483168363571,-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
2062,google/t5-efficient-tiny-nh32,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0131046543829143,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
2063,google/t5-efficient-small-el32,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.006748829036951,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
2064,uer/chinese_roberta_L-2_H-256,"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,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
2065,microsoft/layoutlm-large-uncased,A ridiculously small ELECTRA generator model for testing purposes,lgrobol/electra-minuscule-generator,0.0969329848885536,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
2066,anas-awadalla/bert-tiny-finetuned-squad,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0895798057317733,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
2067,SetFit/deberta-v3-large__sst2__train-8-8,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0930303782224655,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
2068,mwesner/reformer-clm,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
2069,enelpol/czywiesz-context,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0545493268540927,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
2070,Iskaj/hf-challenge-test,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0790053978562355,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
2071,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0974587202072143,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
2072,google/t5-efficient-base-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-base-dm256,1.0,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
2073,stas/mt5-tiny-random,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0472448505461216,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
2074,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0724121704697609,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
2075,arm-on/BERTweet-FA,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0930308550596236,-1.0,NLP,36f660a4-b743-4ce6-8f2c-d814d940e2b3___BERTweet-FA,HF
2076,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.035802636295557,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
2077,thu-coai/LongLM-large,T5-Efficient-LARGE-NH2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh2,0.0223888070322572,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
2078,google/t5-efficient-mini-nl12,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0110409092158078,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
2079,google/t5-efficient-base-nl4,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0559692680835723,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
2080,allenai/wmt16-en-de-12-1,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.099292017519474,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
2081,google/t5-large-ssm-nq,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.032607396133244,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
2082,zoha/wav2vec2-base-timit-demo-colab,"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,0.0962580814957618,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
2083,google/t5-efficient-small-nl20,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0725424066185951,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
2084,KoichiYasuoka/roberta-base-ukrainian,"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,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
2085,google/t5-efficient-base-ff6000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0418583736754954,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
2086,google/t5-efficient-small-kv128,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0184531202539801,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
2087,tau/tavbert-he,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0289440564811229,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
2088,google/t5-efficient-large-kv256,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0392177123576402,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
2089,piotr-rybak/poleval2021-task4-herbert-large-encoder,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0951692461967468,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
2090,mldmm/GlassBERTa,"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,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
2091,abdusahmbzuai/ft-tatoeba-ar-en,"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,0.0587075948715209,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
2092,IDEA-CCNL/Erlangshen-Longformer-330M,"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,0.0065561681985855,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
2093,1one5ome/gpt2-chinese-gulong,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0693632438778877,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
2094,google/t5-efficient-xl-nl16,"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,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
2095,google/t5-efficient-xl-nl4,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0566721744835376,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
2096,ToToKr/kobigbird-bert-base-finetuned-klue,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0823611691594123,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
2097,pszemraj/led-large-book-summary,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
2098,google/t5-efficient-base-el16,"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-v1,0.0398682989180088,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
2099,saibo/random-roberta-mini,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,1.0,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
2100,aditeyabaral/sentencetransformer-bert-hinglish-small,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2101,thu-coai/LongLM-large,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0551527977610627,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
2102,anirudh21/xlnet-base-cased-finetuned-rte,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0585628114640712,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
2103,aditeyabaral/sentencetransformer-distilbert-hinglish-big,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.096954807639122,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
2104,razent/SciFive-large-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,0.0656134262681007,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
2105,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,0.0946246124804019,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
2106,ctoraman/RoBERTa-TR-medium-wp-66k,Its original repository can be found here,tau/splinter-large-qass,0.0110032320022583,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
2107,google/t5-efficient-base-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,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
2108,mmaguero/gn-bert-tiny-cased,"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,0.0900941006839275,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
2109,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0900313258171081,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
2110,akdeniz27/deberta-v2-xlarge-cuad,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0141926724463701,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
2111,minimaxir/magic-the-gathering,We train the model on a very large and heterogeneous French corpus,asi/gpt-fr-cased-base,0.0574624575674533,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
2112,IsaacBot/t5-small-finetuned-qa-google-en-answer_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,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
2113,google/t5-efficient-base-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,0.0859463401138782,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
2114,microsoft/layoutlm-base-uncased,org/abs/2202.08904The model was trained with the parameters:,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0798286348581314,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
2115,allenai/wmt16-en-de-12-1,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,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
2116,google/t5-efficient-tiny-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,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
2117,stas/t5-very-small-random,"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,0.0975521132349968,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
2118,google/t5-efficient-xl-nl4,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
2119,google/t5-efficient-base-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,0.0724490974098444,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
2120,uer/bart-chinese-4-768-cluecorpussmall,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.061330547556281,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
2121,hfl/rbtl3,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0120622366666793,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
2122,google/t5-efficient-small-dm256,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0506843468174338,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
2123,jhu-clsp/bibert-ende,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0408405934770901,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
2124,anirudh21/xlnet-base-cased-finetuned-rte,"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,0.0926229804754257,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
2125,google/t5-efficient-small-el64,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0580547954887151,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
2126,akdeniz27/deberta-v2-xlarge-cuad,The resulting clusters are used to replace the original characters in sentences for the coarse-grained pretraining of the MLM,microsoft/unihanlm-base,0.0903531163930893,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
2127,google/t5-efficient-base-nl48,Its original repository can be found here,tau/splinter-large-qass,0.0574584025889634,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
2128,malay-huggingface/t5-small-bahasa-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0497221210971474,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
2129,google/t5-efficient-large-nl10,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0275030955672264,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
2130,allenai/wmt16-en-de-12-1,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.060365155339241,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
2131,liaad/srl-pt_xlmr-base,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0663288366049527,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
2132,google/t5-efficient-base-el16,"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,0.0371524477377533,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
2133,google/t5-efficient-base-dm256,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0922789797186851,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
2134,hajime9652/xlnet-japanese,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
2135,google/t5-efficient-xl-nl12,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.075089868158102,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
2136,Milos/slovak-gpt-j-1.4B,"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,1.0,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
2137,hf-internal-testing/tiny-random-rembert,Vocabulary size is 384,ctoraman/RoBERTa-TR-medium-char,0.0999345183372497,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
2138,dbmdz/electra-base-turkish-cased-discriminator,ELECTRA is a new method for self-supervised language representation learning,dbmdz/electra-base-turkish-cased-discriminator,1.0,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
2139,google/t5-efficient-small-el32,"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,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
2140,ncduy/phobert-large-finetuned-vietnamese_students_feedback,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
2141,google/t5-efficient-mini-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,0.0762785635888576,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
2142,google/t5-efficient-tiny-nh32,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0350333657115697,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
2143,negfir/distilbert-base-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,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
2144,google/t5-efficient-large-nh8-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,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
2145,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0841632783412933,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
2146,google/t5-efficient-small-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,0.0483618341386317,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
2147,google/t5-efficient-small-ff1000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0229108249768614,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
2148,google/reformer-crime-and-punishment,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0035177012905478,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
2149,google/t5-efficient-small-ff9000,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0051049888134002,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
2150,google/t5-efficient-large-el8,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0741328299045562,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
2151,google/t5-efficient-small-kv256,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0357532147318124,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
2152,google/t5-efficient-xl-nl12,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.066771775484085,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
2153,google/t5-efficient-base-nl36,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0164731070399284,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
2154,GKLMIP/electra-myanmar-small-uncased,This does not make a very readable text,pere/DeUnCaser,0.0655920132994651,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
2155,hyunwoongko/reddit-9B,This does not make a very readable text,pere/DeUnCaser,0.0194872112146445,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
2156,google/t5-efficient-base-nl36,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0720914620906114,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
2157,google/t5-efficient-small-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,0.0643426254391669,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
2158,bolbolzaban/gpt2-persian,I have made no attempts to censor,bigjoedata/rockbot,0.022709229029715,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
2159,google/t5-efficient-base-dm256,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0450543826445936,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
2160,google/t5-efficient-base-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,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
2161,google/bert_uncased_L-12_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-12_H-256_A-4,1.0,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
2162,microsoft/layoutlm-base-cased,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.094200924038887,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
2163,aditeyabaral/sentencetransformer-distilbert-hinglish-big,A distilled version of ProtBert-UniRef100 model,yarongef/DistilProtBert,0.0869052410125732,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
2164,google/t5-efficient-base-dm256,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0076019819825888,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
2165,google/t5-efficient-small-el16-dl4,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0548321995884179,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
2166,allenai/macaw-answer-11b,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,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
2167,google/t5-efficient-base-el6,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0600331388413905,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
2168,google/t5-efficient-base-nh24,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.046439604833722,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
2169,khizon/distilbert-unreliable-news-eng-4L,"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-128,0.0661182403564452,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
2170,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NH2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh2,0.0232104361057281,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
2171,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0002495124936103,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
2172,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
2173,google/t5-efficient-base-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-base-ff6000,1.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
2174,jnz/electra-ka,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0605307035148143,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
2175,j-hartmann/purchase-intention-english-roberta-large,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.029963650740683,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
2176,mrm8488/xlm-multi-finetuned-xquadv1,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0839530862867832,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
2177,lgrobol/electra-minuscule-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,0.092359110713005,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
2178,tartuNLP/gpt-4-est-large,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0163617860525846,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
2179,stas/t5-very-small-random,"The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned",ctoraman/RoBERTa-TR-medium-wp-66k,0.0855910405516624,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
2180,everdoubling/byt5-Korean-small,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,0.0331035293638705,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
2181,google/t5-efficient-base-ff1000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0102065261453389,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
2182,Palak/microsoft_deberta-base_squad,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
2183,google/t5-efficient-base-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,0.014837734401226,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
2184,mimicheng/codeparrot-ds,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0691567957401275,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
2185,razent/SciFive-large-Pubmed,"Therefore, this model does not need a tokenizer",google/reformer-enwik8,0.0689036212861537,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
2186,mikaelsouza/msft-regular-model,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0829998701810836,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
2187,google/t5-efficient-large-dm128,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0070646358653903,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
2188,dhpollack/distilbert-dummy-sentiment,"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,0.0450765267014503,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
2189,google/t5-efficient-mini-nl8,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0626153517514467,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
2190,KoichiYasuoka/roberta-base-ukrainian,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,0.076933279633522,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
2191,google/t5-efficient-small-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,0.0386808216571807,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
2192,stas/mt5-tiny-random,Portuguese (Brazil) model for text inference,rdenadai/BR_BERTo,0.0573555640876293,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
2193,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm128,0.0030998438596725,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2194,google/t5-efficient-base-el6,"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-el6,1.0,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
2195,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
2196,google/t5-efficient-small-el16-dl4,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0631239637732505,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
2197,google/t5-efficient-base-el8,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0652356147766112,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
2198,google/t5-efficient-small-nl48,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0564192216843366,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
2199,UWB-AIR/MQDD-duplicates,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,1.0,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
2200,tau/tavbert-he,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,0.0355639196932315,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
2201,skt/ko-gpt-trinity-1.2B-v0.5,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
2202,google/t5-efficient-large-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,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
2203,allenai/macaw-3b,"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,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
2204,TJMUCH/transcriptome-iseeek,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0713732093572616,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
2205,google/t5-efficient-base-ff1000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
2206,IDEA-CCNL/Zhouwenwang-Unified-110M,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0454916736111044,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
2207,google/t5-efficient-large-dm128,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0176374344155192,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
2208,celine98/canine-s-finetuned-sst2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
2209,uer/t5-v1_1-base-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-base-chinese-cluecorpussmall,1.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
2210,google/t5-efficient-large-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,0.0634597819298505,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
2211,uer/t5-v1_1-small-chinese-cluecorpussmall,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0176196414977312,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
2212,beston91/gpt2-xl_ft_logits_10k,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.089180402457714,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
2213,tau/splinter-base-qass,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,1.0,-1.0,NLP,f68d3e26-2d96-4b99-a309-34b532e9740f___splinter-base-qass,HF
2214,google/t5-efficient-small-nl22,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0546674113720655,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
2215,hajime9652/xlnet-japanese,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0085310600697994,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
2216,wukevin/tcr-bert,See our full codebase and our preprint for more information,wukevin/tcr-bert,1.0,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
2217,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0402630573759476,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
2218,patrickvonplaten/unispeech-sat-large-timit-ft,"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,0.0533666461706161,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
2219,google/t5-efficient-base-nl48,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0320887463167309,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
2220,pszemraj/led-large-book-summary,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0691738883033394,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
2221,malay-huggingface/t5-small-bahasa-cased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0077541222174961,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
2222,mldmm/GlassBERTa,"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,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
2223,google/t5-efficient-base-el6,"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,0.0485360417515039,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
2224,google/t5-efficient-large-nh4,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0398772968910634,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
2225,Davlan/m2m100_418M-eng-yor-mt,This may not generalize well for all use cases in different domains,Davlan/m2m100_418M-eng-yor-mt,1.0,-1.0,NLP,22200502-47bf-477d-b7e3-dec71a3aae90___m2m100_418M-eng-yor-mt,HF
2226,google/t5-efficient-small-el64,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
2227,CuongLD/wav2vec2-large-xlsr-vietnamese,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0905395932495593,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
2228,microsoft/layoutlm-base-uncased,This model is cased: it makes a difference between english and English,google/fnet-large,0.0685319453477859,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
2229,ml6team/distilbart-tos-summarizer-tosdr,"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,1.0,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
2230,dbmdz/bert-medium-historic-multilingual-cased,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0201536007225513,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
2231,google/t5-efficient-small-el32,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0713996291160583,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
2232,google/t5-efficient-small-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,0.0144691355526446,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
2233,Milos/slovak-gpt-j-405M,This is a ported version of  S3PRL's Hubert for the SUPERB Emotion Recognition task,superb/hubert-base-superb-er,0.050040878355503,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
2234,tartuNLP/gpt-4-est-large,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0502508878707885,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
2235,negfir/distilbert-base-uncased-finetuned-cola,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0939598307013511,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
2236,google/t5-efficient-small-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,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
2237,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0255398824810981,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
2238,nghuyong/ernie-1.0,"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,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
2239,Milos/slovak-gpt-j-1.4B,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0148277133703231,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
2240,mgreenbe/bertlet-base-uncased-for-sequence-classification,"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,0.0365783274173736,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
2241,saibo/random-roberta-tiny,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0452763428911566,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
2242,google/t5-efficient-large-nl12,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,0.0834228321909904,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
2243,google/t5-efficient-tiny-ff12000,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0208183843642472,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
2244,google/t5-efficient-small-nl36,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0021912269294262,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
2245,google/t5-efficient-large-el6,"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,0.0576884895563125,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
2246,bolbolzaban/gpt2-persian,You can use this model directly with a pipeline for text generation:,bolbolzaban/gpt2-persian,1.0,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
2247,google/t5-efficient-large-nh4,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0678931809961795,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
2248,google/t5-efficient-small-el4,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0091800689697265,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
2249,google/t5-efficient-small-ff3000,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0538571756333112,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
2250,nghuyong/ernie-tiny,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0703256179889042,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
2251,ToToKr/kobigbird-bert-base-finetuned-klue,"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,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
2252,suzuki/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,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
2253,rdenadai/BR_BERTo,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0230647226174671,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
2254,obss/mt5-base-3task-highlight-tquad2,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0495990286581218,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
2255,google/t5-efficient-large-nh24,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
2256,jean-paul/kinyaRoberta-small,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,1.0,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
2257,razent/SciFive-base-PMC,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0619559623301028,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
2258,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
2259,mwesner/bart-mlm,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0433531068265438,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
2260,malay-huggingface/bert-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,0.0332511821761727,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
2261,google/t5-large-ssm-nq,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,073618da-2eda-4c49-b66a-8dcae7a9a204___t5-large-ssm-nq,HF
2262,allenai/wmt19-de-en-6-6-base,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0307338014245033,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
2263,hf-internal-testing/tiny-random-rembert,"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,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
2264,beston91/gpt2-xl_ft_logits_10k,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0239913556724786,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
2265,google/t5-efficient-small-ff9000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.063605286180973,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
2266,CuongLD/wav2vec2-large-xlsr-vietnamese,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0433581657707691,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
2267,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0493945777416229,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
2268,google/t5-efficient-large-el6,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0593362059444188,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
2269,google/t5-efficient-large-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,0.0390353463590144,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
2270,google/t5-efficient-small-ff3000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0567916743457316,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
2271,Milos/slovak-gpt-j-405M,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
2272,castorini/afriberta_large,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0624619219452142,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
2273,mwesner/bart-mlm,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
2274,ian0delond/model-test,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0573607683181762,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
2275,GKLMIP/electra-myanmar-small-uncased,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,1.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
2276,Milos/slovak-gpt-j-405M,Model is based on GPT-J and has over 405M trainable parameters,Milos/slovak-gpt-j-405M,1.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
2277,IlyaGusev/rugpt3medium_sum_gazeta,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0639069043099879,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
2278,sentence-transformers/paraphrase-MiniLM-L3-v2,This is a converted pytorch checkpoint for bert with 2L trained from scratch,tobiaslee/bert-2l-768h-uncased,0.0579066127538681,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
2279,stas/mt5-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,0.0123255467042326,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
2280,cammy/led-base-16384-100-MDS,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,0.0954364687204361,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
2281,KoichiYasuoka/roberta-small-belarusian,For more details please refer to the official website,flaubert/flaubert_small_cased,0.088999718427658,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
2282,google/t5-efficient-large-el6,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0667477212846279,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
2283,pulp/CHILDES-ParentBERTo,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,0.037120722234249,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
2284,GKLMIP/electra-myanmar-small-uncased,"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,0.0206828545778989,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
2285,fnlp/bart-large-chinese,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.0818106308579444,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
2286,google/t5-efficient-base-nh16,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
2287,ptaszynski/yacis-electra-small-japanese,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0588715383782982,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
2288,stas/t5-very-small-random,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-xquadv1,0.0861158818006515,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
2289,1one5ome/gpt2-chinese-gulong,"We started training from GPT-J, which has been trained on The Pile",Cedille/fr-boris,0.0579007305204868,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
2290,sberbank-ai/rugpt3large_based_on_gpt2,Source language: English,NYTK/translation-marianmt-en-hu,0.031612985767424,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
2291,stas/pegasus-cnn_dailymail-tiny-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,0.0347750820219516,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
2292,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Amazon's BORT,amazon/bort,0.0753892958164215,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
2293,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,It was introduced in this paper,readerbench/RoBERT-small,0.0739009380340576,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
2294,google/t5-efficient-small-el16-dl4,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0864571742713451,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
2295,google/t5-efficient-large-kv16,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0445075500756501,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
2296,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
2297,TJMUCH/transcriptome-iseeek,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,0.0569650717079639,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
2298,google/t5-efficient-small-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,0.0373966721817851,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
2299,aditeyabaral/sentencetransformer-bert-hinglish-small,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2300,cammy/led-base-16384-100-MDS,Source language: English,NYTK/translation-marianmt-en-hu,0.0892053544521331,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
2301,rahulacj/bertweet-base-finetuned-sentiment-analysis,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.037026323378086,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
2302,negfir/distilbert-base-uncased-finetuned-cola,"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,0.0267461668699979,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
2303,google/t5-efficient-small-ff1000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0567122213542461,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
2304,google/t5-efficient-base-dm512,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0855990871787071,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
2305,rinna/japanese-gpt-1b,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0118374923865,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
2306,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"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,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
2307,gayanin/bart-mlm-pubmed-medterm,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0268413536250591,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
2308,phueb/BabyBERTa-2,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,1.0,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
2309,google/t5-efficient-large-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,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
2310,crabz/distil-slovakbert-ner,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0780569091439247,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
2311,flax-community/bengali-t5-base,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.048020047135651,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
2312,negfir/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,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
2313,TJMUCH/transcriptome-iseeek,This model is cased: it does make a difference between bulgarian and Bulgarian,rmihaylov/bert-base-pos-theseus-bg,0.0031892713159322,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
2314,google/t5-efficient-base-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,0.0072139874100685,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
2315,allenai/wmt19-de-en-6-6-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,0.0396700836718082,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
2316,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0384843870997428,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
2317,izumi-lab/bert-small-japanese-fin,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
2318,thu-coai/LongLM-large,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,0.056244027800858,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
2319,google/t5-efficient-base-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,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
2320,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0826466232538223,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
2321,google/t5-efficient-base-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,0.0149267949163913,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
2322,negfir/distilbert-base-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-12,0.0918863862752914,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
2323,allenai/wmt16-en-de-12-1,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,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
2324,google/t5-efficient-small-nl20,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.015495365485549,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
2325,mimicheng/codeparrot-ds,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0755371451377868,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
2326,google/t5-efficient-large-nh8,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0918357782065868,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
2327,kssteven/ibert-roberta-large,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,1.0,-1.0,NLP,c6203cc9-139b-4971-98f4-6cf175a33c61___ibert-roberta-large,HF
2328,cammy/led-large-16384-arxiv-100-MDS,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0771444141864776,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
2329,andrejmiscic/simcls-scorer-billsum,"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,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
2330,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,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,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
2331,SupriyaArun/squeezebert-uncased-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-12,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
2332,DeepPavlov/distilrubert-tiny-cased-conversational,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,0.0323646720498799,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
2333,tartuNLP/gpt-4-est-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,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
2334,tau/tavbert-he,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,0.06852388381958,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
2335,CuongLD/wav2vec2-large-xlsr-vietnamese,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0382792372256517,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
2336,aditeyabaral/sentencetransformer-bert-hinglish-small,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0185706317424774,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2337,ToToKr/kobigbird-bert-base-finetuned-klue,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
2338,IlyaGusev/rugpt3medium_sum_gazeta,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
2339,groar/gpt-neo-1.3B-finetuned-escape2,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0287320129573345,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
2340,google/t5-efficient-small-ff3000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0341399498283862,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
2341,DemangeJeremy/4-sentiments-with-flaubert,"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,0.0309882480651139,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2342,hfl/rbtl3,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0669432021677493,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
2343,danyaljj/opengpt2_pytorch_forward,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0626345674196878,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
2344,minimaxir/magic-the-gathering,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0075587471947073,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
2345,google/t5-efficient-small-nl48,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0073972027748823,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
2346,google/t5-efficient-xl-nl6,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.015229009091854,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
2347,kyryl0s/gpt2-uk-xxs,"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,0.0666185393929481,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
2348,google/t5-efficient-large-nl10,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
2349,google/t5-efficient-tiny-nh16,"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,0.0548332165926694,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
2350,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0756133794784545,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
2351,crabz/distil-slovakbert-ner,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0477619729936122,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
2352,google/t5-efficient-small-el4,This does not make a very readable text,pere/DeUnCaser,0.0593538712710141,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
2353,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"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,0.030920960009098,-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
2354,uer/t5-v1_1-base-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,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
2355,google/t5-efficient-tiny-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,0.0425630044192075,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
2356,brad1141/Longformer-finetuned-norm,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
2357,mgreenbe/bertlet-base-uncased-for-sequence-classification,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0931573882699012,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
2358,mimi/Waynehills-NLP-doogie,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
2359,google/t5-efficient-base-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,0.0650713276118039,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
2360,ffsouza/t5-tiny-random-length-96-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,0.079949676990509,-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
2361,tucan9389/kcbert-base-finetuned-squad,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0977986752986908,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
2362,google/t5-efficient-large-nl10,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.002873906865716,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
2363,KoichiYasuoka/roberta-base-ukrainian,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0414488315582275,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
2364,nguyenvulebinh/envibert,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0137822261701027,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
2365,anirudh21/xlnet-base-cased-finetuned-rte,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0864781141281127,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
2366,google/t5-efficient-large-nh8,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
2367,google/t5-efficient-tiny-nh16,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
2368,ian0delond/model-test,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,0.0705952942371368,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
2369,Iskaj/hf-challenge-test,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0777249783277511,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
2370,google/t5-efficient-small-nl2,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
2371,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
2372,celine98/canine-s-finetuned-sst2,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,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
2373,negfir/distilbert-base-uncased-finetuned-cola,"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,0.0977545082569122,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
2374,ErykWdowiak/GPTalian,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.0484491769224405,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
2375,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"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,0.0044893231242895,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
2376,KoichiYasuoka/roberta-base-ukrainian,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0676579475402832,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
2377,ductuan024/AimeLaw,Its original repository can be found here,tau/splinter-large-qass,0.0154478400945662,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
2378,google/t5-efficient-large-nh2,"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,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
2379,fnlp/bart-large-chinese,"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,0.0166056845337152,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
2380,microsoft/layoutlm-base-uncased,This model is uncased: it does not make a difference between english and English,funnel-transformer/small,0.0583458803594112,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
2381,google/t5-efficient-mini,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0727303847670554,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
2382,google/t5-efficient-base-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,0.025244178250432,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
2383,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,""" 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,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
2384,mrm8488/deberta-v3-small-finetuned-mrpc,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,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
2385,SupriyaArun/squeezebert-uncased-finetuned-squad,For more details please refer to the official website,flaubert/flaubert_small_cased,0.061366893351078,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
2386,google/t5-efficient-tiny-ff12000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0280919400975108,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
2387,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-BASE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el6,0.0719702094793319,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
2388,google/t5-efficient-tiny-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,0.0716388635337351,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
2389,google/t5-efficient-tiny-nl16,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0715554989874362,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
2390,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0363449100404977,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
2391,mgreenbe/bertlet-base-uncased-for-sequence-classification,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
2392,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"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,0.0881867259740829,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
2393,google/t5-efficient-large-nh4,"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,0.0597556084394454,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
2394,google/t5-efficient-base-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,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
2395,google/t5-efficient-large-kv16,This does not make a very readable text,pere/DeUnCaser,0.0424959333613514,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
2396,aubmindlab/aragpt2-large,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
2397,allenai/macaw-answer-11b,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
2398,google/t5-efficient-small-ff9000,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0823593214154242,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
2399,liaad/srl-pt_bertimbau-large,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.041697919058303,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
2400,diper1998/distilgpt2-finetuned-AT,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
2401,google/t5-efficient-small-el64,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0344958025962114,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
2402,google/t5-efficient-xl-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,0.0448646359145641,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
2403,Norod78/hebrew_poetry-gpt_neo-tiny,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
2404,google/reformer-crime-and-punishment,"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,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
2405,hfl/chinese-electra-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,0.0067819966934621,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
2406,microsoft/deberta-v2-xxlarge-mnli,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0496916128322481,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
2407,stas/mt5-tiny-random,Pretrained model on English language using a similar objective objective as ELECTRA,funnel-transformer/small,0.0881856903433799,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
2408,google/t5-efficient-large-nl20,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0396171733736991,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
2409,google/t5-efficient-tiny-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,0.0366125851869582,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
2410,google/t5-efficient-large-dm256,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,0.0937796831130981,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
2411,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0839559435844421,-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
2412,uer/t5-v1_1-small-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,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
2413,google/t5-efficient-small-kv16,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0338015491142869,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
2414,mldmm/GlassBERTa,"Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan BaskaranWork done via MLDMM Lab",mldmm/GlassBERTa,1.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
2415,google/t5-efficient-small-nl40,This does not make a very readable text,pere/DeUnCaser,0.0540173146873712,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
2416,qarib/bert-base-qarib_far_6500k,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0271901786327362,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
2417,google/t5-efficient-base-nh24,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0142433270812034,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
2418,microsoft/unispeech-sat-base,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
2419,google/byt5-xl,Amazon's BORT,amazon/bort,0.0449937535449862,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
2420,ian0delond/model-test,"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,0.0671131387352943,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
2421,google/t5-efficient-small-el64,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0773963779211044,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
2422,google/t5-efficient-large-dm512,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0511589217931031,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
2423,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"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,0.0146482400596141,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
2424,skt/ko-gpt-trinity-1.2B-v0.5,"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,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
2425,google/t5-efficient-tiny-ff9000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0197542533278465,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
2426,diper1998/distilgpt2-finetuned-AT,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0945292860269546,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
2427,MoritzLaurer/MiniLM-L6-mnli,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0360615209986765,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
2428,aubmindlab/aragpt2-large,"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,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
2429,aubmindlab/aragpt2-large,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0283062849193811,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
2430,uer/chinese_roberta_L-2_H-256,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0001833507791161,-1.0,NLP,e3bf3cf0-5561-4cae-bfb5-74fbcb4e43e9___chinese_roberta_L-2_H-256,HF
2431,google/t5-efficient-small-nl22,"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,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
2432,Palak/microsoft_deberta-base_squad,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0622716769576072,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
2433,google/t5-efficient-base-kv32,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0600439328700303,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
2434,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.074721410870552,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
2435,KoichiYasuoka/roberta-base-ukrainian,Amazon's BORT,amazon/bort,0.0845360606908798,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
2436,uer/t5-v1_1-small-chinese-cluecorpussmall,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0956140160560607,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
2437,google/t5-efficient-large-nh24,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,0.0621307026594877,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
2438,eli4s/Bert-L12-h256-A4,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0412309821695088,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
2439,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
2440,aditeyabaral/sentencetransformer-bert-hinglish-small,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,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2441,google/t5-efficient-tiny-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,0.0485132969915866,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
2442,mrp/simcse-model-wangchanberta,"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,0.0403466522693633,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
2443,google/t5-efficient-xl-nl12,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0199349978938699,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
2444,google/t5-efficient-small-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,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
2445,cammy/led-base-16384-100-MDS,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0149034969508647,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
2446,google/t5-efficient-base-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,0.0630186721682548,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
2447,Cedille/fr-boris,"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,0.0296130627393722,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
2448,celine98/canine-s-finetuned-sst2,"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,0.0705663934350013,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
2449,negfir/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,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
2450,google/t5-efficient-base-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,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
2451,google/t5-efficient-large-el8,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0816057883203029,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
2452,google/t5-efficient-xl-nl4,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,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
2453,google/t5-efficient-base-nl32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0360496919602155,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
2454,google/t5-efficient-small-dm1000,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,1.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
2455,krevas/finance-koelectra-base-discriminator,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0247550131753087,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
2456,sentence-transformers/paraphrase-MiniLM-L3-v2,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
2457,VietAI/gpt-neo-1.3B-vietnamese-news,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0102271232753992,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
2458,ian0delond/model-test,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0988881140947341,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
2459,diper1998/distilgpt2-finetuned-AT,"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,0.024917734786868,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
2460,google/t5-efficient-large-el8,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0414489973336458,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
2461,google/t5-efficient-base-kv32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0348970405757426,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
2462,google/t5-efficient-large-dm128,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0131566822528839,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
2463,VietAI/gpt-neo-1.3B-vietnamese-news,"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,0.0583415757864713,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
2464,microsoft/unihanlm-base,"To better utilize the shared knowledge across the languages, we propose UnihanLM, a self-supervised Chinese-Japanese pretrained masked language model (MLM) with a novel two-stage coarse-to-fine training approach",microsoft/unihanlm-base,1.0,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
2465,google/t5-efficient-small-dm1000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.023162271361798,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
2466,clue/roberta_chinese_3L312_clue_tiny,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
2467,aliosm/ComVE-gpt2-large,"Since it has 4 attention heads, the head size is 64 just as for the BERT base model",eli4s/Bert-L12-h256-A4,0.0213028099387884,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
2468,google/t5-efficient-xl-nl16,Its original repository can be found here,tau/splinter-large-qass,0.0602165721356868,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
2469,uer/t5-v1_1-small-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,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
2470,google/t5-efficient-base-ff1000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0534068420529365,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
2471,stas/pegasus-cnn_dailymail-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,0.0477230381220579,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
2472,fnlp/bart-base-chinese,"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,0.0213234927505255,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
2473,stas/t5-very-small-random,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0414752624928951,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
2474,arm-on/BERTweet-FA,BERTweet-FA is a transformer-based model trained on 20665964 Persian tweets,arm-on/BERTweet-FA,1.0,-1.0,NLP,36f660a4-b743-4ce6-8f2c-d814d940e2b3___BERTweet-FA,HF
2475,google/t5-efficient-small-el32,"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,0.0392126189544797,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
2476,google/t5-efficient-small-nl2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0345933055505156,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
2477,jonatasgrosman/wav2vec2-xls-r-1b-english,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0163931089142958,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
2478,google/t5-efficient-small-el32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0388671346008777,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
2479,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0608538417145609,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
2480,hfl/chinese-electra-180g-small-ex-discriminator,"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,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
2481,suzuki/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,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
2482,1one5ome/gpt2-chinese-gulong,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0685161128640174,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
2483,mikaelsouza/msft-regular-model,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0345990471541881,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
2484,fnlp/bart-base-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,0.0287534501403569,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
2485,beston91/gpt2-xl_ft_logits_10k,"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,0.000724971294403,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
2486,microsoft/layoutlm-base-uncased,LayoutLM archives the SOTA results on multiple datasets,microsoft/layoutlm-base-uncased,1.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
2487,KoichiYasuoka/roberta-base-ukrainian,"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,0.0969343557953834,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
2488,hajime9652/xlnet-japanese,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0537296906113624,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
2489,google/t5-efficient-tiny-nh8,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.022734098136425,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
2490,cross-encoder/ms-marco-MiniLM-L-4-v2,This model was trained on the MS Marco Passage Ranking task,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
2491,VietAI/gpt-neo-1.3B-vietnamese-news,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0473110452294349,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
2492,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0663333758711814,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
2493,TJMUCH/transcriptome-iseeek,A distilled version of ProtBert-UniRef100 model,yarongef/DistilProtBert,0.0736525356769561,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
2494,google/t5-efficient-large-nh4,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0380212925374507,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
2495,uer/t5-v1_1-small-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,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
2496,minimaxir/magic-the-gathering,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0722292624413966,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
2497,google/t5-efficient-base-nl2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
2498,oskrmiguel/mt5-simplification-spanish,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0207142084836959,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
2499,hfl/chinese-electra-small-ex-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,0.0227126786485314,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
2500,google/t5-efficient-base-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,0.0357033191248774,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
2501,IDEA-CCNL/Erlangshen-Longformer-330M,"Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes",mnaylor/bigbird-base-mimic-mortality,0.0137831419706344,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
2502,mimi/Waynehills-NLP-doogie,"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,0.0752500742673873,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
2503,jhu-clsp/bibert-ende,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,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
2504,google/t5-efficient-base-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,0.0519424844533204,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
2505,google/t5-efficient-small-dm2000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
2506,google/t5-efficient-small-nl22,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0605262480676173,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
2507,kssteven/ibert-roberta-base,", to TensorRT) for integer-only deployment of the model",kssteven/ibert-roberta-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
2508,neuralspace-reverie/indic-transformers-bn-xlmroberta,The h5 file for Tensorflow has been generated manually by commands suggested here,neuralspace-reverie/indic-transformers-bn-xlmroberta,1.0,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
2509,mnaylor/bigbird-base-mimic-mortality,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0294253872707485,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
2510,google/t5-efficient-large-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,0.047426477074623,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
2511,beston91/gpt2-xl_ft_logits_10k,"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,0.005756520666182,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
2512,microsoft/layoutlm-base-cased,"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,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
2513,google/t5-efficient-mini-nl24,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0649226605892181,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
2514,aditeyabaral/sentencetransformer-distilbert-hinglish-big,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
2515,mrm8488/bert-small-finetuned-squadv2,"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,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
2516,google/t5-efficient-base-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-128,0.0710598304867743,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
2517,jnz/electra-ka,FNet is a transformers model with attention replaced with fourier transforms,google/fnet-large,0.0810825005173683,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
2518,beston91/gpt2-xl_ft_logits_10k,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
2519,ian0delond/model-test,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0793102532625198,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
2520,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh16,0.0629502311348915,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
2521,KoichiYasuoka/roberta-small-belarusian,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
2522,google/t5-efficient-xl-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,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
2523,jkeruotis/LitBERTa-uncased,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0720919556915759,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
2524,amazon/bort,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0328437257558106,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
2525,google/t5-efficient-tiny-nl12,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0662512108683585,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
2526,google/t5-efficient-base-nl24,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
2527,google/bert_uncased_L-6_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-6_H-256_A-4,1.0,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
2528,google/t5-efficient-xl-nl12,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0115937152877449,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
2529,google/t5-efficient-tiny-ff9000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0256225373595952,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
2530,google/t5-efficient-tiny-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,0.0694887228310107,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
2531,google/t5-efficient-base-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,0.0640446338802575,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
2532,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,0.0595441274344921,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
2533,allenai/macaw-3b,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,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
2534,nielsr/coref-roberta-large,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0083716495428234,-1.0,NLP,a42c9bdb-be23-4bfd-8add-30afe8d1724a___coref-roberta-large,HF
2535,google/t5-efficient-large-el6,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0863991379737853,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
2536,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"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,0.0694435536861419,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
2537,TJMUCH/transcriptome-iseeek,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
2538,google/t5-efficient-base-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,0.0501202661544084,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
2539,google/t5-efficient-small-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,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
2540,DeepPavlov/distilrubert-tiny-cased-conversational-v1,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0641344557516276,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
2541,zoha/wav2vec2-base-timit-demo-colab,"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,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
2542,google/t5-efficient-large-nh4,"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-nh4,1.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
2543,google/t5-efficient-small-el32,"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,0.0093739852309226,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
2544,mrm8488/mobilebert-uncased-finetuned-squadv2,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0861119925975799,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
2545,diper1998/distilgpt2-finetuned-AT,"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,0.0395840220153331,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
2546,google/t5-efficient-large-nl16,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
2547,anirudh21/xlnet-base-cased-finetuned-rte,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0638524144887924,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
2548,microsoft/layoutlm-base-cased,The model was created for selective question answering in Polish,enelpol/czywiesz-context,0.0969980135560035,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
2549,google/t5-efficient-small-nl20,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
2550,google/t5-efficient-xl-nl6,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0747220888733863,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
2551,wukevin/tcr-bert,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0268207751214504,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
2552,malay-huggingface/t5-super-tiny-bahasa-cased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0028843749314546,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
2553,microsoft/layoutlm-base-uncased,It can be considered as small copy of Conversational DistilRuBERT-base,DeepPavlov/distilrubert-tiny-cased-conversational,0.0654938891530036,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
2554,google/t5-efficient-large-el6,"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,0.0577675811946391,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
2555,minimaxir/magic-the-gathering,"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,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
2556,mimicheng/codeparrot-ds,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
2557,google/t5-efficient-large-nh8-nl32,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0715397130697965,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
2558,hf-internal-testing/tiny-random-rembert,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0038281064480543,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
2559,google/t5-efficient-small-el4,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0585181266069411,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
2560,google/t5-efficient-base-nl36,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0695124398916959,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
2561,malay-huggingface/bert-tiny-bahasa-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,0.0289022748668988,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
2562,uer/t5-v1_1-base-chinese-cluecorpussmall,This does not make a very readable text,pere/DeUnCaser,0.0527920434251426,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
2563,google/t5-efficient-small-kv256,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0508887227624653,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
2564,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv256,0.0411826204508542,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
2565,abdusahmbzuai/ft-tatoeba-ar-en,"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,0.0908410549163818,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
2566,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,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,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
2567,google/t5-efficient-small-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,0.0500922128558158,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
2568,aditeyabaral/sentencetransformer-bert-hinglish-small,This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset,groar/gpt-neo-1.3B-finetuned-escape2,0.0916589349508285,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2569,dbmdz/bert-medium-historic-multilingual-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,0.0495001822710036,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
2570,TJMUCH/transcriptome-iseeek,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0389896929264068,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
2571,google/t5-efficient-tiny-nl2,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0814891159534454,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
2572,google/t5-efficient-large-kv16,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.059201082214713,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
2573,google/t5-efficient-mini-nl12,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0281361062079668,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
2574,Iskaj/hf-challenge-test,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
2575,amazon/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,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
2576,dhpollack/distilbert-dummy-sentiment,"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,0.063591007143259,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
2577,eli4s/Bert-L12-h384-A6,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0693787212173143,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
2578,google/t5-efficient-tiny-nh16,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
2579,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-DM128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm128,0.056236612300078,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
2580,microsoft/layoutlm-base-cased,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
2581,uer/t5-v1_1-small-chinese-cluecorpussmall,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
2582,google/t5-efficient-tiny-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,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
2583,google/t5-efficient-base-el8,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,4.739314317705329e-05,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
2584,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
2585,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.0020847953855991,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2586,google/t5-efficient-small-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,0.0286365910433233,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
2587,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm768,0.075419645756483,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
2588,google/t5-efficient-large-kv128,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
2589,mrm8488/deberta-v3-small-finetuned-mrpc,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0837578698992729,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
2590,ian0delond/model-test,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0991539657115936,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
2591,allenai/macaw-3b,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0295158003767331,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
2592,uer/t5-v1_1-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,0.0138246817514299,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
2593,tartuNLP/gpt-4-est-large,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
2594,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0022395066916942,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
2595,google/t5-efficient-small-kv32,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.044731680303812,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
2596,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,0.084997147321701,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
2597,sonoisa/byt5-small-japanese,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0818754062056541,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
2598,allenai/wmt16-en-de-dist-6-1,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0697445645928382,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
2599,mikaelsouza/msft-regular-model,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0915164276957511,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
2600,suzuki/distilbert-base-uncased-finetuned-squad,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0751318037509918,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
2601,microsoft/layoutlm-base-cased,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0411167070269584,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
2602,negfir/distilbert-base-uncased-finetuned-cola,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0516004711389541,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
2603,uer/pegasus-base-chinese-cluecorpussmall,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.041494925506413,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
2604,google/t5-efficient-tiny-nh1,"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,0.0495637841522693,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
2605,google/t5-efficient-xl-nl2,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.045322474092245,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
2606,minimaxir/magic-the-gathering,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
2607,IDEA-CCNL/Erlangshen-Longformer-330M,There are also BiLSTM models available as well as these two models in 'https://github,Parsa/BBB_prediction_classification_SMILES,0.0618789847940206,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
2608,gchhablani/fnet-base-finetuned-wnli,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0829425528645515,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
2609,SetFit/deberta-v3-large__sst2__train-8-8,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0983926057815551,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
2610,tartuNLP/gpt-4-est-large,We release a fine-tuned version of our MQDD model for duplicate detection task,UWB-AIR/MQDD-duplicates,0.0497728809714317,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
2611,google/t5-efficient-small-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,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
2612,prajjwal1/bert-medium,These models are supposed to be trained on a downstream task,prajjwal1/bert-medium,1.0,-1.0,NLP,34c0c0a8-7e6a-420b-9ebd-bb0cc78a7a25___bert-medium,HF
2613,google/t5-efficient-tiny-el8,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
2614,beston91/gpt2-xl_ft_logits_10k,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0999257415533065,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
2615,amazon/bort,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,0.0431382665410637,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
2616,aditeyabaral/sentencetransformer-distilbert-hinglish-big,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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
2617,sentence-transformers/paraphrase-MiniLM-L3-v2,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.0663956999778747,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
2618,indobenchmark/indobert-base-p1,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0140270454188187,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
2619,diper1998/distilgpt2-finetuned-AT,This does not make a very readable text,pere/DeUnCaser,0.0626778304576873,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
2620,google/t5-efficient-base-nl4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0735122002661227,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
2621,google/t5-efficient-large-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,0.0332341026514769,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
2622,amazon/bort,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,0.069247281178832,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
2623,google/t5-efficient-base-kv128,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
2624,brad1141/Longformer-finetuned-norm,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0942143052816391,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
2625,facebook/mbart-large-50-many-to-one-mmt,This model is a fine-tuned checkpoint of mBART-large-50,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
2626,google/t5-efficient-tiny-nh1,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0212432797998189,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
2627,DemangeJeremy/4-sentiments-with-flaubert,FNet is a transformers model with attention replaced with fourier transforms,google/fnet-large,0.0045838579535484,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2628,google/t5-efficient-large-el2,"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-v1,0.029566079378128,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
2629,IDEA-CCNL/Wenzhong-GPT2-3.5B,"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,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
2630,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0915850549936294,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
2631,piotr-rybak/poleval2021-task4-herbert-large-encoder,Disclaimer: This model card has been written by gchhablani,google/fnet-large,0.0925947949290275,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
2632,google/t5-efficient-large-dm2000,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0568457441404461,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
2633,google/t5-efficient-base-ff9000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0120670571923256,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
2634,beston91/gpt2-xl_ft_logits_10k,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0250217970460653,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
2635,malay-huggingface/t5-super-tiny-bahasa-cased,"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,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
2636,google/t5-efficient-small-el12,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
2637,nyu-mll/roberta-med-small-1M-3,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
2638,skt/ko-gpt-trinity-1.2B-v0.5,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0408174218609928,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
2639,dhpollack/distilbert-dummy-sentiment,"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,0.0459801573306321,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
2640,sonoisa/byt5-small-japanese,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0760139822959899,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
2641,sonoisa/byt5-small-japanese,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_gpt2,0.0277914330363273,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
2642,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.039476528763771,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
2643,hfl/chinese-xlnet-mid,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.004130428036054,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
2644,piotr-rybak/poleval2021-task4-herbert-large-encoder,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0991089716553688,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
2645,google/t5-efficient-base-dm256,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0574391521513461,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
2646,allenai/wmt16-en-de-dist-6-1,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0630596112459897,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
2647,castorini/afriberta_small,"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_small,1.0,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
2648,SupriyaArun/squeezebert-uncased-finetuned-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,0.022621439769864,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
2649,google/t5-efficient-small-nl2,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0747121535241603,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
2650,google/t5-efficient-base-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,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
2651,hf-internal-testing/tiny-random-rembert,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,0.0028389487415552,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
2652,microsoft/layoutlm-base-cased,This BERT model was trained using the Megatron-LM library,KBLab/megatron-bert-large-swedish-cased-165k,0.0544226467609405,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
2653,eli4s/Bert-L12-h240-A12,"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,0.0077529688132926,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
2654,google/t5-efficient-small-dm1000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.051898481324315,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
2655,ml6team/mt5-small-german-finetune-mlsum,I have made no attempts to censor,bigjoedata/rockbot,0.0342800049111246,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
2656,google/t5-efficient-small-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,0.0503145959228277,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
2657,hfl/chinese-electra-small-ex-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,0.0227126786485314,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
2658,nyu-mll/roberta-med-small-1M-3,"We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 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
2659,j-hartmann/purchase-intention-english-roberta-large,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0466688200831412,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
2660,castorini/afriberta_base,AfriBERTa base is a pretrained multilingual language model with around 111 million parameters,castorini/afriberta_base,1.0,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
2661,MoritzLaurer/MiniLM-L6-mnli,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0457012044886747,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
2662,google/t5-efficient-small-nl22,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.02236302010715,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
2663,piotr-rybak/poleval2021-task4-herbert-large-encoder,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0403800792992115,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
2664,hfl/chinese-electra-180g-small-generator,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.047187687208255,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
2665,gayanin/bart-mlm-pubmed-medterm,"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,0.0672025680541992,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
2666,nielsr/coref-roberta-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,0.031658315565437,-1.0,NLP,a42c9bdb-be23-4bfd-8add-30afe8d1724a___coref-roberta-large,HF
2667,google/t5-efficient-small-ff12000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0129289077594876,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
2668,google/t5-efficient-mini-nl12,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.023344099521637,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
2669,piotr-rybak/poleval2021-task4-herbert-large-encoder,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0944895446300506,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
2670,SupriyaArun/squeezebert-uncased-finetuned-squad,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0348774045705795,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
2671,tau/tavbert-he,T5-Efficient-MINI-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl24,0.0414937250316142,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
2672,aditeyabaral/sentencetransformer-bert-hinglish-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,0.0617254115641117,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2673,google/t5-efficient-large-nl16,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0355310793966054,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
2674,khizon/distilbert-unreliable-news-eng-4L,"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,0.0503604412078857,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
2675,mikaelsouza/msft-regular-model,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0001109708100557,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
2676,rdenadai/BR_BERTo,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0648807932933171,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
2677,aditeyabaral/sentencetransformer-bert-hinglish-small,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,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2678,KoichiYasuoka/roberta-base-ukrainian,This is a tiny random mt5 model used for testingSee mt5-make-tiny-model,stas/mt5-tiny-random,0.031638853251934,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
2679,google/reformer-crime-and-punishment,Amazon's BORT,amazon/bort,0.0053502591326832,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
2680,tartuNLP/gpt-4-est-large,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/mbart50-large-eng-yor-mt,0.0950673371553421,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
2681,TJMUCH/transcriptome-iseeek,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,0.009076802060008,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
2682,akdeniz27/deberta-v2-xlarge-cuad,"This model is the fine-tuned version of ""DeBERTa v2 XLarge""  using CUAD dataset https://huggingface",akdeniz27/deberta-v2-xlarge-cuad,1.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
2683,aubmindlab/aragpt2-large,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0451386962085962,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
2684,NovelAI/genji-python-6B,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0197828635573386,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
2685,google/t5-efficient-large-dm256,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0487798061221837,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
2686,hfl/rbt3,ipynb and the weights were converted to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead,google/reformer-crime-and-punishment,0.0729089640080928,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
2687,google/t5-efficient-small-kv256,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0700827706605195,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
2688,google/t5-efficient-small-ff1000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0265147937461733,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
2689,eli4s/Bert-L12-h240-A12,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0585732245817779,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
2690,qarib/bert-base-qarib_far_6500k,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.019362446386367,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
2691,tau/tavbert-he,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,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
2692,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.083800122141838,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
2693,google/t5-efficient-small-dm768,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,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
2694,akdeniz27/deberta-v2-xlarge-cuad,† ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-162M,0.0739491879940033,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
2695,gchhablani/fnet-base-finetuned-wnli,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0295160207897424,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
2696,eli4s/Bert-L12-h384-A6,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0130110209186871,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
2697,kyryl0s/gpt2-uk-xxs,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
2698,negfir/distilbert-base-uncased-finetuned-cola,We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task,dexhrestha/Nepali-DistilBERT,0.015778450295329,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
2699,liaad/srl-pt_xlmr-base,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0624076363941033,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
2700,aditeyabaral/sentencetransformer-distilbert-hinglish-big,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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
2701,IDEA-CCNL/Erlangshen-Longformer-110M,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0891937762498855,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
2702,MoritzLaurer/MiniLM-L6-mnli,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0279464417447646,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
2703,google/t5-efficient-small-dm2000,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0512257535010576,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
2704,google/t5-efficient-base-nh32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0431312955915927,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
2705,google/t5-efficient-large-nl16,"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,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
2706,VietAI/gpt-neo-1.3B-vietnamese-news,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0564403049647807,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
2707,google/t5-efficient-large-dm128,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
2708,microsoft/deberta-v2-xxlarge-mnli,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0264708579052239,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
2709,aditeyabaral/sentencetransformer-bert-hinglish-small,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2710,google/t5-efficient-base-nl2,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0503795780241488,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
2711,Cedille/fr-boris,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,dfacbca3-bb14-43a3-97f5-481ee00f5d1a___fr-boris,HF
2712,skt/ko-gpt-trinity-1.2B-v0.5,It was introduced in this paper,readerbench/RoBERT-small,0.0627407953143119,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
2713,google/t5-efficient-large-el2,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0460126362740992,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
2714,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0066868588328361,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
2715,rahulacj/bertweet-base-finetuned-sentiment-analysis,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,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
2716,google/t5-efficient-small-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,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
2717,Iskaj/hf-challenge-test,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
2718,tartuNLP/gpt-4-est-large,This makes the model more efficient since the output embeddings are discarded during fine-tuning,google/rembert,0.0471718236804008,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
2719,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
2720,cammy/led-base-16384-100-MDS,It was introduced in this paper and first released on this page,THUMT/mGPT,0.086661197245121,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
2721,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0462872795760631,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
2722,google/t5-efficient-base-nl32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0051444675773382,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
2723,google/t5-efficient-small-el4,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0281328838318586,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
2724,google/t5-efficient-large-nl16,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0899511910974979,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
2725,dhpollack/distilbert-dummy-sentiment,Boris makes use of the unmodified GPT-2 tokenizer,Cedille/fr-boris,0.0691810194402932,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
2726,mmaguero/gn-bert-tiny-cased,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.053710663691163,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
2727,google/t5-efficient-xl-nl28,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0532756699249148,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
2728,mwesner/reformer-clm,T5-Efficient-BASE-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl2,0.069707952439785,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
2729,diper1998/distilgpt2-finetuned-AT,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
2730,google/t5-efficient-large-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,0.0560629256069659,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
2731,mimi/Waynehills-NLP-doogie,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0799722000956535,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
2732,malay-huggingface/xlnet-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,0.027807085464398,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
2733,google/t5-efficient-base-el8,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0528109855949878,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
2734,bolbolzaban/gpt2-persian,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
2735,google/t5-efficient-large-nh24,"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,0.0853154845535755,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
2736,hfl/rbtl3,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0018444964662194,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
2737,google/t5-efficient-large-dm256,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
2738,google/t5-efficient-large-el4,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0751924198120832,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
2739,google/t5-efficient-large-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,0.0714079122990369,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
2740,uer/t5-v1_1-small-chinese-cluecorpussmall,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0466309264302253,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
2741,minimaxir/magic-the-gathering,The article and  summarization text is reduced via extractive summarization before it is finetuned to the model,ml6team/distilbart-tos-summarizer-tosdr,0.0367940096184611,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
2742,xlnet-large-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,1.0,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
2743,google/t5-efficient-mini-nl24,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0648302771151065,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
2744,google/t5-efficient-large-nl20,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0141205992549658,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
2745,fnlp/cpt-base,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0375097445212304,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
2746,tuner007/pegasus_qa,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0947116166353225,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
2747,tartuNLP/gpt-4-est-large,"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,0.0534543916583061,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
2748,google/t5-efficient-tiny-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,0.0827114507555961,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
2749,google/t5-efficient-xl-nl8,Its original repository can be found here,tau/splinter-large-qass,0.0520362462848424,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
2750,tuner007/pegasus_qa,"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,0.0964704304933548,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
2751,Parsa/BBB_prediction_classification_SMILES,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0710489253203073,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
2752,mwesner/reformer-clm,T5-Efficient-BASE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm2000,0.0518802404403686,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
2753,fnlp/bart-base-chinese,Any bias held by the imdb dataset may be reflected in the model's output,lannelin/bert-imdb-1hidden,0.0639137886464595,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
2754,stas/t5-very-small-random,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0170366689562797,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
2755,superb/hubert-large-superb-sid,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,0.0403870809823274,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
2756,TJMUCH/transcriptome-iseeek,org/abs/2202.08904The model was trained with the parameters:,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.058104321360588,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
2757,stas/mt5-tiny-random,Encoder-decoder models are trained typically by maximizing the likelihood of the target output given an input,thu-coai/LongLM-large,0.0785546600818634,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
2758,google/t5-efficient-mini,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0692138783633708,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
2759,mimi/Waynehills-NLP-doogie,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.079360157251358,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
2760,cammy/led-base-16384-100-MDS,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0977080315351486,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
2761,diper1998/distilgpt2-finetuned-AT,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0987271070480346,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
2762,google/t5-efficient-xl-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,0.0353184305131435,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
2763,andrejmiscic/simcls-scorer-billsum,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,1.0,-1.0,NLP,414f32ef-94a6-4464-bae1-210860b6baa3___simcls-scorer-billsum,HF
2764,google/t5-efficient-base-ff1000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0139611596241593,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
2765,google/t5-efficient-small-el12,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0109369326382875,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
2766,google/t5-efficient-small-el64,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.074667390435934,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
2767,GKLMIP/electra-myanmar-small-uncased,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.08817820250988,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
2768,google/t5-efficient-tiny-el8,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0726453643292188,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
2769,google/t5-efficient-large-kv256,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0554976351559161,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
2770,google/t5-efficient-xl-nl12,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0044727940112351,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
2771,sentence-transformers/paraphrase-MiniLM-L3-v2,"Trained, validate, and tested using a subset of the NELA-GT-2018 dataset",khizon/distilbert-unreliable-news-eng-4L,0.0982996523380279,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
2772,beston91/gpt2-xl_ft_logits_10k,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0302482582628726,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
2773,DemangeJeremy/4-sentiments-with-flaubert,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2774,google/t5-efficient-base-kv32,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
2775,Palak/microsoft_deberta-base_squad,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0986150652170181,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
2776,amazon/bort,Pretrained model on Albanian language using a masked language modeling (MLM) objective,macedonizer/al-roberta-base,0.0612336583435535,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
2777,nguyenvulebinh/envibert,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0549779434998829,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
2778,KoichiYasuoka/roberta-base-ukrainian,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-v1,0.0747398436069488,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
2779,facebook/wmt21-dense-24-wide-en-x,"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,0.0037124702706932,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
2780,nghuyong/ernie-tiny,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0832079276442527,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
2781,google/t5-efficient-small-dm128,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0621451027691363,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
2782,facebook/blenderbot-3B,We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy,facebook/blenderbot-3B,1.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
2783,krevas/finance-koelectra-base-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",krevas/finance-koelectra-base-discriminator,1.0,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
2784,allenai/wmt19-de-en-6-6-base,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-base,1.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
2785,google/t5-efficient-base-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,0.0180769488215446,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
2786,microsoft/layoutlm-large-uncased,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
2787,KoichiYasuoka/roberta-small-belarusian,"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,0.0358884613960981,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
2788,google/t5-efficient-small-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,0.0377598162740468,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
2789,stas/mt5-tiny-random,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0288360342383384,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
2790,google/t5-efficient-tiny-ff12000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0360750723630189,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
2791,microsoft/layoutlm-base-cased,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,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
2792,aditeyabaral/sentencetransformer-bert-hinglish-small,"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,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2793,ian0delond/model-test,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
2794,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el8,0.0786440707743167,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
2795,DemangeJeremy/4-sentiments-with-flaubert,Both pytorch(pytorch_model bin) and Rust(rust_modelot) models are providedNOTE: Use T5Tokenizer to initiate the tokenizer,jweb/japanese-soseki-gpt2-1b,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2796,SetFit/deberta-v3-large__sst2__train-8-8,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0220332108438015,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
2797,sberbank-ai/rugpt3large_based_on_gpt2,Model was trained with sequence length 1024 using transformers lib by SberDevices team on 80B tokens for 3 epochs,sberbank-ai/rugpt3large_based_on_gpt2,1.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
2798,hf-internal-testing/tiny-random-rembert,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0703313052654266,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
2799,aditeyabaral/sentencetransformer-bert-hinglish-small,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,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2800,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,This is an implementation of CPT-Base,fnlp/cpt-base,0.0841357111930847,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
2801,mldmm/GlassBERTa,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0341837406158446,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
2802,google/t5-efficient-large-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-128,0.0667053908109664,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
2803,mrm8488/bert-small-finetuned-squadv2,"If you use the model, please consider citing both the papers:",prajjwal1/bert-tiny,0.0,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
2804,facebook/blenderbot-90M,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0043007539352402,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
2805,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0914101600646972,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
2806,IDEA-CCNL/Erlangshen-Longformer-110M,And there's a lot of English in this dataset too,ErykWdowiak/GPTalian,0.070975812152028,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
2807,google/t5-efficient-small-dm128,"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,0.0386123117059468,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
2808,google/t5-efficient-base-kv128,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0447030430659651,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
2809,tau/tavbert-he,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0232782214879989,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
2810,VietAI/gpt-neo-1.3B-vietnamese-news,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0570067390799522,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
2811,ductuan024/AimeLaw,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0393007397651672,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
2812,KoichiYasuoka/roberta-base-ukrainian,This model was made following the research done by Iz Beltagy and Matthew E,mrm8488/longformer-base-4096-spanish,0.0799354091286659,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
2813,mgreenbe/bertlet-base-uncased-for-sequence-classification,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0544654130935668,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
2814,google/t5-efficient-small-ff1000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0359550593420862,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
2815,aditeyabaral/sentencetransformer-distilbert-hinglish-big,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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
2816,google/t5-efficient-large-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,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
2817,malay-huggingface/t5-super-tiny-bahasa-cased,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0487617850303649,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
2818,google/t5-efficient-xl-nl16,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0251009464263916,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
2819,pulp/CHILDES-ParentBERTo,T5-Efficient-XL-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl12,0.0898274034261703,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
2820,google/t5-efficient-mini-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,0.0758316777646541,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
2821,google/t5-efficient-xl-nl28,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
2822,SetFit/deberta-v3-large__sst2__train-8-8,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0777715891599655,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
2823,google/t5-efficient-base-ff1000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0219201799482107,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
2824,superspray/electra_large_discriminator_squad2_custom_dataset,See SimCLS paper for a description of baselines,andrejmiscic/simcls-scorer-billsum,0.0732181519269943,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
2825,beston91/gpt2-xl_ft_logits_10k,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0308572277426719,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
2826,google/t5-efficient-xl-nl28,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0632757060229777,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
2827,tartuNLP/gpt-4-est-large,The model might be used through the astonishing 🤗 Transformers librairie,asi/gpt-fr-cased-base,0.0434647724032402,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
2828,jweb/japanese-soseki-gpt2-1b,"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,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
2829,sarnikowski/convbert-medium-small-da-cased,This model is case-sensitive: it makes a difference between tirana and Tirana,macedonizer/al-roberta-base,0.0297106616199016,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
2830,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 256",eli4s/Bert-L12-h256-A4,0.0739861913025378,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
2831,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0527724996209144,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
2832,google/t5-efficient-base-dm256,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0375579325482249,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
2833,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0282419547438621,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
2834,diper1998/distilgpt2-finetuned-AT,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-3,0.0762223303318023,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
2835,google/t5-efficient-small-dm128,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
2836,soheeyang/rdr-ctx_encoder-single-trivia-base,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0679218489676713,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
2837,mwesner/reformer-clm,"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,0.0853914394974708,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
2838,IDEA-CCNL/Erlangshen-Longformer-110M,"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-v2,0.051605574786663,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
2839,mrm8488/mobilebert-uncased-finetuned-squadv2,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,0.0501914974302053,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
2840,google/t5-efficient-mini-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,0.0058141648769378,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
2841,Iskaj/hf-challenge-test,Its original repository can be found here,tau/splinter-large-qass,0.0899979770183563,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
2842,groar/gpt-neo-1.3B-finetuned-escape2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
2843,google/t5-efficient-tiny-nh8,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0223448835313319,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
2844,stas/mt5-tiny-random,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0656969249248504,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
2845,google/t5-efficient-small-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-small-dm2000,1.0,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
2846,google/t5-efficient-large-el2,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0049014324322342,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
2847,google/t5-efficient-large-nh2,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,0.0689419694244861,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
2848,1one5ome/gpt2-chinese-gulong,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0561180710792541,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
2849,j-hartmann/purchase-intention-english-roberta-large,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0195633297165235,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
2850,thu-coai/LongLM-large,T5-Efficient-TINY-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff9000,0.0491946803716321,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
2851,ncduy/phobert-large-finetuned-vietnamese_students_feedback,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.011479914188385,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
2852,VietAI/gpt-neo-1.3B-vietnamese-news,"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,0.0315816574729979,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
2853,abdusahmbzuai/aradia-ctc-distilhubert-ft,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0756974890828132,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
2854,google/t5-efficient-large-nh8,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,1.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
2855,SetFit/deberta-v3-large__sst2__train-8-8,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,0.087173193693161,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
2856,aditeyabaral/sentencetransformer-bert-hinglish-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,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2857,google/t5-efficient-base-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,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
2858,google/t5-efficient-base-kv16,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0499879019334911,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
2859,allenai/wmt19-de-en-6-6-base,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0132713904604316,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
2860,hfl/rbt3,Any bias held by the imdb dataset may be reflected in the model's output,lannelin/bert-imdb-1hidden,0.0732637178152799,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
2861,izumi-lab/bert-small-japanese-fin,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0300950780510902,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
2862,skt/ko-gpt-trinity-1.2B-v0.5,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
2863,google/t5-efficient-xl-nl28,"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,0.038654732517898,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
2864,VietAI/gpt-neo-1.3B-vietnamese-news,"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,0.0788266006857156,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
2865,stas/mt5-tiny-random,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0624056719243526,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
2866,google/t5-efficient-base-el6,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.081031572073698,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
2867,rahulacj/bertweet-base-finetuned-sentiment-analysis,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
2868,khizon/distilbert-unreliable-news-eng-4L,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0447949608787894,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
2869,mwesner/reformer-clm,T5-Efficient-BASE-EL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el16,0.0416126735508441,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
2870,suzuki/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,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
2871,cosmoquester/bart-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,0.0,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
2872,uer/t5-v1_1-base-chinese-cluecorpussmall,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
2873,abdusahmbzuai/ft-tatoeba-ar-en,"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,0.0857069119811058,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
2874,ToToKr/kobigbird-bert-base-finetuned-klue,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.018418151885271,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
2875,dhpollack/distilbert-dummy-sentiment,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0535497330129146,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
2876,google/t5-efficient-large-nh2,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,0.0774324368685483,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
2877,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
2878,malay-huggingface/xlnet-tiny-bahasa-cased,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0296498474975426,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
2879,google/t5-efficient-large-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,0.0332158729434013,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
2880,google/t5-efficient-large-nh8-nl32,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0727455839514732,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
2881,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
2882,google/t5-efficient-base-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,0.0691284965723753,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
2883,google/t5-efficient-small-kv32,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0070745130069553,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
2884,mimi/Waynehills-NLP-doogie,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0667026042938232,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
2885,celine98/canine-s-finetuned-sst2,Source language: English,NYTK/translation-marianmt-en-hu,0.0145692508667707,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
2886,aditeyabaral/sentencetransformer-bert-hinglish-small,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0786758139729499,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
2887,skplanet/dialog-koelectra-small-generator,It was trained with 22GB colloquial and written style Korean text data,skplanet/dialog-koelectra-small-generator,1.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
2888,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0463483557105064,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
2889,negfir/distilbert-base-uncased-finetuned-cola,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0531310066580772,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
2890,kyryl0s/gpt2-uk-xxs,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-512,0.0341972885653376,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
2891,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl20,0.0763064175844192,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
2892,rahulacj/bertweet-base-finetuned-sentiment-analysis,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0037695877254009,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
2893,abdusahmbzuai/ft-tatoeba-ar-en,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.070230022072792,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
2894,NovelAI/genji-python-6B,"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,0.0488052206734815,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
2895,google/t5-efficient-large-nh4,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
2896,google/t5-efficient-small-el16-dl4,"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,0.0323473634198307,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
2897,NovelAI/genji-python-6B,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0283152312040329,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
2898,google/t5-efficient-tiny-ff3000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0371131431311368,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
2899,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0576283112168312,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
2900,IDEA-CCNL/Zhouwenwang-Unified-110M,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
2901,DemangeJeremy/4-sentiments-with-flaubert,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,0.0545319560915231,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2902,google/t5-efficient-base-dm1000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
2903,SupriyaArun/squeezebert-uncased-finetuned-squad,"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,0.0440554320812225,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
2904,google/t5-efficient-large-dm2000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
2905,Norod78/hebrew_poetry-gpt_neo-tiny,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0808462351560592,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
2906,google/t5-efficient-large-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,0.0867414958775043,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
2907,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 256",eli4s/Bert-L12-h256-A4,0.0453553944826126,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
2908,google/t5-efficient-tiny-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,0.0764509737491607,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
2909,google/t5-efficient-xl-nl2,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.011742525268346,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
2910,uer/t5-v1_1-small-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,0.0595459621399641,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
2911,allenai/wmt19-de-en-6-6-base,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
2912,piotr-rybak/poleval2021-task4-herbert-large-encoder,This model is a fine-tuned version of distilgpt2 on the None dataset,diper1998/distilgpt2-finetuned-AT,0.066188246011734,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
2913,patrickvonplaten/unispeech-sat-large-timit-ft,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0800475776195526,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
2914,google/t5-efficient-xl-nl4,"Similar to GPT-2, It was pre-trained on the raw texts only, with no human labeling",THUMT/mGPT,0.0488917604088783,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
2915,google/t5-efficient-base-ff1000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0275261336937546,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
2916,google/t5-efficient-small-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,0.0578849092125892,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
2917,minimaxir/magic-the-gathering,"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,0.0439766384661197,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
2918,neuralspace-reverie/indic-transformers-bn-xlmroberta,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,1.0,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
2919,hfl/chinese-legal-electra-base-generator,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.047187687208255,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
2920,google/t5-efficient-base-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,0.062062706798315,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
2921,clue/roberta_chinese_3L312_clue_tiny,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
2922,google/t5-efficient-tiny-nl16,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0460201520472765,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
2923,DemangeJeremy/4-sentiments-with-flaubert,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,0.0692540742456912,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2924,tau/tavbert-he,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,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
2925,microsoft/deberta-v2-xxlarge-mnli,DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder,microsoft/deberta-v2-xxlarge-mnli,1.0,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
2926,google/t5-efficient-large-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,0.0547558087855577,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
2927,KBLab/megatron-bert-large-swedish-cased-165k,"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,0.0285722327729066,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
2928,google/t5-efficient-base-el8,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0186091754585504,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
2929,brad1141/Longformer-finetuned-norm,"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,0.0939285308122634,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
2930,crabz/distil-slovakbert-ner,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0915118604898452,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
2931,google/t5-efficient-xl-nl28,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.031365055590868,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
2932,google/t5-efficient-small-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,0.0236908998340368,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
2933,google/t5-efficient-large-el2,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0833369567990302,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
2934,ErykWdowiak/GPTalian,"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,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
2935,cammy/led-base-16384-100-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,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
2936,google/t5-efficient-tiny-nh16,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.065413886681199,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
2937,tau/tavbert-he,T5-Efficient-SMALL-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff9000,0.0258488678373395,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
2938,google/t5-efficient-xl-nl16,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
2939,ToToKr/kobigbird-bert-base-finetuned-klue,"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,0.0835569202899932,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
2940,google/t5-efficient-base-dm512,This does not make a very readable text,pere/DeUnCaser,0.0226002018898725,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
2941,google/t5-efficient-base-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,0.033701304346323,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
2942,Parsa/BBB_prediction_classification_SMILES,"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,0.0424605893592039,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
2943,google/t5-efficient-large-nh32,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
2944,google/t5-efficient-mini-nl24,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0698130950331687,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
2945,diper1998/distilgpt2-finetuned-AT,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0697527527809143,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
2946,lgrobol/electra-minuscule-generator,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
2947,google/t5-efficient-xl-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,0.0448646359145641,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
2948,hakurei/gpt-j-random-tinier,"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,0.0352884661406278,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
2949,google/t5-efficient-base-dm512,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0233962964266538,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
2950,mimi/Waynehills-NLP-doogie,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0405356176197528,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
2951,tucan9389/kcbert-base-finetuned-squad,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0544336661696434,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
2952,piotr-rybak/poleval2021-task4-herbert-large-encoder,"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,0.0571764633059501,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
2953,Norod78/hebrew_poetry-gpt_neo-tiny,"However, this model outperforms the base model by large margins",tau/splinter-large-qass,0.0121423490345478,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
2954,mimi/Waynehills-NLP-doogie,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0964876115322113,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
2955,google/t5-efficient-small-nl48,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0730039328336715,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
2956,ductuan024/AimeLaw,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0782525576651096,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
2957,google/t5-efficient-tiny-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,0.0,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
2958,google/t5-efficient-tiny-nh8,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0474380534142255,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
2959,uer/chinese_roberta_L-6_H-128,"This is the set of 24 Chinese RoBERTa models pre-trained by UER-py, which is introduced in this paper",uer/chinese_roberta_L-6_H-128,1.0,-1.0,NLP,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
2960,brad1141/Longformer-finetuned-norm,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0378087759017944,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
2961,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,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,0.05369608476758,-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
2962,everdoubling/byt5-Korean-small,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,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
2963,google/t5-efficient-small-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,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
2964,google/t5-efficient-large-nl10,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0401686672121285,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
2965,google/t5-efficient-large-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,0.0693294256925582,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
2966,google/t5-efficient-base-kv256,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,0.0595435611903666,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
2967,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0333119643231232,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
2968,google/t5-efficient-base-kv256,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0457738693803548,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
2969,ptaszynski/yacis-electra-small-japanese,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,1.0,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
2970,brad1141/Longformer-finetuned-norm,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
2971,google/t5-efficient-large-nl10,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0571025125682353,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
2972,google/t5-efficient-large-dm768,This does not make a very readable text,pere/DeUnCaser,0.0500927437096833,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
2973,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
2974,TristanBehrens/js-fakes-4bars,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0210260536521673,-1.0,NLP,84456894-0906-4840-8de2-f427e2536a64___js-fakes-4bars,HF
2975,microsoft/layoutlm-base-cased,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
2976,mrm8488/mobilebert-uncased-finetuned-squadv2,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0512293602029482,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
2977,DemangeJeremy/4-sentiments-with-flaubert,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0410197973251342,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
2978,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
2979,google/t5-efficient-large-nh8,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0754469782114028,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
2980,allenai/wmt16-en-de-dist-6-1,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.0091362446546554,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
2981,mmaguero/gn-bert-tiny-cased,T5-Efficient-SMALL-EL64 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el64,0.0932189784944056,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
2982,mwesner/bart-mlm,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0938061773777008,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
2983,oskrmiguel/mt5-simplification-spanish,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0334691917523741,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
2984,dhpollack/distilbert-dummy-sentiment,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0193815007805824,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
2985,google/t5-efficient-large-el4,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0294957291334867,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
2986,1one5ome/gpt2-chinese-gulong,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
2987,google/t5-efficient-large-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,0.0552722420543431,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
2988,ncduy/phobert-large-finetuned-vietnamese_students_feedback,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0737556144595146,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
2989,google/t5-efficient-large-nl10,"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,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
2990,ian0delond/model-test,This model is a fine-tuned version of camembert/camembert-large on the None dataset,ian0delond/model-test,1.0,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
2991,google/t5-efficient-small-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,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
2992,google/t5-efficient-tiny-nh1,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0341794863343238,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
2993,google/t5-efficient-large-el2,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0773769244551658,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
2994,hfl/chinese-electra-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_v1,0.0557823032140731,-1.0,NLP,4827d900-c34c-4fb3-b5a3-2ec9b4423e64___chinese-electra-small-ex-generator,HF
2995,uer/bart-chinese-4-768-cluecorpussmall,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0607352182269095,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
2996,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
2997,uer/t5-v1_1-base-chinese-cluecorpussmall,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0707081705331801,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
2998,google/t5-efficient-large-kv256,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0093328990042209,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
2999,google/t5-efficient-large-el6,"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-el6,1.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
3000,cammy/led-base-16384-100-MDS,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0518120005726814,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
3001,IDEA-CCNL/Erlangshen-Longformer-330M,Its original repository can be found here,tau/splinter-large-qass,0.004452396184206,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
3002,IDEA-CCNL/Erlangshen-Longformer-330M,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0148325497284531,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
3003,microsoft/layoutlm-large-uncased,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0663190186023712,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
3004,rdenadai/BR_BERTo,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0144156962633132,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
3005,google/t5-efficient-tiny-nl16,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0060111414641141,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
3006,google/t5-efficient-large-nh4,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0668417029082774,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
3007,google/t5-efficient-base-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,0.0,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
3008,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0962541177868843,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
3009,piotr-rybak/poleval2021-task4-herbert-large-encoder,"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,0.0628242045640945,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
3010,google/t5-efficient-tiny-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,0.0360289411619305,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
3011,google/t5-efficient-small-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,0.0349323889240623,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
3012,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"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,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
3013,google/t5-efficient-small-dm768,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0605325363576411,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
3014,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,"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,0.0368347770224014,-1.0,NLP,6493ad33-df30-4985-946f-a59823df46ee___wav2vec2-large-robust-12-ft-emotion-msp-dim,HF
3015,google/t5-efficient-large-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,0.0400387370027601,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
3016,IlyaGusev/rugpt3medium_sum_gazeta,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.027977580204606,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
3017,mimi/Waynehills-NLP-doogie,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.055735170841217,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
3018,google/t5-efficient-large-kv32,T5-Efficient-LARGE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv32,1.0,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
3019,tartuNLP/gpt-4-est-large,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0679306760430336,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
3020,google/t5-efficient-mini-nl12,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0775600001215934,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
3021,danyaljj/opengpt2_pytorch_forward,Zero-shot SELECTRA is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset,Recognai/zeroshot_selectra_small,0.0625071277221043,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
3022,abdusahmbzuai/ft-tatoeba-ar-en,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0942967385053634,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
3023,j-hartmann/purchase-intention-english-roberta-large,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0426114555448293,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
3024,TJMUCH/transcriptome-iseeek,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3025,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl36,0.0981252044439315,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
3026,google/t5-efficient-xl-nl8,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0431413697078824,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
3027,google/t5-efficient-base-nl24,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0436393506824969,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
3028,google/t5-efficient-small-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,0.0664213504642247,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
3029,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl40,0.0710609033703803,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
3030,patrickvonplaten/unispeech-sat-large-timit-ft,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0083082374185323,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
3031,1one5ome/gpt2-chinese-gulong,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.057801179587841,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
3032,google/t5-efficient-large-nl32,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0676764510571956,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
3033,microsoft/layoutlm-base-cased,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0050210393965244,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3034,google/t5-efficient-large-nh4,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.029037544503808,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
3035,google/t5-efficient-small-ff12000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0434331642463803,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
3036,mikaelsouza/msft-regular-model,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
3037,suzuki/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,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
3038,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff1000,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
3039,abdusahmbzuai/ft-tatoeba-ar-en,"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,0.076625071465969,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
3040,google/t5-efficient-base-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-base-nl36,1.0,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
3041,PlanTL-GOB-ES/gpt2-base-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,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
3042,google/bert_uncased_L-12_H-256_A-4,Its original repository can be found here,tau/splinter-large-qass,0.0176675698409477,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
3043,piotr-rybak/poleval2021-task4-herbert-large-encoder,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,0.084960401058197,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
3044,facebook/m2m100-12B-avg-10-ckpt,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,3.697443753478367e-05,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
3045,malay-huggingface/t5-super-tiny-bahasa-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,0.0580021757632493,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
3046,stas/mt5-tiny-random,The challenge of this project is to train a model to write the languages of Italy,ErykWdowiak/GPTalian,0.094610258936882,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
3047,google/t5-efficient-tiny-el8,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0695117861032485,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
3048,TJMUCH/transcriptome-iseeek,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0042097093537449,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3049,sberbank-ai/rugpt3large_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,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
3050,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0721603259444236,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
3051,jonfd/convbert-small-igc-is,I have made no attempts to censor,bigjoedata/rockbot,0.024055777862668,-1.0,NLP,8eeeab89-304b-413e-9ad8-7e9dade83c8a___convbert-small-igc-is,HF
3052,celine98/canine-s-finetuned-sst2,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0857136473059654,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
3053,google/t5-efficient-base-nl2,T5-Efficient-BASE-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl2,1.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
3054,malay-huggingface/t5-super-tiny-bahasa-cased,Pretrained T5 super-tiny language model for Malay,malay-huggingface/t5-super-tiny-bahasa-cased,1.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
3055,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"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-v1,0.080456718802452,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
3056,ncduy/phobert-large-finetuned-vietnamese_students_feedback,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0812317952513694,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
3057,ctoraman/RoBERTa-TR-medium-wp-66k,"Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size)",ctoraman/RoBERTa-TR-medium-wp-66k,1.0,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
3058,microsoft/deberta-v2-xxlarge-mnli,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0139969130977988,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
3059,google/t5-efficient-tiny-ff3000,"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,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
3060,eli4s/Bert-L12-h240-A12,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0252163799013942,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
3061,google/t5-efficient-large-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,0.0685765910893678,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
3062,google/t5-efficient-mini-nl8,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0574358291923998,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
3063,google/t5-efficient-tiny-el8,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0896260999143123,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
3064,everdoubling/byt5-Korean-small,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,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
3065,mikaelsouza/msft-regular-model,"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,0.0582876652479171,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
3066,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
3067,fnlp/bart-base-chinese,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,0.084892850369215,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
3068,oskrmiguel/mt5-simplification-spanish,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0300393030047416,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
3069,google/t5-efficient-tiny-nl12,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0794237628579139,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
3070,google/t5-efficient-large-dm768,"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,0.0253335433080792,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
3071,google/t5-efficient-small-ff9000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
3072,hfl/chinese-electra-180g-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,0.0157637856900692,-1.0,NLP,62f41372-5c87-4b84-9ef8-8c7f5f29708a___chinese-electra-180g-small-generator,HF
3073,google/bert_uncased_L-6_H-256_A-4,The smaller BERT models are intended for environments with restricted computational resources,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
3074,GKLMIP/electra-myanmar-small-uncased,"The model has 8 layers, 6 attention heads, 768 hidden units and 3072 feed forward size",castorini/afriberta_base,0.041216230019927,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
3075,tartuNLP/gpt-4-est-large,"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,0.0869551599025726,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
3076,google/t5-efficient-xl-nl6,Its original repository can be found here,tau/splinter-large-qass,0.0602165721356868,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
3077,mikaelsouza/msft-regular-model,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0775012001395225,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
3078,malay-huggingface/t5-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,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
3079,google/t5-efficient-base-nl36,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.066049501299858,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
3080,google/t5-efficient-large-nl12,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0101731177419424,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
3081,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl6,0.0871432125568389,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
3082,Iskaj/hf-challenge-test,"6,860,062 examples are extracted from below corpora",lassl/roberta-ko-small,0.0937617570161819,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
3083,uer/t5-v1_1-small-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,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
3084,mwesner/reformer-clm,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0505978018045425,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
3085,aubmindlab/aragpt2-large,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
3086,izumi-lab/bert-small-japanese-fin,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0782904749115307,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
3087,mrm8488/bert-small-finetuned-squadv2,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
3088,google/t5-efficient-large-nh8,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0548113770782947,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
3089,google/t5-efficient-tiny-nl16,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0049161370843648,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
3090,google/t5-efficient-base-nl32,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0053623728454113,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
3091,google/t5-efficient-base-nh32,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0096496450714766,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
3092,hajime9652/xlnet-japanese,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0398391485214233,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
3093,google/t5-efficient-large-kv16,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0583981629461049,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
3094,google/t5-efficient-tiny-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,0.0943655334413051,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
3095,cammy/led-base-16384-100-MDS,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0598442703485488,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
3096,amazon/bort,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,0.0608782079070806,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
3097,UWB-AIR/MQDD-duplicates,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0319647276774048,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
3098,google/t5-efficient-small-ff3000,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0446712914854287,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
3099,google/t5-efficient-small-ff1000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0609879773110151,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
3100,google/t5-efficient-small-el64,"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,0.0538913561031222,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
3101,mldmm/GlassBERTa,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,1.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
3102,google/t5-efficient-small-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,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
3103,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0008212076500058,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
3104,skt/ko-gpt-trinity-1.2B-v0.5,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0588895566761493,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
3105,aubmindlab/aragpt2-large,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,0.0555550046265125,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
3106,superspray/electra_large_discriminator_squad2_custom_dataset,"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,0.0627947300672531,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
3107,VietAI/gpt-neo-1.3B-vietnamese-news,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0398759860545396,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
3108,anirudh21/xlnet-base-cased-finetuned-rte,"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,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
3109,VietAI/gpt-neo-1.3B-vietnamese-news,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0907813273370265,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
3110,google/t5-efficient-xl-nl12,Its original repository can be found here,tau/splinter-large-qass,0.0559509117156266,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
3111,microsoft/layoutlm-large-uncased,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0050210393965244,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
3112,anirudh21/xlnet-base-cased-finetuned-rte,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0611343197524547,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
3113,google/t5-efficient-base-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,0.0691842939704656,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
3114,piotr-rybak/poleval2021-task4-herbert-large-encoder,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
3115,suzuki/distilbert-base-uncased-finetuned-squad,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0712829232215881,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
3116,google/t5-efficient-small-kv16,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
3117,VietAI/gpt-neo-1.3B-vietnamese-news,"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,0.0557582043111323,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
3118,google/t5-efficient-xl-nl12,"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,0.0517319291830062,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
3119,microsoft/layoutlm-base-cased,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,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3120,DemangeJeremy/4-sentiments-with-flaubert,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.025735436938703,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
3121,google/t5-efficient-base-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,0.0460500214248895,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
3122,ctoraman/RoBERTa-TR-medium-wp-66k,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,1.0,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
3123,google/rembert,"It is meant to be a general-purpose model, similar to mBERT",google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
3124,amazon/bort,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0030116261914372,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
3125,google/t5-efficient-base-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,0.0802173539996147,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
3126,hfl/rbt3,"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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
3127,tobiaslee/bert-2l-768h-uncased,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0911905132234096,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
3128,hakurei/gpt-j-random-tinier,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
3129,Milos/slovak-gpt-j-405M,"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,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
3130,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,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,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
3131,hfl/chinese-electra-180g-small-ex-generator,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
3132,hfl/chinese-electra-180g-small-ex-generator,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
3133,zoha/wav2vec2-base-timit-demo-colab,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0833330750465393,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
3134,danyaljj/opengpt2_pytorch_forward,"Its sister model used the same setup, but was instead trained for only 125k steps",KBLab/megatron-bert-base-swedish-cased-600k,0.0806939130028088,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
3135,google/t5-efficient-base-el8,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
3136,gilparmentier/pokemon_gptj_model,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
3137,facebook/wmt21-dense-24-wide-en-x,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0440712995827197,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
3138,google/t5-efficient-large-nh32,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.054970022290945,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
3139,google/t5-efficient-small-el64,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0834895893931388,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
3140,flax-community/bengali-t5-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,0.0150928860530257,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
3141,google/t5-efficient-mini-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,0.075872553512454,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
3142,abdusahmbzuai/ft-tatoeba-ar-en,"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,0.0729486346244812,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
3143,google/t5-efficient-large-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,0.0565483476966619,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
3144,tau/tavbert-he,We used the T5-base model for this model,flax-community/bengali-t5-base,0.0127009935677051,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
3145,google/t5-efficient-large-el4,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0466324184089899,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
3146,mrm8488/longformer-base-4096-spanish,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,1.0,-1.0,NLP,47fa5077-4d92-4913-a31a-b5f621200d4b___longformer-base-4096-spanish,HF
3147,tau/tavbert-he,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.019810264930129,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
3148,microsoft/layoutlm-base-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3149,suzuki/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,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
3150,fnlp/bart-base-chinese,bin weights file is recommended,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0731594190001487,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
3151,google/t5-efficient-xl-nl8,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.06544491648674,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
3152,IDEA-CCNL/Zhouwenwang-Unified-1.3B,"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,0.0440031975507736,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
3153,SetFit/deberta-v3-large__sst2__train-8-8,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0649735406041145,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
3154,aliosm/ComVE-gpt2-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,0.0169697548262774,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
3155,google/t5-efficient-base-dm512,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0042956490069628,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
3156,google/t5-efficient-large-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,0.0473943129181861,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
3157,SupriyaArun/squeezebert-uncased-finetuned-squad,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0881092473864555,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
3158,indobenchmark/indobert-base-p1,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.026653158167998,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
3159,PlanTL-GOB-ES/gpt2-base-bne,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0118700272093216,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
3160,google/t5-efficient-base-nh16,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0037894807755946,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
3161,IDEA-CCNL/Erlangshen-Longformer-110M,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0242718383669852,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
3162,tau/tavbert-he,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,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
3163,google/t5-efficient-mini-nl6,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0684110447764396,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
3164,google/t5-efficient-large-dm128,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.040482385084033,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
3165,google/t5-efficient-base-ff12000,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,0.0851540528237819,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
3166,DTAI-KULeuven/robbertje-1-gb-bort,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
3167,flboehm/reddit-bert-text_5,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0839833617210388,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
3168,jonatasgrosman/wav2vec2-xls-r-1b-english,Fine-tuned facebook/wav2vec2-xls-r-1b on English using the Common Voice 8,jonatasgrosman/wav2vec2-xls-r-1b-english,1.0,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
3169,nherve/flaubert-oral-asr,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,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
3170,brad1141/Longformer-finetuned-norm,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0896724164485931,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
3171,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0815782770514488,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
3172,google/t5-efficient-small-ff3000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0664540752768516,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
3173,google/t5-efficient-large-kv16,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0278950966894626,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
3174,sentence-transformers/paraphrase-MiniLM-L3-v2,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
3175,google/t5-efficient-xl-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,0.0786212682723999,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
3176,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3177,cammy/led-large-16384-arxiv-100-MDS,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0413134433329105,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
3178,eli4s/Bert-L12-h240-A12,This model was pretrained on the bookcorpus dataset using knowledge distillation,eli4s/Bert-L12-h240-A12,1.0,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
3179,groar/gpt-neo-1.3B-finetuned-escape2,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0959748774766922,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
3180,google/t5-efficient-mini-nl6,"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-v1,0.0530513571575283,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
3181,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0744974613189697,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
3182,pere/DeUnCaser,This does not make a very readable text,pere/DeUnCaser,1.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
3183,google/t5-efficient-base-ff6000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
3184,mldmm/GlassBERTa,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0036949007771909,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
3185,negfir/distilbert-base-uncased-finetuned-cola,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0954825952649116,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
3186,mrm8488/xlm-multi-finetuned-xquadv1,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
3187,superspray/electra_large_discriminator_squad2_custom_dataset,RobBERTje is a collection of distilled models based on RobBERT,DTAI-KULeuven/robbertje-1-gb-bort,0.0598401464521884,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
3188,aubmindlab/aragpt2-large,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0251322193071246,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
3189,google/t5-efficient-large-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,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
3190,google/t5-efficient-small-el12,T5-Efficient-SMALL-EL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el12,1.0,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
3191,akdeniz27/deberta-v2-xlarge-cuad,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0972639322280883,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
3192,VietAI/gpt-neo-1.3B-vietnamese-news,This model is finetuned on STS-B dataset with state-of-the-art second-order optimizer M-FAC,M-FAC/bert-mini-finetuned-stsb,0.0806489512324332,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
3193,mrm8488/deberta-v3-small-finetuned-mrpc,"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,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
3194,google/t5-efficient-large-dm512,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
3195,ptaszynski/yacis-electra-small-japanese,"This model uses ELECTRA Small model settings, 12 layers, 128 dimensions of hidden states, and 12 attention heads",ptaszynski/yacis-electra-small-japanese,1.0,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
3196,google/t5-efficient-base-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,0.0560757927596568,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
3197,google/t5-efficient-tiny-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,0.0520214568823575,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
3198,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,"All training was done on a Tesla P100, provided by Kaggle",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
3199,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,This is a multilingual translation model with multiple target languages,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
3200,suzuki/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,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
3201,google/t5-efficient-large-dm256,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.011217376217246,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
3202,google/t5-efficient-large-kv256,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0400303304195403,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
3203,uer/t5-v1_1-base-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,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
3204,google/t5-efficient-xl-nl16,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
3205,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0684023424983024,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
3206,google/t5-efficient-base-kv32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0492026964202522,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
3207,pszemraj/led-large-book-summary,The architecture requires two separate models,enelpol/czywiesz-context,0.073178507387638,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
3208,NovelAI/genji-python-6B,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0122703711191812,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
3209,Parsa/BBB_prediction_classification_SMILES,The model was created for selective question answering in Polish,enelpol/czywiesz-context,0.0278336952129999,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
3210,microsoft/layoutlm-base-cased,Note: This model does not have a tokenizer as it was pretrained on audio alone,microsoft/unispeech-sat-base,0.0260134786367416,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3211,inokufu/flaubert-base-uncased-xnli-sts,T5-Efficient-BASE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv16,0.0626382660120725,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
3212,google/t5-efficient-large-nl32,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0384437758475541,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
3213,ErykWdowiak/GPTalian,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0104364329949021,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
3214,NeuML/bert-small-cord19,"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,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
3215,Recognai/selectra_medium,"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,0.0344898663461208,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
3216,abdusahmbzuai/aradia-ctc-distilhubert-ft,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,0.0776685774326324,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
3217,google/t5-efficient-large-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,0.0692541077733039,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
3218,google/t5-efficient-xl-nl16,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0067766085267066,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
3219,TJMUCH/transcriptome-iseeek,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0978626161813736,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3220,google/t5-efficient-small-kv256,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0221836157143115,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
3221,google/t5-efficient-small-dm256,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0458056721836328,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
3222,anirudh21/xlnet-base-cased-finetuned-rte,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,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
3223,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,0.0125444047152996,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
3224,google/t5-efficient-small-ff6000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0673027001321315,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
3225,google/t5-efficient-large-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,0.0457805711776017,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
3226,IDEA-CCNL/Zhouwenwang-Unified-1.3B,"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,1.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
3227,google/t5-efficient-base-el8,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0698608122766017,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
3228,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
3229,thu-coai/LongLM-large,This was first described in this paper and released in this repository,SajjadAyoubi/distil-bigbird-fa-zwnj,0.0145703526213764,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
3230,Palak/microsoft_deberta-base_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,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
3231,hfl/rbt3,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0669432021677493,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
3232,google/t5-efficient-tiny-el8,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0074067180976272,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
3233,google/t5-efficient-large-nl32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.070844754576683,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
3234,akdeniz27/deberta-v2-xlarge-cuad,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,0.0471143946051597,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
3235,google/t5-efficient-tiny-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,0.0164734721183776,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
3236,microsoft/layoutlm-base-uncased,This model is case-sensitive: it makes a difference between tirana and Tirana,macedonizer/al-roberta-base,0.0514089576900005,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3237,google/t5-efficient-large-dm256,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
3238,IlyaGusev/rugpt3medium_sum_gazeta,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0596710201352834,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
3239,google/t5-efficient-xl-nl12,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0331011451780796,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
3240,google/t5-efficient-large-el8,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
3241,GKLMIP/electra-myanmar-small-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,0.0403806706890463,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
3242,google/t5-efficient-small-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,0.0444652885198592,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
3243,microsoft/layoutlm-base-uncased,For other languages this will be mainly experimental,pere/DeUnCaser,0.0630133599042892,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3244,stas/pegasus-cnn_dailymail-tiny-random,"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,0.0855580456554889,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
3245,dhpollack/distilbert-dummy-sentiment,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
3246,hajime9652/xlnet-japanese,"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,0.0832683369517326,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
3247,google/t5-efficient-large-nh8,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0127511974424124,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
3248,sentence-transformers/paraphrase-MiniLM-L3-v2,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
3249,google/t5-efficient-tiny-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,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
3250,google/t5-efficient-small-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,0.0488569028675555,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
3251,cosmoquester/bart-ko-base,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,8d330177-2381-48d6-b4ea-b548ec18afc2___bart-ko-base,HF
3252,mrm8488/deberta-v3-small-finetuned-mrpc,"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,0.0873108208179473,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
3253,google/t5-efficient-base-el6,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.02023964561522,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
3254,google/t5-efficient-base-el8,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0858923867344856,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
3255,google/t5-efficient-base-kv128,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0334747973829508,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
3256,google/t5-efficient-small-dm768,This does not make a very readable text,pere/DeUnCaser,0.0523285595700144,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
3257,google/t5-efficient-base-nh16,"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,0.0517602991312741,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
3258,fnlp/bart-base-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,0.0,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
3259,sberbank-ai/rugpt3large_based_on_gpt2,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0458414796739816,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
3260,razent/SciFive-large-Pubmed,"For instance, to load the tokenizer for BabyBERTa-1, load it as follows:",phueb/BabyBERTa-2,0.0541423372924327,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
3261,google/t5-efficient-base-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,0.055330455303192,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
3262,mimi/Waynehills-NLP-doogie,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0976015031337738,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
3263,allenai/macaw-3b,"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,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
3264,cammy/led-large-16384-arxiv-100-MDS,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,0.0597272552549839,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
3265,brad1141/Longformer-finetuned-norm,"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,0.0875884220004081,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
3266,google/t5-efficient-large-dm2000,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0846649929881095,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
3267,nghuyong/ernie-tiny,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0515076940258343,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
3268,superspray/electra_large_discriminator_squad2_custom_dataset,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0410954914987087,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
3269,rahulacj/bertweet-base-finetuned-sentiment-analysis,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0297936722636222,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
3270,khizon/distilbert-unreliable-news-eng-4L,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0257726358249783,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
3271,1one5ome/gpt2-chinese-gulong,"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,0.0980584621429443,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
3272,DemangeJeremy/4-sentiments-with-flaubert,mGPT is a Transformer-based model which pre-trained on massive multilingual data covering over 101 languages,THUMT/mGPT,0.0373370097950101,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
3273,google/t5-efficient-small-el64,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0636835806071757,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
3274,sberbank-ai/rugpt3medium_based_on_gpt2,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0580078582279384,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
3275,google/t5-efficient-tiny-nh8,T5-Efficient-TINY-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh8,1.0,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
3276,google/t5-efficient-large-nh32,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0339640770107507,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
3277,stas/mt5-tiny-random,"The quality estimation can be done at different levels: document level, sentence level and word level",TransQuest/monotransquest-hter-en_lv-it-nmt,0.0340877249836921,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
3278,hf-internal-testing/tiny-random-rembert,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0702686682343483,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
3279,CuongLD/wav2vec2-large-xlsr-vietnamese,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0855410955846309,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
3280,mldmm/GlassBERTa,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
3281,M-FAC/bert-mini-finetuned-stsb,com/huggingface/transformers/tree/master/examples/pytorch/text-classification and just swap Adam optimizer with M-FAC,M-FAC/bert-mini-finetuned-stsb,1.0,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
3282,mimi/Waynehills-NLP-doogie,"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,0.0670392513275146,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
3283,inokufu/flaubert-base-uncased-xnli-sts,"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,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
3284,cammy/led-large-16384-arxiv-100-MDS,The corpus was tokenized for pretraining with MeCab,ptaszynski/yacis-electra-small-japanese,0.0895523726940155,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
3285,google/t5-efficient-large-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,0.0441301632672547,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
3286,google/byt5-large,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,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
3287,hfl/chinese-electra-180g-small-ex-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,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
3288,jnz/electra-ka,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0919570699334144,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
3289,google/t5-efficient-small-ff9000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0394090060144662,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
3290,skt/ko-gpt-trinity-1.2B-v0.5,"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,0.0479281693696975,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
3291,google/t5-efficient-base-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,0.0679360274225473,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
3292,deep-learning-analytics/triviaqa-t5-base,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0288290200488907,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
3293,Iskaj/hf-challenge-test,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0682482495903968,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
3294,google/t5-efficient-base-nl48,"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-p1,0.01064377091825,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
3295,lannelin/bert-imdb-1hidden,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0315589314947525,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
3296,uer/pegasus-base-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,0.0297964876517653,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
3297,google/t5-efficient-xl-nl16,"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,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
3298,google/t5-efficient-large-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,0.0402524545788764,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
3299,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm768,0.0695102736353874,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
3300,google/t5-efficient-tiny-nh8,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0628330409526824,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
3301,mrm8488/mobilebert-uncased-finetuned-squadv2,"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,0.0335870534181594,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
3302,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0068787336349487,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
3303,kyryl0s/gpt2-uk-xxs,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.015617161989212,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
3304,KoichiYasuoka/roberta-small-belarusian,"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,0.0142202237620949,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
3305,allenai/macaw-3b,"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,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
3306,izumi-lab/bert-small-japanese-fin,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0525320520003636,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
3307,google/t5-efficient-large-dm768,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0825365781784057,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
3308,google/t5-efficient-base-nh8,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
3309,DemangeJeremy/4-sentiments-with-flaubert,"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,0.0769607573747634,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
3310,TJMUCH/transcriptome-iseeek,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0209277644753456,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3311,microsoft/layoutlm-base-uncased,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0050210393965244,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3312,google/t5-efficient-tiny-nl16,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0685497187077998,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
3313,google/t5-efficient-mini-nl24,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0091225225478411,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
3314,soheeyang/rdr-ctx_encoder-single-trivia-base,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0648759727676709,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
3315,groar/gpt-neo-1.3B-finetuned-escape2,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0461153648793697,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
3316,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0948181450366973,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
3317,google/t5-efficient-small-nl48,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0841067843139171,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
3318,nguyenvulebinh/envibert,The model architecture is custom for production so it only contains 70M parameters,nguyenvulebinh/envibert,1.0,-1.0,NLP,842ee526-5601-4d38-9244-0292ae5d0c5f___envibert,HF
3319,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
3320,pszemraj/led-large-book-summary,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0293169547803699,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
3321,google/t5-efficient-xl-nl8,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0798269286751747,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
3322,NeuML/bert-small-cord19,BERT L6_H-512_A-8 model fine-tuned on the CORD-19 dataset,NeuML/bert-small-cord19,1.0,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
3323,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,7e25dbfa-5035-4e7b-81e7-883e087905e0___distil-wav2vec2-xls-r-adult-child-cls-64m,HF
3324,skt/ko-gpt-trinity-1.2B-v0.5,"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,0.0377056803554296,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
3325,kyryl0s/gpt2-uk-xxs,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0498217605054378,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
3326,google/t5-efficient-small-dm256,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0293219760060309,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
3327,google/t5-efficient-base-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,0.0110989175736903,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
3328,allenai/wmt16-en-de-12-1,"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,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
3329,bookbot/distil-wav2vec2-adult-child-cls-52m,DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture,bookbot/distil-wav2vec2-adult-child-cls-52m,1.0,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
3330,google/t5-efficient-xl-nl12,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0755846723914146,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
3331,stas/t5-very-small-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,0.0,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
3332,anas-awadalla/bert-tiny-finetuned-squad,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0315717607736587,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
3333,hf-internal-testing/tiny-random-rembert,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
3334,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
3335,google/t5-efficient-small-dm768,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0505130216479301,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
3336,google/t5-efficient-base-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,0.0769268311560153,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
3337,google/t5-efficient-large-nl16,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0508235115557908,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
3338,google/t5-efficient-small-dm768,"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,0.0381516390480101,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
3339,google/t5-efficient-xl-nl6,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.065633088350296,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
3340,stas/t5-very-small-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,0.0926449000835418,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
3341,akdeniz27/deberta-v2-xlarge-cuad,"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,0.0437616854906082,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
3342,google/t5-efficient-small-dm1000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
3343,google/t5-efficient-small-ff12000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
3344,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,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,0.0498600080609321,-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
3345,google/t5-efficient-large-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,0.0226214155554771,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
3346,cammy/led-large-16384-arxiv-100-MDS,"For instance, you can run the following command to finetune on the MRPC text classification task",kssteven/ibert-roberta-large,0.0950042903423309,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
3347,aditeyabaral/sentencetransformer-bert-hinglish-small,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0221016630530357,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
3348,google/t5-efficient-large-nh2,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0330847967416048,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
3349,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0288687273859977,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
3350,MoritzLaurer/MiniLM-L6-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,0.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
3351,google/t5-efficient-large-el6,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0613003447651862,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
3352,aditeyabaral/sentencetransformer-bert-hinglish-small,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,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
3353,bigjoedata/rockbot,I have made no attempts to censor,bigjoedata/rockbot,1.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
3354,google/t5-efficient-large-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,0.0297416727989911,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
3355,google/bert_uncased_L-12_H-256_A-4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,96c97a7e-9c4e-41ac-8b02-83b3d250c827___bert_uncased_L-12_H-256_A-4,HF
3356,suzuki/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,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
3357,google/reformer-enwik8,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0632535604139168,-1.0,NLP,6aa23d61-51e8-44d6-b6b7-b68cce7cd0c1___reformer-enwik8,HF
3358,mwesner/reformer-clm,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0877363085746765,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
3359,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"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,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
3360,mmaguero/gn-bert-tiny-cased,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0648800004273652,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
3361,microsoft/layoutlm-base-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,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3362,rinna/japanese-gpt2-xsmall,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0232865251600741,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
3363,patrickvonplaten/unispeech-sat-large-timit-ft,"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,0.0889634639024734,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
3364,google/t5-efficient-xl-nl6,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0251009464263916,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
3365,google/t5-efficient-mini-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,0.0686257351189851,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
3366,cross-encoder/ms-marco-MiniLM-L-2-v2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0522377134433814,-1.0,NLP,c2d1bb44-2e13-4b2e-8b5d-23fa4c9a88e9___ms-marco-MiniLM-L-2-v2,HF
3367,microsoft/layoutlm-base-cased,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0270363669842481,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3368,cammy/led-base-16384-100-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,0.0258153527975082,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
3369,google/t5-efficient-large-dm768,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,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
3370,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0504526831209659,-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
3371,sonoisa/byt5-small-japanese,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0820307433605194,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
3372,google/t5-efficient-large-dm768,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0436312765814363,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
3373,everdoubling/byt5-Korean-small,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,0.0379957925528287,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
3374,google/t5-efficient-base-kv16,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0492775198072194,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
3375,negfir/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,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
3376,mnaylor/bigbird-base-mimic-mortality,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
3377,ebagdasa/propaganda_positive_bart_large,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0173899134000142,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
3378,sonoisa/byt5-small-japanese,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0360958576202392,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
3379,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"Trained, validate, and tested using a subset of the NELA-GT-2018 dataset",khizon/distilbert-unreliable-news-eng-4L,0.0982996523380279,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
3380,1one5ome/gpt2-chinese-gulong,"For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github",sarnikowski/convbert-medium-small-da-cased,0.077541671693325,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
3381,mldmm/GlassBERTa,This repository provides an extra-small-sized Japanese GPT-2 model,rinna/japanese-gpt2-xsmall,0.054434688296169,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
3382,microsoft/layoutlm-base-uncased,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3383,hfl/chinese-electra-180g-small-ex-discriminator,I have made no attempts to censor,bigjoedata/rockbot,0.0041386261582374,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
3384,diper1998/distilgpt2-finetuned-AT,It was introduced in this paper,readerbench/RoBERT-small,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
3385,wukevin/tcr-bert,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0467636049725115,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
3386,google/t5-efficient-large-nl12,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.07731244713068,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
3387,amazon/bort,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0321277808398007,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
3388,google/t5-efficient-tiny-nl24,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0103786122053861,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
3389,TJMUCH/transcriptome-iseeek,Final perplexity on test set is 13.6,sberbank-ai/rugpt3large_based_on_gpt2,0.093775451183319,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3390,hajime9652/xlnet-japanese,Pretrained roberta-ko-small on korean language was trained by LASSL framework,lassl/roberta-ko-small,0.0966632738709449,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
3391,cosmoquester/bart-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,0.0148665172358353,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
3392,anirudh21/xlnet-base-cased-finetuned-rte,"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,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
3393,VietAI/gpt-neo-1.3B-vietnamese-news,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
3394,google/rembert,RemBERT's main difference with mBERT is that the input and output embeddings are not tied,google/rembert,1.0,-1.0,NLP,ece401f9-6a61-4723-b9ac-b898493cfee8___rembert,HF
3395,google/t5-efficient-mini-nl12,This does not make a very readable text,pere/DeUnCaser,0.0577092748135327,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
3396,hf-internal-testing/tiny-random-rembert,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.05839728936553,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
3397,brad1141/Longformer-finetuned-norm,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0566916465759277,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
3398,fnlp/cpt-base,py (Download Here) that define the architecture of CPT into your project,fnlp/cpt-base,1.0,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
3399,brad1141/Longformer-finetuned-norm,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0866372436285018,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
3400,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3401,google/t5-efficient-small-nl24,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
3402,cammy/led-base-16384-100-MDS,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
3403,stas/mt5-tiny-random,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
3404,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
3405,google/t5-efficient-tiny-nl6,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0172935482114553,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
3406,malay-huggingface/t5-super-tiny-bahasa-cased,t5-super-tiny-bahasa-cased model was pretrained on multiple tasks,malay-huggingface/t5-super-tiny-bahasa-cased,1.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
3407,gayanin/bart-mlm-pubmed-medterm,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.072266399860382,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
3408,google/t5-efficient-small-nl24,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0318143889307976,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
3409,tartuNLP/gpt-4-est-base,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0458830092102289,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
3410,allenai/wmt19-de-en-6-6-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,0.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
3411,google/t5-efficient-large-nh12,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0729764960706233,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
3412,SupriyaArun/squeezebert-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,0.0224074833095073,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
3413,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3414,google/t5-efficient-base-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,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
3415,anas-awadalla/bert-tiny-finetuned-squad,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0745361670851707,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
3416,suzuki/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,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
3417,stas/t5-very-small-random,"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,0.047692947089672,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
3418,nielsr/coref-roberta-base,CorefRoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion,nielsr/coref-roberta-base,1.0,-1.0,NLP,8473d81a-bfd8-4703-b569-a14970be6a42___coref-roberta-base,HF
3419,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,0.0490115880966186,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
3420,google/t5-efficient-small-ff1000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.002139527350664,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
3421,google/t5-efficient-tiny-nl2,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0557189658284187,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
3422,stas/pegasus-cnn_dailymail-tiny-random,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0157395740970969,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
3423,rdenadai/BR_BERTo,T5-Efficient-LARGE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh24,0.0120446837196747,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
3424,google/t5-efficient-base-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,0.0617144592106342,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
3425,M-FAC/bert-mini-finetuned-stsb,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,1.0,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
3426,google/t5-efficient-large-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,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
3427,google/t5-efficient-small-el16-dl4,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,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
3428,allenai/macaw-3b,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0580244101583957,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
3429,piotr-rybak/poleval2021-task4-herbert-large-encoder,"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,0.0526548698544502,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
3430,Davlan/m2m100_418M-eng-yor-mt,This model is limited by its training dataset,Davlan/m2m100_418M-eng-yor-mt,1.0,-1.0,NLP,22200502-47bf-477d-b7e3-dec71a3aae90___m2m100_418M-eng-yor-mt,HF
3431,google/t5-efficient-xl-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,0.0624515935778617,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
3432,google/t5-efficient-small-kv32,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
3433,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,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-squadv2,0.0981269031763076,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
3434,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,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,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
3435,google/t5-efficient-large-dm2000,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0768695510923861,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
3436,macedonizer/al-roberta-base,It was introduced in this paper and first released in this repository,macedonizer/al-roberta-base,1.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
3437,kyryl0s/gpt2-uk-xxs,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0596933495253324,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
3438,gilparmentier/pokemon_gptj_model,This resulted into 2TB of Spanish clean corpus,PlanTL-GOB-ES/gpt2-large-bne,0.01553351059556,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
3439,google/t5-efficient-xl-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-xl-nl4,1.0,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
3440,google/t5-efficient-small-ff3000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
3441,google/t5-efficient-small-el8,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0749511271715164,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
3442,rinna/japanese-gpt-1b,For best results use python as below,pszemraj/led-large-book-summary,0.0211855260034401,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
3443,google/t5-efficient-large-el2,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0602050982415675,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
3444,mgreenbe/bertlet-base-uncased-for-sequence-classification,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0443831197917461,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
3445,allenai/macaw-3b,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0281438858558733,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
3446,google/t5-efficient-large-el4,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0251912251114844,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
3447,google/t5-efficient-large-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,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
3448,google/t5-efficient-base-kv32,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0430122101679443,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
3449,google/t5-efficient-base-dm512,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
3450,google/t5-efficient-small-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,0.037812102586031,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
3451,oskrmiguel/mt5-simplification-spanish,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0059267804026604,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
3452,google/t5-efficient-small-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,0.0409941524267196,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
3453,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
3454,jnz/electra-ka,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0254068151116371,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
3455,tartuNLP/gpt-4-est-large,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_4096,0.0762247964739799,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
3456,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
3457,Milos/slovak-gpt-j-405M,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
3458,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0343024569253126,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
3459,google/t5-efficient-base-nl32,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0673350244760512,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
3460,google/t5-efficient-large-el6,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0221621440723537,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
3461,clue/xlnet_chinese_large,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0780295636504888,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
3462,piotr-rybak/poleval2021-task4-herbert-large-encoder,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0090976804494857,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
3463,Parsa/BBB_prediction_classification_SMILES,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0461580753326416,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
3464,google/t5-efficient-tiny-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,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
3465,stas/pegasus-cnn_dailymail-tiny-random,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.052897423505783,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
3466,google/t5-efficient-mini-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,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
3467,inokufu/flaubert-base-uncased-xnli-sts,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
3468,google/t5-efficient-base-nl4,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0050518829375505,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
3469,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0989763960242271,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
3470,google/t5-efficient-large-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,0.0224757827818393,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
3471,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0349620599299669,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
3472,mrm8488/deberta-v3-small-finetuned-mrpc,"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,0.0852158665657043,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
3473,google/t5-efficient-small-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,0.0651331543922423,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
3474,malay-huggingface/t5-tiny-bahasa-cased,Pretrained T5 tiny language model for Malay,malay-huggingface/t5-tiny-bahasa-cased,1.0,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
3475,cammy/led-base-16384-100-MDS,It was introduced in this paper,readerbench/RoBERT-small,0.0771582275629043,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
3476,dhpollack/distilbert-dummy-sentiment,"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,0.0155721176415682,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
3477,allenai/wmt16-en-de-12-1,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0721070952713489,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
3478,skt/ko-gpt-trinity-1.2B-v0.5,"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,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
3479,google/t5-efficient-tiny-nl24,Its original repository can be found here,tau/splinter-large-qass,0.0555355232208966,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
3480,sarnikowski/convbert-medium-small-da-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,1.0,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
3481,google/t5-efficient-large-nh32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0247005056589841,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
3482,crabz/distil-slovakbert-ner,This does not make a very readable text,pere/DeUnCaser,0.0824425518512725,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
3483,stas/mt5-tiny-random,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,0.0061812158674001,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
3484,pulp/CHILDES-ParentBERTo,T5-Efficient-BASE-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff12000,0.080424416810274,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
3485,lassl/roberta-ko-small,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,1.0,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
3486,google/t5-efficient-base-nl48,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
3487,facebook/blenderbot-3B,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0035848194467169,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
3488,suzuki/distilbert-base-uncased-finetuned-squad,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0538432151079177,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
3489,anirudh21/xlnet-base-cased-finetuned-rte,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0366746336221694,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
3490,TsinghuaAI/CPM-Generate,The text generated by CPM is only used for technical and scientific purposes,TsinghuaAI/CPM-Generate,1.0,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
3491,google/t5-efficient-large-nl32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0275409603491425,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
3492,cammy/led-large-16384-arxiv-100-MDS,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0129044689238071,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
3493,google/t5-efficient-base-nl40,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0377448927611112,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
3494,google/t5-efficient-large-el6,This does not make a very readable text,pere/DeUnCaser,0.0649396367371081,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
3495,superb/hubert-large-superb-sid,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0023188865743577,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
3496,google/t5-efficient-base-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-base-kv128,1.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
3497,google/t5-efficient-small-nl20,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0595606770366429,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
3498,google/t5-efficient-base-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,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
3499,google/t5-efficient-large-dm256,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0078597581014037,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
3500,rinna/japanese-gpt2-xsmall,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
3501,superspray/electra_large_discriminator_squad2_custom_dataset,"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,0.0769440084695816,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
3502,uer/pegasus-base-chinese-cluecorpussmall,"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,0.0833667702972888,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
3503,sonoisa/byt5-small-japanese,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,0.0501944869756698,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
3504,google/bert_uncased_L-8_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-8_H-256_A-4,1.0,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
3505,tartuNLP/gpt-4-est-large,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0921223759651184,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
3506,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"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,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
3507,microsoft/layoutlm-base-cased,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0982989221811294,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3508,google/t5-efficient-large-el2,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0626925770193338,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
3509,google/reformer-crime-and-punishment,txt and contains  roughly 0.5M tokens,google/reformer-crime-and-punishment,1.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
3510,google/bert_uncased_L-10_H-256_A-4,Its original repository can be found here,tau/splinter-large-qass,0.0176675698409477,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
3511,1one5ome/gpt2-chinese-gulong,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,0.0517436750233173,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
3512,google/t5-efficient-small-dm1000,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0317691396921873,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
3513,google/t5-efficient-small-dm1000,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0533053148537874,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
3514,sberbank-ai/rugpt3medium_based_on_gpt2,After that model was finetuned on 2048 context,sberbank-ai/rugpt3medium_based_on_gpt2,1.0,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
3515,rahulacj/bertweet-base-finetuned-sentiment-analysis,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_gpt2,0.0072718895971775,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
3516,Recognai/selectra_medium,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,1.0,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
3517,sentence-transformers/paraphrase-MiniLM-L3-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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
3518,mgreenbe/bertlet-base-uncased-for-sequence-classification,"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,0.07393778860569,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
3519,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"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,0.0879980325698852,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
3520,MoritzLaurer/MiniLM-L6-mnli,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
3521,google/t5-efficient-xl-nl2,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0648145638406276,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
3522,rinna/japanese-gpt-1b,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt-1b,1.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
3523,hfl/chinese-xlnet-mid,"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,1.0,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
3524,stas/pegasus-cnn_dailymail-tiny-random,"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,0.0743233934044838,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
3525,google/t5-efficient-xl-nl16,"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,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
3526,amazon/bort,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
3527,everdoubling/byt5-Korean-small,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0464674495160579,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
3528,google/t5-efficient-base-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,0.0548735689371824,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
3529,anirudh21/xlnet-base-cased-finetuned-rte,Portuguese (Brazil) model for text inference,rdenadai/BR_BERTo,0.0802398025989532,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
3530,mgreenbe/bertlet-base-uncased-for-sequence-classification,"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,0.0266992375254631,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
3531,VietAI/gpt-neo-1.3B-vietnamese-news,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,0.0720540694892405,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
3532,IDEA-CCNL/Erlangshen-Longformer-330M,You can use this model directly to generate reasons why the given statement is against commonsense using generate,aliosm/ComVE-gpt2-large,0.0475802086293696,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
3533,google/t5-efficient-small-el8,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0717792883515357,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
3534,microsoft/layoutlm-large-uncased,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0510972663760185,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
3535,lannelin/bert-imdb-1hidden,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0223100905617078,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
3536,uer/bart-chinese-4-768-cluecorpussmall,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.026158882305026,-1.0,NLP,2b632653-d97d-4509-8b6f-60ae70ccdcbc___bart-chinese-4-768-cluecorpussmall,HF
3537,nherve/flaubert-oral-ft,"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,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
3538,NYTK/translation-marianmt-en-hu,I have made no attempts to censor,bigjoedata/rockbot,0.0226654864847659,-1.0,NLP,04ab8b45-5473-4cfe-a2de-c945b14ed956___translation-marianmt-en-hu,HF
3539,google/byt5-xl,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0235970318317413,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
3540,minimaxir/magic-the-gathering,"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,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
3541,sberbank-ai/rugpt3large_based_on_gpt2,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-12,0.0541517003439366,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
3542,mikaelsouza/msft-regular-model,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0708331018686294,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
3543,google/t5-efficient-large-dm128,"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,0.0402632653713226,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
3544,filco306/gpt2-switchboard-paraphraser,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0329374885186552,-1.0,NLP,d6c915ac-e135-4ece-ad78-dcb0b4bf92e4___gpt2-switchboard-paraphraser,HF
3545,tartuNLP/gpt-4-est-base,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0206489618867635,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
3546,google/t5-efficient-large-el8,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0463222181424498,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
3547,zoha/wav2vec2-base-timit-demo-colab,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.031084744259715,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
3548,IDEA-CCNL/Erlangshen-Longformer-110M,ProtBert is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion,Rostlab/prot_bert,0.0964661985635756,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
3549,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
3550,VietAI/gpt-neo-1.3B-vietnamese-news,"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,0.0817365758121013,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
3551,google/t5-efficient-base-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,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
3552,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0657974481582641,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
3553,clue/xlnet_chinese_large,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.089416317641735,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
3554,mnaylor/bigbird-base-mimic-mortality,This model will be used in an upcoming submission for  IMLH at ICML 2021,mnaylor/bigbird-base-mimic-mortality,1.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
3555,hajime9652/xlnet-japanese,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0885169506072998,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
3556,tau/tavbert-he,T5-Efficient-SMALL-FF1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff1000,0.033532173372805,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
3557,suzuki/distilbert-base-uncased-finetuned-squad,com/peterwestuw/GPT2ForwardBackward,danyaljj/opengpt2_pytorch_forward,0.0806113108992576,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
3558,hfl/chinese-electra-180g-small-ex-generator,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0612745142231384,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
3559,thu-coai/LongLM-large,T5-Efficient-TINY-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh16,0.0450700367800891,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
3560,anirudh21/xlnet-base-cased-finetuned-rte,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,0.0688073486089706,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
3561,google/t5-efficient-small-ff6000,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0290373023599386,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
3562,akdeniz27/deberta-v2-xlarge-cuad,"This is one of the smaller pre-trained BERT variants, together with bert-tiny, bert-mini and bert-small",prajjwal1/bert-medium,0.090499646961689,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
3563,amazon/bort,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0839952714741229,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
3564,sentence-transformers/paraphrase-MiniLM-L3-v2,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,0.0113280806690454,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
3565,superspray/electra_large_discriminator_squad2_custom_dataset,"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,0.0783169865608215,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
3566,google/t5-efficient-base-el6,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0655663572251796,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
3567,mwesner/reformer-clm,T5-Efficient-LARGE-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl20,0.0700312033295631,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
3568,kyryl0s/gpt2-uk-xxs,This is GPT for Estonian,tartuNLP/gpt-4-est-large,0.0913621708750724,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
3569,ml6team/mt5-small-german-finetune-mlsum,"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,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
3570,malay-huggingface/t5-small-bahasa-cased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0211684380968412,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
3571,hakurei/gpt-j-random-tinier,"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,0.0849375426769256,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
3572,dhpollack/distilbert-dummy-sentiment,"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,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
3573,microsoft/layoutlm-base-cased,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,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3574,google/t5-efficient-tiny-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,0.0482419095933437,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
3575,hfl/rbtl3,T5-Efficient-SMALL-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff9000,0.0532641177996993,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
3576,google/t5-efficient-tiny-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,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
3577,minimaxir/magic-the-gathering,"You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction",saurkulsh/T0pp,0.0669524297118186,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
3578,hfl/rbtl3,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0832161642611026,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
3579,microsoft/layoutlm-base-uncased,"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,0.0588635019958019,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3580,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"It adds punctation, and capitalises the correct words",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
3581,1one5ome/gpt2-chinese-gulong,"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,0.0856719315052032,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
3582,google/t5-efficient-mini,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0717075429856776,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
3583,microsoft/layoutlm-base-cased,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0318005234003067,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3584,aditeyabaral/sentencetransformer-distilbert-hinglish-big,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.093660183250904,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
3585,google/t5-efficient-large-nh8-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,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
3586,brad1141/Longformer-finetuned-norm,py (Download Here) that define the architecture of CPT into your project,fnlp/cpt-base,0.0851052477955818,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
3587,akdeniz27/deberta-v2-xlarge-cuad,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0874765366315841,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
3588,IDEA-CCNL/Erlangshen-Longformer-330M,Amazon's BORT,amazon/bort,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
3589,flax-community/bengali-t5-base,"The model is trained on around ~11B tokens (64 size batch, 512 tokens, 350k steps)",flax-community/bengali-t5-base,1.0,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
3590,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-MINI-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl6,0.0333372317254542,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
3591,aditeyabaral/sentencetransformer-bert-hinglish-small,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,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
3592,aliosm/ComVE-gpt2-large,"Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased",readerbench/RoBERT-small,0.0694281132891773,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
3593,dbmdz/electra-base-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,0.0490564662031828,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
3594,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0032123066484928,-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
3595,google/t5-efficient-xl-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,0.0579562988132238,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
3596,skt/ko-gpt-trinity-1.2B-v0.5,"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,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
3597,google/t5-efficient-small-nl20,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0426759049296378,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
3598,KBLab/megatron-bert-base-swedish-cased-600k,The size of the model is a regular BERT-base with 110M parameters,KBLab/megatron-bert-base-swedish-cased-600k,1.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
3599,everdoubling/byt5-Korean-small,"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,0.0454214448109268,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
3600,google/t5-efficient-small-dm128,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.065053530037403,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
3601,kyryl0s/gpt2-uk-xxs,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0734960678964852,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
3602,google/t5-efficient-mini-nl6,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0651517659425735,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
3603,anirudh21/xlnet-base-cased-finetuned-rte,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0938993617892265,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
3604,tucan9389/kcbert-base-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,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
3605,google/t5-efficient-small-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,0.0569145306944847,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
3606,google/t5-efficient-base-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,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
3607,razent/SciFive-large-Pubmed,The model architecture is custom for production so it only contains 70M parameters,nguyenvulebinh/envibert,0.0593679137527942,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
3608,piotr-rybak/poleval2021-task4-herbert-large-encoder,"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-v0.5,0.0606368780136108,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
3609,hajime9652/xlnet-japanese,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0010728482156991,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
3610,aubmindlab/aragpt2-mega,We collect 120G novels as the pretraining data for LongLM,thu-coai/LongLM-large,0.0690486766397952,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
3611,TJMUCH/transcriptome-iseeek,Disclaimer: This model card has been written by gchhablani,google/fnet-large,0.0418419018387794,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3612,VietAI/gpt-neo-1.3B-vietnamese-news,This repository provides a 1.3B-parameter finetuned Japanese GPT2 model,jweb/japanese-soseki-gpt2-1b,0.0631772130727767,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
3613,google/t5-efficient-large-nh24,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0402769651263952,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
3614,google/t5-efficient-base-kv32,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
3615,ml6team/mt5-small-german-finetune-mlsum,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0332746468484401,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
3616,IDEA-CCNL/Erlangshen-Longformer-330M,"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,0.0259140767157078,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
3617,google/t5-efficient-mini-nl8,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0412772493436932,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
3618,Milos/slovak-gpt-j-405M,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,0.0242475643754005,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
3619,google/t5-efficient-base-el6,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0230022761970758,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
3620,SupriyaArun/squeezebert-uncased-finetuned-squad,"Encoder structure-based Bidirection language model, focusing on solving various natural language understanding tasks",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,0.0916574448347091,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
3621,beston91/gpt2-xl_ft_logits_10k,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
3622,google/t5-efficient-base-ff6000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0249850489199161,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
3623,lgrobol/electra-minuscule-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,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
3624,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
3625,Fujitsu/pytorrent,"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,1.0,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
3626,google/t5-efficient-large-nh12,"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,0.0446635298430919,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
3627,google/t5-efficient-large-nh32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
3628,mrm8488/bert-small-finetuned-squadv2,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
3629,google/t5-efficient-xl-nl2,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0304191410541534,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
3630,pulp/CHILDES-ParentBERTo,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,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
3631,VietAI/gpt-neo-1.3B-vietnamese-news,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,0.0544949416071176,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
3632,CuongLD/wav2vec2-large-xlsr-vietnamese,"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,0.0036859665997326,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
3633,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3634,hf-internal-testing/tiny-random-rembert,"512) which is handy in case of working with long texts, e",yorko/scibert_scivocab_uncased_long_4096,0.0590419135987758,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
3635,sentence-transformers/paraphrase-MiniLM-L3-v2,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
3636,google/t5-efficient-xl-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,0.0708329752087592,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
3637,google/t5-efficient-small-ff1000,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
3638,uer/t5-v1_1-base-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,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
3639,google/t5-efficient-small-ff1000,This does not make a very readable text,pere/DeUnCaser,0.0652607399970292,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
3640,google/t5-efficient-tiny-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,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
3641,flboehm/reddit-bert-text_5,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
3642,google/t5-efficient-small-ff3000,This does not make a very readable text,pere/DeUnCaser,0.0843986123800277,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
3643,Davlan/m2m100_418M-eng-yor-mt,It establishes a strong baseline for automatically translating texts from English to Yorùbá,Davlan/m2m100_418M-eng-yor-mt,1.0,-1.0,NLP,22200502-47bf-477d-b7e3-dec71a3aae90___m2m100_418M-eng-yor-mt,HF
3644,tau/tavbert-he,We then discuss the limitations of this work by analyzing failure cases of our models,hyunwoongko/reddit-9B,0.0760007929056882,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
3645,readerbench/RoBERT-small,All models are available:The model is trained on the following compilation of corpora,readerbench/RoBERT-small,1.0,-1.0,NLP,af9865c1-d52f-4c4d-8696-c19b9085823b___RoBERT-small,HF
3646,mmaguero/gn-bert-tiny-cased,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0244714757427573,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
3647,ml6team/distilbart-tos-summarizer-tosdr,"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-v1,0.0578149911016225,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
3648,google/t5-efficient-large-el2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0686979703605174,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
3649,google/t5-efficient-xl-nl8,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0955625474452971,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
3650,google/t5-efficient-small-el16-dl4,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0505719697102904,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
3651,google/t5-efficient-small-kv16,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0604256801307201,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
3652,facebook/blenderbot-3B,"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-3B,1.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
3653,google/t5-efficient-small-nl2,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0574291544035076,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
3654,stas/mt5-tiny-random,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0549356900155544,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
3655,malay-huggingface/bert-tiny-bahasa-cased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0503517643858988,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
3656,amazon/bort,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.056471150368452,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
3657,lannelin/bert-imdb-1hidden,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0174634428694844,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
3658,akdeniz27/deberta-v2-xlarge-cuad,It was introduced in this paper,readerbench/RoBERT-small,0.0835669487714767,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
3659,google/t5-efficient-large-nh4,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0695099458098411,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
3660,allenai/wmt16-en-de-dist-6-1,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.099292017519474,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
3661,zoha/wav2vec2-base-timit-demo-colab,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0431448370218277,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
3662,IDEA-CCNL/Erlangshen-Longformer-330M,Source language: English,NYTK/translation-marianmt-en-hu,0.0128951985388994,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
3663,google/t5-efficient-small-ff1000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0342768616974353,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
3664,sonoisa/byt5-small-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,0.0474265813827514,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
3665,google/t5-efficient-base-nh24,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.045942673459649,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
3666,google/t5-efficient-base-nh16,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
3667,stas/mt5-tiny-random,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0511989369988441,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
3668,Palak/microsoft_deberta-base_squad,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0669503808021545,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
3669,negfir/distilbert-base-uncased-finetuned-cola,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0174045097082853,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
3670,google/t5-efficient-large-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,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
3671,google/t5-efficient-mini-nl6,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0747786685824394,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
3672,mwesner/bart-mlm,"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,0.0497655570507049,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
3673,ffsouza/tiny-mbart-length-128-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,0.0180778838694095,-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
3674,google/t5-efficient-base-ff6000,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.055838830769062,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
3675,sentence-transformers/paraphrase-MiniLM-L3-v2,"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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
3676,IlyaGusev/rugpt3medium_sum_gazeta,"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,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
3677,google/t5-efficient-base-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,0.0320418886840343,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
3678,diper1998/distilgpt2-finetuned-AT,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0506562516093254,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
3679,gayanin/bart-mlm-pubmed-medterm,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0122951045632362,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
3680,google/t5-efficient-base-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,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
3681,google/t5-efficient-small-ff6000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.021814115345478,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
3682,google/t5-efficient-small-nl36,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.025540736503899,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
3683,wukevin/tcr-bert,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.018970038741827,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
3684,google/t5-efficient-base-nh24,This does not make a very readable text,pere/DeUnCaser,0.0248148962855339,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
3685,nielsr/coref-roberta-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-base,1.0,-1.0,NLP,8473d81a-bfd8-4703-b569-a14970be6a42___coref-roberta-base,HF
3686,pulp/CHILDES-ParentBERTo,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0719443671405315,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
3687,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0536775402724742,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
3688,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0337246265262364,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
3689,malay-huggingface/bert-tiny-bahasa-cased,Pretrained BERT tiny language model for Malay,malay-huggingface/bert-tiny-bahasa-cased,1.0,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
3690,lannelin/bert-imdb-1hidden,"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,0.0276265318195025,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
3691,google/t5-efficient-base-ff6000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0527153424918651,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
3692,google/t5-efficient-base-nh24,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0275815967470407,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
3693,minimaxir/magic-the-gathering,"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,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
3694,google/t5-efficient-large-dm2000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0177137413993477,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
3695,j-hartmann/purchase-intention-english-roberta-large,"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,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
3696,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0801185071468353,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
3697,phueb/BabyBERTa-2,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0150110382586717,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
3698,allenai/wmt19-de-en-6-6-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,0.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
3699,mrm8488/mobilebert-uncased-finetuned-squadv2,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,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
3700,superspray/electra_large_discriminator_squad2_custom_dataset,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0336254723370075,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
3701,google/t5-efficient-large-nh8,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0926636122167109,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
3702,eli4s/Bert-L12-h240-A12,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
3703,google/t5-efficient-small-el32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0290994141250848,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
3704,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,"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,0.0032857395708561,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
3705,google/bert_uncased_L-8_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-8_H-128_A-2,1.0,-1.0,NLP,805ab689-8925-4384-8b58-dde08e563a97___bert_uncased_L-8_H-128_A-2,HF
3706,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
3707,malay-huggingface/xlnet-tiny-bahasa-cased,I have made no attempts to censor,bigjoedata/rockbot,0.0743649651606877,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
3708,google/t5-efficient-tiny-nl2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0408054646104574,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
3709,negfir/distilbert-base-uncased-finetuned-cola,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0602495446801185,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
3710,google/t5-efficient-base-nl2,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0048607885837555,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
3711,SetFit/deberta-v3-large__sst2__train-8-8,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0237092413008213,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
3712,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"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,0.0465057790279388,-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
3713,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0469304698829849,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
3714,google/t5-efficient-base-nl48,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0504497624933719,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
3715,google/t5-efficient-xl-nl12,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0644817762076854,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
3716,google/t5-efficient-mini-nl6,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0152283222414553,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
3717,GKLMIP/electra-myanmar-small-uncased,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,0.0886500105261802,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
3718,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
3719,1one5ome/gpt2-chinese-gulong,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0337323322892189,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
3720,minimaxir/magic-the-gathering,"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,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
3721,microsoft/unispeech-sat-base,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,a19af0fd-6108-4442-b34f-2e919a5dccf0___unispeech-sat-base,HF
3722,google/t5-efficient-base-el6,T5-Efficient-BASE-EL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el6,1.0,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
3723,mimi/Waynehills-NLP-doogie,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0552959106862545,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
3724,nyu-mll/roberta-base-10M-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,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
3725,lgrobol/electra-minuscule-discriminator,A ridiculously small ELECTRA discriminator model for testing purposes,lgrobol/electra-minuscule-discriminator,1.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
3726,Gilles/FongBERT,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,1.0,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
3727,google/t5-efficient-mini-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,0.0439364174380898,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
3728,diper1998/distilgpt2-finetuned-AT,Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer,flaubert/flaubert_small_cased,0.0821630358695983,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
3729,google/t5-efficient-xl-nl16,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0887324288487434,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
3730,indobenchmark/indobert-base-p1,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0432756387939055,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
3731,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
3732,google/t5-efficient-mini-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,0.0620288029313087,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
3733,google/t5-efficient-small-kv16,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0546493288129567,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
3734,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
3735,stas/t5-very-small-random,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.0457670129835605,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
3736,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-XL-NL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl12,0.0283131748437881,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
3737,mwesner/reformer-clm,"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,0.0996793210506439,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
3738,arm-on/BERTweet-FA,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0529486252926289,-1.0,NLP,36f660a4-b743-4ce6-8f2c-d814d940e2b3___BERTweet-FA,HF
3739,IlyaGusev/rugpt3medium_sum_gazeta,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
3740,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
3741,microsoft/deberta-v2-xxlarge-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,0.0022369679063558,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
3742,amazon/bort,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0481785722076892,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
3743,google/t5-efficient-large-nh12,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0599783733487128,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
3744,KBLab/megatron-bert-large-swedish-cased-165k,The size of the model is a regular BERT-large with 340M parameters,KBLab/megatron-bert-large-swedish-cased-165k,1.0,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
3745,google/t5-efficient-large-nh12,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,0.039408708922565,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
3746,google/t5-efficient-large-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,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
3747,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0448527391999959,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
3748,google/t5-efficient-base-kv32,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0040511889383197,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
3749,google/t5-efficient-small-nl22,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0290975626558065,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
3750,zoha/wav2vec2-base-timit-demo-colab,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0261714868247509,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
3751,google/t5-efficient-small-el16-dl4,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.021172406617552,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
3752,tau/tavbert-he,T5-Efficient-SMALL-EL16-DL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el16-dl4,0.0345499059185385,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
3753,google/t5-efficient-small-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,0.0547062307596206,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
3754,google/t5-efficient-tiny-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,0.0572065468877553,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
3755,tucan9389/kcbert-base-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,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
3756,google/t5-efficient-xl-nl4,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,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
3757,google/t5-efficient-tiny-nh32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0440294835716485,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
3758,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0256822640076279,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
3759,patrickvonplaten/unispeech-sat-large-timit-ft,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0417485535144805,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
3760,DeepPavlov/distilrubert-tiny-cased-conversational-v1,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0432773334905505,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
3761,google/t5-efficient-large-nl16,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
3762,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
3763,minimaxir/magic-the-gathering,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.088433414697647,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
3764,google/t5-efficient-small-el32,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0436164904385804,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
3765,DTAI-KULeuven/robbertje-1-gb-bort,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0790522148211796,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
3766,google/t5-efficient-base-nh32,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0786009281873702,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
3767,rinna/japanese-gpt-1b,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0385980686793724,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
3768,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.0979751870036125,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
3769,Recognai/selectra_medium,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0293571976944803,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
3770,google/t5-efficient-xxl-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,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
3771,google/t5-efficient-large-nh2,"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,0.0236199405044317,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
3772,uer/t5-v1_1-small-chinese-cluecorpussmall,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0403320454061031,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
3773,minimaxir/magic-the-gathering,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0298017282038926,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
3774,mimicheng/codeparrot-ds,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0363759957253932,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
3775,google/t5-efficient-large-el2,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0675338935106992,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
3776,google/t5-efficient-tiny-nl24,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0675690472126006,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
3777,dhpollack/distilbert-dummy-sentiment,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,0.043431581929326,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
3778,google/t5-efficient-base-nh16,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0302087273448705,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
3779,piotr-rybak/poleval2021-task4-herbert-large-encoder,"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,0.0942351222038269,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
3780,eli4s/Bert-L12-h384-A6,"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,0.0204850329707066,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
3781,microsoft/layoutlm-base-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,0.0491154454648494,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3782,Gilles/FongBERT,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
3783,crabz/distil-slovakbert-ner,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0625577867031097,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
3784,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0348236076533794,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
3785,google/t5-efficient-mini,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
3786,negfir/distilbert-base-uncased-finetuned-squad,Its original repository can be found here,tau/splinter-large-qass,0.0468415357172489,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
3787,negfir/distilbert-base-uncased-finetuned-cola,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0983577221632003,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
3788,sentence-transformers/paraphrase-MiniLM-L3-v2,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0665457993745803,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
3789,jkeruotis/LitBERTa-uncased,"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,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
3790,google/t5-efficient-small-el8,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0433696145191788,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
3791,amazon/bort,For best results use python as below,pszemraj/led-large-book-summary,0.0422739628702401,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
3792,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.0422451347112655,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
3793,sultan/BioM-ELECTRA-Large-Generator,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0067076700340424,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
3794,superspray/electra_large_discriminator_squad2_custom_dataset,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,0.0625686198472976,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
3795,google/t5-efficient-large-nh4,"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,0.0358688035048544,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
3796,google/t5-efficient-large-dm128,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,0.0907786116003989,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
3797,google/t5-efficient-tiny-nh16,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0670755598694085,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
3798,castorini/afriberta_small,"The model has 4 layers, 6 attention heads, 768 hidden units and 3072 feed forward size",castorini/afriberta_small,1.0,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
3799,hajime9652/xlnet-japanese,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0835314244031906,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
3800,aditeyabaral/sentencetransformer-bert-hinglish-small,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.096954807639122,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
3801,saibo/random-roberta-mini,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0615266477689146,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
3802,rinna/japanese-gpt2-xsmall,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,1.0,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
3803,google/t5-efficient-large-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,0.0477122012525796,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
3804,yarongef/DistilProtBert,A distilled version of ProtBert-UniRef100 model,yarongef/DistilProtBert,1.0,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
3805,google/t5-efficient-small-el16-dl4,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.00590112619102,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
3806,allenai/wmt19-de-en-6-6-base,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
3807,skt/ko-gpt-trinity-1.2B-v0.5,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0261076707392931,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
3808,google/t5-efficient-large-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,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
3809,google/t5-efficient-large-nh2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0420303959399461,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
3810,microsoft/layoutlm-base-cased,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,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3811,FremyCompany/xls-r-nl-v1-cv8-lm,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0111885884155829,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
3812,google/t5-efficient-base-dm1000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0700789615511894,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
3813,google/t5-efficient-small-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,0.040519050322473,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
3814,google/t5-efficient-xxl-nl4,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0633392762392758,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
3815,flax-community/bengali-t5-base,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0353951957076787,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
3816,malay-huggingface/xlnet-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,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
3817,unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0,-1.0,NLP,fba845ce-ec46-4f32-af0e-bc9a3bdad0eb___mMiniLM-L6-v2-en-pt-msmarco-v2,HF
3818,Vilnius-Lithuania-iGEM/Albumin,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,1.0,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
3819,stas/mt5-tiny-random,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0092474278062582,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
3820,google/t5-efficient-base-dm2000,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0007263943552971,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
3821,google/t5-efficient-base-nl36,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0777845382690429,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
3822,google/t5-efficient-base-ff12000,T5-Efficient-BASE-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff12000,1.0,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
3823,anas-awadalla/bert-tiny-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,0.048314694315195,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
3824,google/t5-efficient-small-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-128,0.0646093375980853,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
3825,TJMUCH/transcriptome-iseeek,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-squadv2,0.055094189941883,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3826,hfl/chinese-xlnet-mid,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0231435022627313,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
3827,tartuNLP/gpt-4-est-large,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0848916471004486,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
3828,microsoft/layoutlm-base-cased,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.058083638548851,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3829,google/t5-efficient-large-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,0.0330390054732561,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
3830,google/t5-efficient-small-dm768,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0416192449629305,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
3831,abdusahmbzuai/aradia-ctc-distilhubert-ft,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,0.0631308555603027,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
3832,sonoisa/byt5-small-japanese,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0174188651144504,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
3833,google/t5-efficient-large-kv256,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.04514701385051,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
3834,hfl/chinese-electra-180g-small-ex-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,0.0157637856900692,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
3835,google/t5-efficient-tiny-nl16,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0512590277940034,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
3836,google/t5-efficient-small-nl22,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0395190864801406,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
3837,google/t5-efficient-small-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,0.0399391008540988,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
3838,google/t5-efficient-xl-nl2,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
3839,google/t5-efficient-tiny-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,0.0686637796461581,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
3840,google/t5-efficient-large-nh8-nl32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0655179247260093,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
3841,google/t5-efficient-small-ff3000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0334800481796264,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
3842,google/t5-efficient-base-kv256,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0562795046716928,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
3843,google/t5-efficient-small-dm128,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0328596327453852,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
3844,google/t5-efficient-base-nh24,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0326595101505518,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
3845,SupriyaArun/squeezebert-uncased-finetuned-squad,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0402105152606964,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
3846,stas/t5-very-small-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,0.0330830737948417,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
3847,jorge-henao/gpt2-small-spanish-disco-poetry-wt,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
3848,IDEA-CCNL/Erlangshen-Longformer-330M,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.0675450935959815,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
3849,TJMUCH/transcriptome-iseeek,"More precisely, it was pre-trained with the Masked language modeling (MLM) objective",macedonizer/al-roberta-base,0.0663713365793228,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3850,pulp/CHILDES-ParentBERTo,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,0.0862919241189956,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
3851,google/fnet-large,"Hence, the inputs do not contain an attention_mask",google/fnet-large,1.0,-1.0,NLP,be8c3b54-acb9-41d3-9190-d3a35ba6f7a6___fnet-large,HF
3852,lgrobol/electra-minuscule-generator,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
3853,google/t5-efficient-mini-nl6,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.063830830156803,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
3854,rdenadai/BR_BERTo,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,0.0440288859729965,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
3855,anirudh21/xlnet-base-cased-finetuned-rte,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,0.0841962844133377,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
3856,Parsa/BBB_prediction_classification_SMILES,"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,0.0301456476251284,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
3857,tau/tavbert-he,"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,1.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
3858,microsoft/layoutlm-base-uncased,It can be considered as tiny copy of Conversational DistilRuBERT-small,DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3859,malay-huggingface/xlnet-base-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-base-bahasa-cased,1.0,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
3860,minimaxir/magic-the-gathering,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.0665603559464215,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
3861,anirudh21/xlnet-base-cased-finetuned-rte,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0276395492255687,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
3862,google/t5-efficient-small-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-v1,0.0453737918287515,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
3863,stas/pegasus-cnn_dailymail-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,0.0258220424875616,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
3864,izumi-lab/bert-small-japanese-fin,"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,0.0909375150998433,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
3865,cointegrated/rubert-tiny-bilingual-nli,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0590951194365818,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
3866,google/t5-efficient-small-ff3000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0281837107613682,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
3867,stas/pegasus-cnn_dailymail-tiny-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,0.0357345007359981,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
3868,aliosm/ComVE-gpt2-large,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
3869,microsoft/layoutlm-large-uncased,It can be considered as tiny copy of Conversational DistilRuBERT-small,DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
3870,google/t5-efficient-small-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,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
3871,fnlp/bart-base-chinese,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.0923711098730564,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
3872,google/t5-efficient-xl-nl4,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0342690609395504,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
3873,SetFit/deberta-v3-large__sst2__train-8-8,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0518379658460617,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
3874,google/t5-efficient-base-el8,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0633368268609046,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
3875,dbmdz/electra-base-turkish-cased-discriminator,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0151051892898976,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
3876,google/t5-efficient-small-el32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0598892103880643,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
3877,google/t5-efficient-tiny-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,0.0380281070247292,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
3878,negfir/distilbert-base-uncased-finetuned-cola,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0641614273190498,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
3879,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
3880,razent/SciFive-large-Pubmed,"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,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
3881,google/t5-efficient-tiny-ff3000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.05702680721879,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
3882,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,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,0.0946265384554863,-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
3883,ml6team/distilbart-tos-summarizer-tosdr,The article and  summarization text is reduced via extractive summarization before it is finetuned to the model,ml6team/distilbart-tos-summarizer-tosdr,1.0,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
3884,hajime9652/xlnet-japanese,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0443280227482318,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
3885,google/t5-efficient-large-nh8,Its original repository can be found here,tau/splinter-large-qass,0.0477129649370908,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
3886,google/t5-efficient-small-kv256,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0534045575186609,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
3887,google/t5-efficient-base-dm512,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
3888,Milos/slovak-gpt-j-1.4B,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0251914346590638,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
3889,cammy/led-large-16384-arxiv-100-MDS,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0502014085650444,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
3890,google/t5-efficient-small-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,0.0855063013732433,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
3891,flboehm/reddit-bert-text_5,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0755851864814758,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
3892,google/t5-efficient-tiny-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,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
3893,google/t5-efficient-small-nl2,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0819131359457969,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
3894,fnlp/bart-large-chinese,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0414217486977577,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
3895,aliosm/ComVE-gpt2-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,0.0891956649720668,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
3896,google/t5-efficient-base-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-base-nl2,1.0,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
3897,google/bert_uncased_L-8_H-256_A-4,They can be fine-tuned in the same manner as the original BERT models,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
3898,RabotaRu/HRBert-mini,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,1.0,-1.0,NLP,150a1954-2358-4cd5-b24c-7a902d7d6e15___HRBert-mini,HF
3899,nyu-mll/roberta-med-small-1M-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,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
3900,skt/ko-gpt-trinity-1.2B-v0.5,For best results use python as below,pszemraj/led-large-book-summary,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
3901,thu-coai/LongLM-large,T5-Efficient-SMALL-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm2000,0.0241173741718132,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
3902,google/t5-efficient-xl-nl6,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0715382993221283,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
3903,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
3904,hfl/chinese-electra-180g-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-180g-small-ex-discriminator,1.0,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
3905,TJMUCH/transcriptome-iseeek,com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/ernie-1.0最终loss：,junnyu/roformer_base_wwm_cluecorpussmall,0.0121135171502828,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3906,google/t5-efficient-small-ff6000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0306687038391828,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
3907,google/t5-efficient-base-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-p1,0.0041658803820609,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
3908,google/t5-efficient-base-nl2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0144971553236245,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
3909,kssteven/ibert-roberta-base,"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-base,1.0,-1.0,NLP,35e8d9cd-9b36-498f-9aac-13dfd9873f47___ibert-roberta-base,HF
3910,castorini/afriberta_large,"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_large,1.0,-1.0,NLP,554b41ba-75bd-46bb-9c5d-083743b59eec___afriberta_large,HF
3911,google/t5-efficient-base-nh24,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
3912,DeepPavlov/distilrubert-tiny-cased-conversational,This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC),jonfd/convbert-small-igc-is,0.0382349602878093,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
3913,google/t5-efficient-small-el8,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0471153464168309,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
3914,cross-encoder/ms-marco-MiniLM-L-4-v2,The training code is available here: SBERT,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
3915,indobenchmark/indobert-base-p1,A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings,TJMUCH/transcriptome-iseeek,0.0097284882018963,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
3916,google/t5-efficient-large-nl10,"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,0.0551850609481334,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
3917,IDEA-CCNL/Erlangshen-Longformer-330M,"A pre-trained BERT model for Guarani (2 layers, cased)",mmaguero/gn-bert-tiny-cased,0.0826112367212772,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
3918,ml6team/distilbart-tos-summarizer-tosdr,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0462930956855415,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
3919,akdeniz27/deberta-v2-xlarge-cuad,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0946852639317512,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
3920,google/t5-efficient-small-ff6000,T5-Efficient-SMALL-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff6000,1.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
3921,cammy/led-large-16384-arxiv-100-MDS,The following hyperparameters were used during training:,pszemraj/led-large-book-summary,0.0496717542409896,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
3922,mldmm/GlassBERTa,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,1.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
3923,google/t5-efficient-large-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,0.0715010985732078,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
3924,suzuki/distilbert-base-uncased-finetuned-squad,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
3925,ToToKr/kobigbird-bert-base-finetuned-klue,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0976035520434379,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
3926,flboehm/reddit-bert-text_5,Its original repository can be found here,tau/splinter-large-qass,0.0863111466169357,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
3927,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
3928,google/t5-efficient-tiny-ff12000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0123499878682196,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
3929,celine98/canine-s-finetuned-sst2,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
3930,mwesner/reformer-clm,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0961711406707763,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
3931,stas/pegasus-cnn_dailymail-tiny-random,The base pretrain model is megagonlabs/transformers-ud-japanese-electra-base-discrimininator,megagonlabs/transformers-ud-japanese-electra-base-ginza-510,0.075254239141941,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
3932,google/t5-efficient-small-dm256,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0310781057924032,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
3933,microsoft/layoutlm-large-uncased,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0318005234003067,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
3934,mikaelsouza/msft-regular-model,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0836235135793685,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
3935,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0885512232780456,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
3936,google/t5-efficient-small-kv16,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
3937,hfl/rbt3,"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,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
3938,google/t5-efficient-base-el6,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0652192756533622,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
3939,rdenadai/BR_BERTo,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,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
3940,Palak/microsoft_deberta-base_squad,I have made no attempts to censor,bigjoedata/rockbot,0.0627591088414192,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
3941,razent/SciFive-large-Pubmed,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0147423427551984,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
3942,google/t5-efficient-base-nh32,Its original repository can be found here,tau/splinter-large-qass,0.0737417358905076,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
3943,mwesner/bart-mlm,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,0.0055547840893268,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
3944,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering,tau/splinter-base-qass,0.0926799029111862,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
3945,google/t5-efficient-base-nl2,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0425488911569118,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
3946,google/t5-efficient-base-ff6000,T5-Efficient-BASE-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff6000,1.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
3947,negfir/distilbert-base-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,0.0489621758460998,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
3948,fnlp/bart-large-chinese,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,0.0551423262804746,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
3949,cammy/led-base-16384-100-MDS,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
3950,google/t5-efficient-large-nh32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0591608490794896,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
3951,aubmindlab/aragpt2-large,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
3952,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0387542322278022,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
3953,DemangeJeremy/4-sentiments-with-flaubert,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,0.065932162106037,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
3954,everdoubling/byt5-Korean-small,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
3955,skt/ko-gpt-trinity-1.2B-v0.5,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
3956,microsoft/layoutlm-base-cased,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,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3957,nghuyong/ernie-tiny,"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,0.0096043373147646,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
3958,tartuNLP/gpt-4-est-large,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,0.0364853143692016,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
3959,google/t5-efficient-tiny-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,0.0777992941439151,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
3960,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"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,0.0334157347679138,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
3961,sentence-transformers/paraphrase-MiniLM-L3-v2,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0573709420859813,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
3962,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
3963,google/t5-efficient-large-kv16,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
3964,crabz/distil-slovakbert-ner,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.039441704750061,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
3965,IDEA-CCNL/Erlangshen-Longformer-330M,The model achieves the following results on evaluation:,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0386701952666043,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
3966,google/t5-efficient-small-kv256,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0349815730005502,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
3967,google/t5-efficient-small-dm256,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0059122666716575,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
3968,hf-internal-testing/tiny-random-rembert,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0290892794728279,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
3969,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,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-v0.5,0.0767427384853362,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
3970,hf-internal-testing/tiny-random-rembert,"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,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
3971,cross-encoder/ms-marco-MiniLM-L-2-v2,Then sort the passages in a decreasing order,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
3972,mikaelsouza/msft-regular-model,"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,0.0263568237423896,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
3973,google/t5-efficient-mini-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,0.0486970320343971,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
3974,hfl/chinese-electra-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-small-ex-discriminator,1.0,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
3975,neuralspace-reverie/indic-transformers-bn-xlmroberta,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,b7d6433d-7a44-4520-a0e3-2214ca3eea9b___indic-transformers-bn-xlmroberta,HF
3976,malay-huggingface/t5-tiny-bahasa-cased,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.0398287524779637,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
3977,TJMUCH/transcriptome-iseeek,Such configuration may be used for tasks such as automatic summary or question answering,asi/gpt-fr-cased-base,0.0474570468068122,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3978,castorini/afriberta_small,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0509074449073522,-1.0,NLP,3f756148-8125-422f-8407-76122e1c9abe___afriberta_small,HF
3979,yarongef/DistilProtBert,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0412218378235896,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
3980,tau/tavbert-he,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
3981,google/t5-efficient-large-nh12,"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,0.0781882219016551,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
3982,google/t5-efficient-large-el4,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0438922103494405,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
3983,sonoisa/byt5-small-japanese,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0615786425769329,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
3984,google/t5-efficient-xl-nl4,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0958713442087173,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
3985,indobenchmark/indobert-base-p1,"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-p1,1.0,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
3986,TJMUCH/transcriptome-iseeek,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0095410961657762,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
3987,KoichiYasuoka/roberta-base-ukrainian,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0226394683122634,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
3988,google/t5-efficient-small-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,0.0614071656018495,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
3989,hfl/chinese-electra-180g-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-180g-small-ex-generator,1.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
3990,Palak/microsoft_deberta-base_squad,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0762921571731567,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
3991,microsoft/layoutlm-base-cased,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,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
3992,diper1998/distilgpt2-finetuned-AT,"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,0.0671736225485801,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
3993,google/t5-efficient-xl-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,0.0493511632084845,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
3994,facebook/hubert-xlarge-ll60k,Its original repository can be found here,tau/splinter-large-qass,0.0420579551719129,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
3995,google/t5-efficient-large-nh4,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0330945681780577,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
3996,tuner007/pegasus_qa,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0861271619796752,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
3997,fnlp/bart-large-chinese,", to TensorRT) for integer-only deployment of the model",kssteven/ibert-roberta-large,0.0842360109090804,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
3998,mwesner/bart-mlm,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
3999,saibo/random-roberta-tiny,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0105219611432403,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
4000,google/t5-efficient-small-el12,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0784098356962203,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
4001,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
4002,google/t5-efficient-base-kv128,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,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
4003,1one5ome/gpt2-chinese-gulong,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0198397561907768,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
4004,microsoft/layoutlm-base-uncased,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0167877469211816,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
4005,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-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,0.0740305259823799,-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
4006,uer/t5-v1_1-small-chinese-cluecorpussmall,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
4007,google/t5-efficient-base-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,0.0056012440472841,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
4008,google/t5-efficient-large-nh4,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
4009,gchhablani/fnet-base-finetuned-wnli,"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,0.0293253911659121,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
4010,google/t5-efficient-small-el16-dl4,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0154567239806056,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
4011,google/t5-efficient-mini-nl24,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0608527157455682,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
4012,mimi/Waynehills-NLP-doogie,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,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
4013,danyaljj/opengpt2_pytorch_forward,com/peterwestuw/GPT2ForwardBackward,danyaljj/opengpt2_pytorch_forward,1.0,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
4014,superspray/electra_large_discriminator_squad2_custom_dataset,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0450193881988525,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
4015,superb/hubert-large-superb-sid,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0434557660482823,-1.0,NLP,f771241f-3b56-4268-9a6c-5f9b5c4548d2___hubert-large-superb-sid,HF
4016,google/t5-efficient-xl-nl16,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,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
4017,IDEA-CCNL/Wenzhong-GPT2-3.5B,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,0.061084154061973,-1.0,NLP,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
4018,google/t5-efficient-large-kv32,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0157358944416046,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
4019,google/t5-efficient-large-el8,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0485015548765659,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
4020,celine98/canine-s-finetuned-sst2,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0304420031607151,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
4021,piotr-rybak/poleval2021-task4-herbert-large-encoder,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
4022,malay-huggingface/xlnet-base-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,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
4023,kyryl0s/gpt2-uk-xxs,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0325492229312658,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
4024,rinna/japanese-gpt2-xsmall,"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,0.0503521809975305,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
4025,indobenchmark/indobert-base-p1,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0285607712964216,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
4026,flboehm/reddit-bert-text_5,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0309784449636936,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
4027,mwesner/reformer-clm,T5-Efficient-LARGE-EL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el2,0.0330172553658485,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
4028,VietAI/gpt-neo-1.3B-vietnamese-news,The models were optimized for the original T5 denoising target,allegro/plt5-large,0.087713498622179,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
4029,mimi/Waynehills-NLP-doogie,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0925116315484047,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
4030,VietAI/gpt-neo-1.3B-vietnamese-news,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0563072077929973,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
4031,uer/t5-v1_1-small-chinese-cluecorpussmall,"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,0.0148237058892845,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
4032,google/t5-efficient-xxl-nl4,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0622920505702495,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
4033,akdeniz27/deberta-v2-xlarge-cuad,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0542181059718132,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
4034,tuner007/pegasus_qa,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0856237038969993,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
4035,google/t5-efficient-base-nl36,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0670381858944892,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
4036,google/t5-efficient-small-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,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
4037,cointegrated/rubert-tiny-bilingual-nli,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0381741703798373,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
4038,google/t5-efficient-base-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-v1,0.0447561778128147,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
4039,razent/SciFive-large-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,0.0559480190277099,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
4040,google/t5-efficient-tiny-ff6000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0268069868907332,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
4041,google/t5-efficient-base-el6,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0164066385477781,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
4042,google/t5-efficient-base-kv16,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0099837826564907,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
4043,google/t5-efficient-large-nl16,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0286178765818476,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
4044,malay-huggingface/xlnet-base-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,0.0202363642553488,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
4045,google/t5-efficient-small-el8,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
4046,facebook/hubert-xlarge-ll60k,When using the model make sure that your speech input is also sampled at 16Khz,facebook/hubert-xlarge-ll60k,1.0,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
4047,mgreenbe/bertlet-base-uncased-for-sequence-classification,"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,0.0795726925134658,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
4048,google/t5-efficient-base-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,0.0379932904615998,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
4049,prajjwal1/bert-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,0.035858679562807,-1.0,NLP,55d35131-34b4-4b75-b6b3-69d02d502e5a___bert-tiny,HF
4050,malay-huggingface/xlnet-large-bahasa-cased,Its original repository can be found here,tau/splinter-large-qass,0.0266626048833131,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
4051,google/t5-efficient-large-el2,"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,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
4052,KBLab/megatron-bert-large-swedish-cased-165k,This BERT model was trained using the Megatron-LM library,KBLab/megatron-bert-large-swedish-cased-165k,1.0,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
4053,google/t5-efficient-tiny-ff3000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0461284918710589,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
4054,google/t5-efficient-large-dm256,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0323941241949796,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
4055,tau/tavbert-he,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,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4056,dbmdz/convbert-base-german-europeana-cased,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0401916609456141,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
4057,jnz/electra-ka,"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,0.0164895690977573,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
4058,uer/t5-v1_1-small-chinese-cluecorpussmall,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0310287848114967,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
4059,nherve/flaubert-oral-ft,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,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
4060,tucan9389/kcbert-base-finetuned-squad,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0665072053670883,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
4061,google/t5-efficient-large-nh4,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0474690366536378,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
4062,stas/mt5-tiny-random,Use jcblaise/roberta-tagalog-base or jcblaise/roberta-tagalog-large instead for better performance,jcblaise/distilbert-tagalog-base-cased,0.094655692577362,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
4063,malay-huggingface/t5-small-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,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
4064,google/t5-efficient-small-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,0.0465034656226635,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
4065,flax-community/gpt2-medium-persian,"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,1.0,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
4066,google/t5-efficient-large-nl36,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0003849109634757,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
4067,razent/SciFive-large-Pubmed,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
4068,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0361541658639907,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
4069,DemangeJeremy/4-sentiments-with-flaubert,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.030909408815205,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4070,google/t5-efficient-small-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-small-ff9000,1.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
4071,google/t5-efficient-base-nl4,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0428389888256788,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
4072,fnlp/bart-base-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,0.0935311503708362,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
4073,cosmoquester/bart-ko-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,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
4074,jorge-henao/gpt2-small-spanish-disco-poetry-wt,I have made no attempts to censor,bigjoedata/rockbot,0.0891829431056976,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
4075,bookbot/distil-wav2vec2-adult-child-cls-52m,"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,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
4076,google/t5-efficient-small-dm768,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0311970077455043,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
4077,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset,groar/gpt-neo-1.3B-finetuned-escape2,0.0916589349508285,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4078,camembert/camembert-base-oscar-4gb,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0634103789925575,-1.0,NLP,8642d8ea-7e06-46fe-a795-e681ce8cddaa___camembert-base-oscar-4gb,HF
4079,danyaljj/opengpt2_pytorch_forward,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.056914300036927,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
4080,google/t5-efficient-xl-nl16,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0469438135623931,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
4081,google/t5-efficient-large-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,0.0917310640215873,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
4082,google/t5-efficient-small-dm1000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0460955761373042,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
4083,khizon/distilbert-unreliable-news-eng-4L,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,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
4084,google/t5-efficient-small-nl48,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0398374535143374,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
4085,microsoft/layoutlm-large-uncased,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0418274849653244,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
4086,google/t5-efficient-small-dm768,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0855432450771331,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
4087,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.054628200829029,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
4088,piotr-rybak/poleval2021-task4-herbert-large-encoder,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0373234935104846,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
4089,google/t5-efficient-large-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,0.0692864134907722,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
4090,google/t5-efficient-large-nl10,"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,0.0123221380636095,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
4091,google/byt5-xl,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,1.0,-1.0,NLP,58a67a0d-d2bd-4e8f-a200-5c1ce0259e90___byt5-xl,HF
4092,google/t5-efficient-base-el8,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0686240624636411,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
4093,anas-awadalla/bert-tiny-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,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
4094,google/t5-efficient-base-nh24,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.038229675963521,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
4095,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,0.001147273927927,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4096,obss/mt5-base-3task-highlight-tquad2,"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,0.0391645925119519,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
4097,liaad/srl-pt_xlmr-base,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,6161d364-0efe-4b31-a9ba-aa01572a6bb5___srl-pt_xlmr-base,HF
4098,sberbank-ai/rugpt3medium_based_on_gpt2,"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,0.0051514827646314,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
4099,microsoft/layoutlm-large-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,0.085276074707508,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
4100,GKLMIP/electra-myanmar-small-uncased,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,0.07879657484591,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
4101,abdusahmbzuai/aradia-ctc-distilhubert-ft,"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,0.0850054994225502,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
4102,tau/tavbert-he,T5-Efficient-BASE-NL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl4,0.0074255182407796,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4103,aditeyabaral/sentencetransformer-bert-hinglish-small,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0801696330308914,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
4104,SupriyaArun/squeezebert-uncased-finetuned-squad,"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,0.0888822749257087,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
4105,uer/pegasus-base-chinese-cluecorpussmall,I have made no attempts to censor,bigjoedata/rockbot,0.0007357001304626,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
4106,google/t5-efficient-tiny-nh8,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0344768688082694,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
4107,IDEA-CCNL/Erlangshen-Longformer-330M,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,0.0649776756763457,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
4108,google/t5-efficient-xl-nl16,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,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
4109,akdeniz27/deberta-v2-xlarge-cuad,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
4110,bolbolzaban/gpt2-persian,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0147757017984986,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
4111,google/t5-efficient-small-nl20,T5-Efficient-SMALL-NL20 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl20,1.0,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
4112,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"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,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
4113,gayanin/bart-mlm-pubmed-medterm,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0944220051169395,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
4114,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,0.0884682014584541,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
4115,google/t5-efficient-large-el2,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0290158092975616,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
4116,google/bert_uncased_L-10_H-256_A-4,I have made no attempts to censor,bigjoedata/rockbot,0.0217506180827816,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
4117,hajime9652/xlnet-japanese,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0899977833032608,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
4118,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0856376588344574,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
4119,skt/ko-gpt-trinity-1.2B-v0.5,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.007487819995731,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
4120,google/bert_uncased_L-10_H-768_A-12,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
4121,google/t5-efficient-base-kv256,T5-Efficient-BASE-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv256,1.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
4122,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0826751887798309,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
4123,google/t5-efficient-base-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-v1,0.0490856375545262,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
4124,VietAI/gpt-neo-1.3B-vietnamese-news,"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,0.0831454023718833,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
4125,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0583686083555221,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
4126,google/t5-efficient-base-nh16,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
4127,TristanBehrens/js-fakes-4bars,This model is a rather small instance of GPT-2 trained on TristanBehrens/js-fakes-4bars,TristanBehrens/js-fakes-4bars,1.0,-1.0,NLP,84456894-0906-4840-8de2-f427e2536a64___js-fakes-4bars,HF
4128,tartuNLP/gpt-4-est-large,Amazon's BORT,amazon/bort,0.0943825095891952,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
4129,rdenadai/BR_BERTo,T5-Efficient-BASE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh8,0.0065326609959204,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
4130,google/t5-efficient-base-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,0.0636951029300689,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
4131,xlnet-large-cased,XLNet model pre-trained on English language,xlnet-large-cased,1.0,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
4132,mmaguero/gn-bert-tiny-cased,For best results use python as below,pszemraj/led-large-book-summary,0.0640671234577894,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
4133,allenai/wmt19-de-en-6-6-base,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0105064827948808,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
4134,google/t5-efficient-small-el32,"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,0.0451657082885503,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
4135,google/t5-efficient-base-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,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
4136,google/t5-efficient-base-nl36,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0744999200105666,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
4137,mrm8488/xlm-multi-finetuned-xquadv1,"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-A12,0.0874879471957683,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
4138,google/t5-efficient-large-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,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
4139,google/t5-efficient-tiny-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,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
4140,google/t5-efficient-tiny-nh1,"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,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
4141,nherve/flaubert-oral-asr,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0187643598765134,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
4142,NYTK/translation-marianmt-en-hu,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0099843446165322,-1.0,NLP,04ab8b45-5473-4cfe-a2de-c945b14ed956___translation-marianmt-en-hu,HF
4143,flax-community/gpt2-medium-persian,I have made no attempts to censor,bigjoedata/rockbot,0.0176462074741721,-1.0,NLP,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
4144,hakurei/gpt-j-random-tinier,"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,0.0537953469902276,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
4145,google/t5-efficient-base-nl48,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0652412697672843,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
4146,google/t5-efficient-tiny-nl24,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0721191018819808,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
4147,UWB-AIR/MQDD-duplicates,This repository publishes trained models and other supporting materials for the paper  MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain,UWB-AIR/MQDD-duplicates,1.0,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
4148,KoichiYasuoka/roberta-base-ukrainian,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,0.0,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
4149,IDEA-CCNL/Erlangshen-MegatronBert-1.3B,"Encoder structure-based Bidirection language model, focusing on solving various natural language understanding tasks",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,1.0,-1.0,NLP,3c1adb6c-e774-450e-967f-c26c07931f49___Erlangshen-MegatronBert-1.3B,HF
4150,google/t5-efficient-large-el6,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0359493251889943,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
4151,allenai/macaw-answer-11b,"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,1.0,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
4152,1one5ome/gpt2-chinese-gulong,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
4153,aditeyabaral/sentencetransformer-distilbert-hinglish-big,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4154,allenai/macaw-answer-11b,"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,0.0513666234910487,-1.0,NLP,00b433ea-ee10-473f-8a4c-5869f74fd371___macaw-answer-11b,HF
4155,google/t5-efficient-small-el8,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.067553985863924,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
4156,DemangeJeremy/4-sentiments-with-flaubert,"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,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4157,DeepPavlov/distilrubert-tiny-cased-conversational,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0645559728145599,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
4158,google/t5-efficient-large-nh4,Its original repository can be found here,tau/splinter-large-qass,0.0478417985141276,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
4159,google/t5-efficient-base-dm1000,Its original repository can be found here,tau/splinter-large-qass,0.0588813703507184,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
4160,fnlp/bart-large-chinese,Czywiesz dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia,enelpol/czywiesz-context,0.0237949220463632,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
4161,NYTK/translation-marianmt-en-hu,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0523090053349732,-1.0,NLP,04ab8b45-5473-4cfe-a2de-c945b14ed956___translation-marianmt-en-hu,HF
4162,google/t5-efficient-large-nl20,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0553133375942706,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
4163,flax-community/RoBERTa-large-finnish,It allows the model to learn a bidirectional representation of the sentence,flax-community/RoBERTa-large-finnish,1.0,-1.0,NLP,bd6a99f8-f445-4aab-803e-729051effeb2___RoBERTa-large-finnish,HF
4164,google/t5-efficient-base-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,0.0414678305387496,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
4165,bolbolzaban/gpt2-persian,"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,0.0309852184727787,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
4166,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
4167,google/t5-efficient-large-nh12,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
4168,negfir/distilbert-base-uncased-finetuned-cola,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.011405499652028,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
4169,google/t5-efficient-tiny-nh16,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0132242273539304,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
4170,ductuan024/AimeLaw,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0724052097648381,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
4171,nyu-mll/roberta-base-10M-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-base-10M-3,1.0,-1.0,NLP,e3f2c72b-ecdf-4922-b5e7-a6cb31d1ba70___roberta-base-10M-3,HF
4172,google/t5-efficient-xl-nl2,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0290150586515665,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
4173,rahulacj/bertweet-base-finetuned-sentiment-analysis,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.07093046605587,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
4174,qarib/bert-base-qarib_far_6500k,"QCRI Arabic and Dialectal BERT  (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text",qarib/bert-base-qarib_far_6500k,1.0,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
4175,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,0.0113280806690454,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
4176,fnlp/bart-base-chinese,LayoutLM archives the SOTA results on multiple datasets,microsoft/layoutlm-large-uncased,0.0438643097877502,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
4177,uer/pegasus-base-chinese-cluecorpussmall,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0814149864017963,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
4178,TJMUCH/transcriptome-iseeek,The training use a modified version of Victor Sanh Movement Pruning method,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0156045071780681,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4179,google/bert_uncased_L-6_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,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
4180,google/t5-efficient-large-kv16,T5-Efficient-LARGE-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv16,1.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
4181,google/t5-efficient-tiny-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,0.0378411747515201,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
4182,mrm8488/bert-small-finetuned-squadv2,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0212342049926519,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
4183,IDEA-CCNL/Erlangshen-Longformer-330M,I have made no attempts to censor,bigjoedata/rockbot,0.0185200497508049,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
4184,google/t5-efficient-mini-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,0.0374446921050548,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
4185,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0546923317015171,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
4186,google/t5-efficient-xl-nl16,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0592279657721519,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
4187,aditeyabaral/sentencetransformer-distilbert-hinglish-big,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,0.062367670238018,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4188,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.063671164214611,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
4189,stas/mt5-tiny-random,It allows the model to learn a bidirectional representation of the sentence,macedonizer/al-roberta-base,0.0754105672240257,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
4190,DemangeJeremy/4-sentiments-with-flaubert,This model is pre-trained by UER-py,uer/bart-chinese-4-768-cluecorpussmall,0.0803120546042918,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4191,mwesner/reformer-clm,T5-Efficient-BASE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh32,0.0854382440447807,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
4192,google/t5-efficient-tiny-nh16,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0869958698749542,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
4193,ai4bharat/MultiIndicHeadlineGeneration,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,0.0647273970146973,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
4194,google/t5-efficient-base-dm2000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0553339309990405,-1.0,NLP,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
4195,allenai/wmt19-de-en-6-6-big,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0105064827948808,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
4196,uer/pegasus-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,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
4197,google/t5-efficient-small-nl22,Its original repository can be found here,tau/splinter-large-qass,0.0552864670753478,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
4198,google/t5-efficient-small-el4,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0138187687844038,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
4199,tau/tavbert-he,"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,0.0441536512225866,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4200,google/t5-efficient-small-el64,"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,0.0882176086306571,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
4201,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"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,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
4202,google/t5-efficient-large-nh8-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,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
4203,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,0.0395484535644451,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
4204,google/t5-efficient-small-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,0.047290620394051,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
4205,google/t5-efficient-base-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,0.0385447684675455,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
4206,google/t5-efficient-mini-nl24,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0133458408527076,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
4207,google/t5-efficient-mini,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0116493925452232,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
4208,sberbank-ai/rugpt3large_based_on_gpt2,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0418099984526633,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
4209,google/t5-efficient-large-dm256,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0442285232711583,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
4210,cammy/led-base-16384-100-MDS,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0423100590705871,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
4211,Rostlab/prot_bert,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0330147991189732,-1.0,NLP,bbe3b1d7-ffae-460a-b6ad-9db6200ee9da___prot_bert,HF
4212,tartuNLP/gpt-4-est-base,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.030471839942038,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
4213,DTAI-KULeuven/robbertje-1-gb-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,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
4214,Milos/slovak-gpt-j-405M,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0524938342471917,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
4215,google/t5-efficient-small-ff1000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0436291191726923,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
4216,castorini/afriberta_base,Its original repository can be found here,tau/splinter-large-qass,0.0005129496566951,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
4217,flboehm/reddit-bert-text_5,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.0508974567055702,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
4218,lannelin/bert-imdb-1hidden,"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,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
4219,mwesner/reformer-clm,"Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0850835815072059,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
4220,hfl/chinese-electra-180g-small-ex-discriminator,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0514261772235234,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
4221,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4222,negfir/distilbert-base-uncased-finetuned-cola,This model is cased: it makes a difference between english and English,google/fnet-large,0.0711751878261566,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
4223,beston91/gpt2-xl_ft_logits_10k,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0453858412802219,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
4224,groar/gpt-neo-1.3B-finetuned-escape2,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0449451431632041,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
4225,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,"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,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
4226,abdusahmbzuai/aradia-ctc-distilhubert-ft,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.051257062703371,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
4227,google/t5-efficient-large-nl12,"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,0.0544740892946719,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
4228,IDEA-CCNL/Erlangshen-Longformer-330M,See the MuRIL checkpoint for further details,Sindhu/muril-large-squad2,0.0381638724356888,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
4229,mwesner/bart-mlm,"Our DistilRuBERT-small was highly inspired by [3], [4]",DeepPavlov/distilrubert-tiny-cased-conversational,0.0445927530527114,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
4230,google/t5-efficient-small-el16-dl4,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
4231,google/t5-efficient-base-nl40,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0696371346712112,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
4232,razent/SciFive-base-PMC,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0147423427551984,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
4233,uer/t5-v1_1-base-chinese-cluecorpussmall,"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,0.0148237058892845,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
4234,lgrobol/electra-minuscule-generator,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0301602780818938,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
4235,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
4236,google/bert_uncased_L-10_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-10_H-768_A-12,1.0,-1.0,NLP,3da331c9-9764-4180-8cf3-f1391f6749bc___bert_uncased_L-10_H-768_A-12,HF
4237,hfl/rbtl3,"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,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
4238,allenai/wmt16-en-de-12-1,It was introduced in this paper and first released in this repository,Rostlab/prot_bert,0.0697445645928382,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
4239,google/t5-efficient-tiny-nl24,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0741433165967464,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
4240,diper1998/distilgpt2-finetuned-AT,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0338583663105964,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
4241,google/t5-efficient-tiny-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,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
4242,thu-coai/LongLM-large,T5-Efficient-TINY-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-el8,0.055711020870755,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
4243,SetFit/deberta-v3-large__sst2__train-8-8,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0922933146357536,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
4244,sonoisa/byt5-small-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,0.0683771073818206,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
4245,DemangeJeremy/4-sentiments-with-flaubert,"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,0.0699048992246389,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4246,tau/tavbert-he,T5-Efficient-LARGE-NH24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh24,0.0092043289914726,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4247,jhu-clsp/bibert-ende,"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,0.011155213539799,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
4248,microsoft/layoutlm-base-uncased,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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
4249,DemangeJeremy/4-sentiments-with-flaubert,Global attention is user-configured based on the task to allow the model to learn task-specific representations,mrm8488/longformer-base-4096-spanish,0.0677130632102489,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4250,IDEA-CCNL/Erlangshen-Longformer-330M,"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,1.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
4251,google/t5-efficient-small-kv16,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0054174140095711,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
4252,KoichiYasuoka/roberta-base-ukrainian,"This model is on:If you are looking for a model trained only on MAA, please see our other model",wukevin/tcr-bert,0.0875622183084487,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
4253,microsoft/layoutlm-large-uncased,"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,0.0588635019958019,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
4254,DemangeJeremy/4-sentiments-with-flaubert,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0139481723308562,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4255,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0350893437862396,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
4256,uer/t5-v1_1-small-chinese-cluecorpussmall,For best results use python as below,pszemraj/led-large-book-summary,0.084156796336174,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
4257,hajime9652/xlnet-japanese,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
4258,kyryl0s/gpt2-uk-xxs,"The model that can directly translate between the 9,900 directions of 100 languages",facebook/m2m100-12B-avg-10-ckpt,0.0717005003243684,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
4259,google/t5-efficient-small-nl24,"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,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
4260,abdusahmbzuai/aradia-ctc-distilhubert-ft,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0606066510081291,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
4261,mwesner/reformer-clm,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.060218371450901,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
4262,DemangeJeremy/4-sentiments-with-flaubert,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,0.0335119222290813,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4263,google/t5-efficient-tiny-nl2,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0727534517645835,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
4264,google/t5-efficient-small-nl24,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0722170732915401,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
4265,kyryl0s/gpt2-uk-xxs,"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,0.0346673214808106,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
4266,google/t5-efficient-small-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,0.0506790876388549,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
4267,google/t5-efficient-base-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,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
4268,kyryl0s/gpt2-uk-xxs,"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,0.0296949634794145,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
4269,nherve/flaubert-oral-asr,"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-asr,1.0,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
4270,uer/t5-v1_1-small-chinese-cluecorpussmall,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
4271,tau/tavbert-he,T5-Efficient-TINY-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff12000,0.0389351230114697,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4272,gayanin/bart-mlm-pubmed-medterm,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0965962931513786,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
4273,mmaguero/gn-bert-tiny-cased,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0225844550877809,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
4274,hf-internal-testing/tiny-random-rembert,"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,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
4275,jcblaise/distilbert-tagalog-base-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0432828197787914,-1.0,NLP,29e88f0d-47b4-4733-8b59-3cbe0f4bb77e___distilbert-tagalog-base-cased,HF
4276,flboehm/reddit-bert-text_5,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.032187134027481,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
4277,jonatasgrosman/wav2vec2-xls-r-1b-english,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.06316173573335,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
4278,google/t5-efficient-xl-nl16,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,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
4279,cointegrated/rubert-tiny-bilingual-nli,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0524059906601905,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
4280,clue/xlnet_chinese_large,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0114256883971392,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
4281,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"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,0.0295378006994724,-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
4282,uer/t5-v1_1-small-chinese-cluecorpussmall,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,1.0,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
4283,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,com/mephisto121/BBBNLP if you want to check them all and check the codes too,Parsa/BBB_prediction_classification_SMILES,0.0335049380858739,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
4284,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,Further details are given in the associated paper,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
4285,stas/mt5-tiny-random,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
4286,jorge-henao/gpt2-small-spanish-disco-poetry-wt,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0968194231390953,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
4287,stas/mt5-tiny-random,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-xquadv1,0.0681793391704559,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
4288,google/t5-efficient-base-nl32,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
4289,tau/tavbert-he,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,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4290,IDEA-CCNL/Erlangshen-Longformer-330M,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0754730626940726,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
4291,google/t5-efficient-base-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,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
4292,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,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,0.0725923279921213,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
4293,piotr-rybak/poleval2021-task4-herbert-large-encoder,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.0599774830043315,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
4294,amazon/bort,128K vocabulary chosen because language has a lot of word forms,jkeruotis/LitBERTa-uncased,0.0643722489476203,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
4295,suzuki/distilbert-base-uncased-finetuned-squad,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0019106138497591,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
4296,nherve/flaubert-oral-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,0.0047844325502713,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
4297,mgreenbe/bertlet-base-uncased-for-sequence-classification,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0761610940098762,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
4298,SupriyaArun/squeezebert-uncased-finetuned-squad,Vocabulary size is 66.7k,ctoraman/RoBERTa-TR-medium-wp-66k,0.0931245312094688,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
4299,THUMT/mGPT,You can use the raw model for text generation or using prompts for adapting it to a downstream task,THUMT/mGPT,1.0,-1.0,NLP,337b82e9-8452-490f-be57-2e0059a8854e___mGPT,HF
4300,google/t5-efficient-large-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,0.0193493440747261,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
4301,malay-huggingface/t5-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,0.0,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
4302,aditeyabaral/sentencetransformer-distilbert-hinglish-big,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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4303,allenai/wmt16-en-de-dist-6-1,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0321799116209148,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
4304,google/t5-efficient-small-kv128,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0372014520689845,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
4305,TJMUCH/transcriptome-iseeek,"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,0.084320344030857,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4306,google/t5-efficient-small-nl20,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0677340198308228,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
4307,google/t5-efficient-base-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,0.0414771093055605,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
4308,google/t5-efficient-tiny-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,0.072288729250431,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
4309,gayanin/bart-mlm-pubmed-medterm,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0565628185868263,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
4310,mwesner/reformer-clm,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0218449272215366,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
4311,google/t5-efficient-small-el32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0461743976920842,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
4312,mmaguero/gn-bert-tiny-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,0.0881317295134067,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
4313,aubmindlab/aragpt2-mega,"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,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
4314,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0194903975352644,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
4315,SupriyaArun/squeezebert-uncased-finetuned-squad,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
4316,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,I have made no attempts to censor,bigjoedata/rockbot,0.0445015989243984,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
4317,mrm8488/longformer-base-4096-spanish,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,47fa5077-4d92-4913-a31a-b5f621200d4b___longformer-base-4096-spanish,HF
4318,clue/xlnet_chinese_large,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,0.0542058479040861,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
4319,phueb/BabyBERTa-2,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0163447171449661,-1.0,NLP,1f139471-d31d-4cf9-a218-57b5de0a42bb___BabyBERTa-2,HF
4320,mimi/Waynehills-NLP-doogie,"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,0.0891494676470756,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
4321,beston91/gpt2-xl_ft_logits_10k,This does not make a very readable text,pere/DeUnCaser,0.0702949687838554,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
4322,mwesner/bart-mlm,"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-p1,0.0817718803882598,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
4323,tartuNLP/gpt-4-est-large,This is GPT for Estonian,tartuNLP/gpt-4-est-large,1.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
4324,NovelAI/genji-python-6B,"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,0.0383626880745092,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
4325,google/t5-efficient-mini-nl12,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0409864522516727,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
4326,google/t5-efficient-small-el4,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0444563999772071,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
4327,google/t5-efficient-small-ff6000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.014828784391284,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
4328,google/t5-efficient-tiny-nl6,T5-Efficient-TINY-NL6 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nl6,1.0,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
4329,google/bert_uncased_L-8_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-8_H-768_A-12,1.0,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
4330,microsoft/deberta-v2-xxlarge-mnli,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0130782960914075,-1.0,NLP,cbc32c67-dff0-4360-9d19-43d9ae6a8457___deberta-v2-xxlarge-mnli,HF
4331,google/t5-efficient-tiny-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-p1,0.0156555017456412,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
4332,hfl/rbt3,"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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
4333,google/t5-efficient-small-el4,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.053440911695361,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
4334,malay-huggingface/t5-super-tiny-bahasa-cased,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0605558399111031,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
4335,amazon/bort,We use the same tokenization and vocabulary as the mT5 model,THUMT/mGPT,0.067785121500492,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
4336,allenai/wmt16-en-de-dist-6-1,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0136142522096634,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
4337,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,Note: flaubert-small-cased is partially trained so performance is not guaranteed,flaubert/flaubert_small_cased,0.064358040690422,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
4338,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.08441723883152,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
4339,google/t5-efficient-large-nh4,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0062011387199163,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
4340,DTAI-KULeuven/robbertje-1-gb-bort,"There are multiple models with different sizes and different training settings, which you can choose for your use-case",DTAI-KULeuven/robbertje-1-gb-bort,1.0,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
4341,ncduy/phobert-large-finetuned-vietnamese_students_feedback,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0764960944652557,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
4342,aditeyabaral/sentencetransformer-distilbert-hinglish-big,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0851347297430038,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4343,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
4344,google/t5-efficient-large-nh4,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0267161522060631,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
4345,tobiaslee/bert-2l-768h-uncased,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0853112637996673,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
4346,ian0delond/model-test,The hypothesis_template parameter is important and should be in Spanish,Recognai/zeroshot_selectra_small,0.0567417629063129,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
4347,microsoft/layoutlm-base-uncased,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0737722367048263,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
4348,cmarkea/distilcamembert-base-qa,This modelization is close to etalab-ia/camembert-base-squadFR-fquad-piaf based on CamemBERT model,cmarkea/distilcamembert-base-qa,1.0,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
4349,skt/ko-gpt-trinity-1.2B-v0.5,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0612551849335431,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
4350,jhu-clsp/bibert-ende,"At small scale, ELECTRA achieves strong results even when trained on a single GPU",skplanet/dialog-koelectra-small-generator,0.0009134051700432,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
4351,google/t5-efficient-small-ff1000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0120557565242051,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
4352,google/t5-efficient-large-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,0.0430209161713719,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
4353,IDEA-CCNL/Erlangshen-Longformer-330M,This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset,groar/gpt-neo-1.3B-finetuned-escape2,0.0671434570103882,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
4354,tartuNLP/gpt-4-est-base,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0817253254354,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
4355,google/t5-efficient-large-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,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
4356,google/t5-efficient-small-kv128,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0813019797205924,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
4357,google/t5-efficient-xl-nl4,Boris was trained on around 78B tokens of French text from the C4 dataset,Cedille/fr-boris,0.0896127521991729,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
4358,google/t5-efficient-base-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,0.0696462858468294,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
4359,mwesner/reformer-clm,T5-Efficient-SMALL-KV16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv16,0.0531975999474525,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
4360,google/t5-efficient-tiny-nl24,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0257817227393388,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
4361,krevas/finance-koelectra-base-discriminator,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0235759294591843,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
4362,google/t5-efficient-small-dm1000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0445016650483011,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
4363,uer/pegasus-base-chinese-cluecorpussmall,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0133863594383001,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
4364,stas/pegasus-cnn_dailymail-tiny-random,It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper,facebook/mbart-large-50-many-to-one-mmt,0.0984069555997848,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
4365,google/t5-efficient-large-el4,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.051906776614487,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
4366,ffsouza/tiny-mbart-length-128-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,0.0,-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
4367,google/t5-efficient-large-dm512,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0906587243080139,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
4368,bigjoedata/rockbot,GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX,gilparmentier/pokemon_gptj_model,0.0,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
4369,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
4370,google/t5-efficient-xl-nl4,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0064420141279697,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
4371,IlyaGusev/rugpt3medium_sum_gazeta,Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer,flaubert/flaubert_small_cased,0.0539127439260481,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
4372,google/t5-efficient-base-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,0.0906043648719787,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
4373,fnlp/bart-base-chinese,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0819843038916587,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
4374,Palak/microsoft_deberta-base_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,0.0412136688828468,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
4375,cammy/led-large-16384-arxiv-100-MDS,"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-128,0.0874461978673935,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
4376,piotr-rybak/poleval2021-task4-herbert-large-encoder,"To acquire the pre-trained model only, see the UWB-AIR/MQDD-pretrained",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
4377,google/t5-efficient-small-dm256,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0340465158224105,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
4378,stas/pegasus-cnn_dailymail-tiny-random,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,1.0,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
4379,prajjwal1/bert-medium,"This is one of the smaller pre-trained BERT variants, together with bert-tiny, bert-mini and bert-small",prajjwal1/bert-medium,1.0,-1.0,NLP,34c0c0a8-7e6a-420b-9ebd-bb0cc78a7a25___bert-medium,HF
4380,microsoft/layoutlm-base-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,0.085276074707508,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
4381,google/t5-efficient-large-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,0.0432758051902055,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
4382,tartuNLP/gpt-4-est-large,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,0.0830755457282066,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
4383,google/t5-efficient-large-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,0.0981138460338115,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
4384,google/t5-efficient-small-nl24,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0084423273801803,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
4385,google/t5-efficient-large-nl16,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
4386,suzuki/distilbert-base-uncased-finetuned-squad,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0272981747984886,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
4387,hfl/rbtl3,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0387086570262908,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
4388,google/t5-efficient-small-ff6000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0383949894458055,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
4389,deep-learning-analytics/triviaqa-t5-base,The model is tuned to search for the answer in its memory to return it,deep-learning-analytics/triviaqa-t5-base,1.0,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
4390,skt/ko-gpt-trinity-1.2B-v0.5,SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization,andrejmiscic/simcls-scorer-billsum,0.0778775587677955,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
4391,piotr-rybak/poleval2021-task4-herbert-large-encoder,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-v0.5,0.030767984688282,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
4392,google/t5-efficient-tiny-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,0.0597013514488935,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
4393,ncduy/phobert-large-finetuned-vietnamese_students_feedback,"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,0.0940647050738334,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
4394,google/t5-efficient-small-nl24,Its original repository can be found here,tau/splinter-large-qass,0.0565811004489659,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
4395,google/t5-efficient-tiny-nl6,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0031132381409407,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
4396,CuongLD/wav2vec2-large-xlsr-vietnamese,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0485750716179608,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
4397,fnlp/bart-base-chinese,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0859981812536715,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
4398,macedonizer/al-roberta-base,This model is case-sensitive: it makes a difference between tirana and Tirana,macedonizer/al-roberta-base,1.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
4399,microsoft/layoutlm-large-uncased,Our bibert-ende is a bilingual English-German Language Model,jhu-clsp/bibert-ende,0.0868481695652008,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
4400,google/t5-efficient-xl-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,0.0401595178991555,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
4401,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0895114690065383,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
4402,SetFit/deberta-v3-large__sst2__train-8-8,"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,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
4403,stas/t5-very-small-random,The pre-training data was majorly taken from OSCAR,neuralspace-reverie/indic-transformers-bn-xlmroberta,0.0875458493828773,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
4404,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This repository provides a 1.3B-parameter finetuned Japanese GPT2 model,jweb/japanese-soseki-gpt2-1b,0.0573621280491352,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
4405,minimaxir/magic-the-gathering,"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,0.0304036866873503,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
4406,google/t5-efficient-base-el6,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0642876029014587,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
4407,google/t5-efficient-small-kv16,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0551965655758976,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
4408,allegro/plt5-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,0.0430459106961886,-1.0,NLP,ae66b1a7-cd5c-40c8-a268-585cc928cd04___plt5-large,HF
4409,ffsouza/tiny-mbart-length-128-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,0.0372561290860176,-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
4410,Norod78/hebrew_poetry-gpt_neo-tiny,See SimCLS paper for a description of baselines,andrejmiscic/simcls-scorer-billsum,0.0062677487730979,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
4411,razent/SciFive-base-PMC,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0622477978467941,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
4412,DemangeJeremy/4-sentiments-with-flaubert,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0589365456253289,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4413,mwesner/reformer-clm,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.0486233942210674,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
4414,cammy/led-base-16384-100-MDS,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0994254648685455,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
4415,google/t5-efficient-large-nl10,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0131223574280738,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
4416,google/bert_uncased_L-6_H-256_A-4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0245217963432272,-1.0,NLP,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
4417,TJMUCH/transcriptome-iseeek,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0451081208884716,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4418,google/t5-efficient-small-dm256,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0699304528534412,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
4419,microsoft/layoutlm-base-cased,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
4420,mrp/simcse-model-wangchanberta,"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,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
4421,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-DM2000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm2000,0.0482363722597559,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
4422,aditeyabaral/sentencetransformer-bert-hinglish-small,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
4423,mimicheng/codeparrot-ds,"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,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
4424,MoritzLaurer/MiniLM-L6-mnli,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0471064994732538,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
4425,allenai/wmt19-de-en-6-6-big,"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,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
4426,mimi/Waynehills-NLP-doogie,Colab: linkEvaluation script: evaluate,IlyaGusev/rugpt3medium_sum_gazeta,0.0988496169447898,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
4427,google/t5-efficient-large-nh4,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0797322690486907,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
4428,google/t5-efficient-large-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,0.0656657218933105,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
4429,stas/mt5-tiny-random,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
4430,google/t5-efficient-small-nl40,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0215769289061427,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
4431,DemangeJeremy/4-sentiments-with-flaubert,"Google's mT5mT5 is pretrained on the mC4 corpus, covering 101 languages:",mrm8488/mT5-small-finetuned-tydiqa-for-xqa,0.0345809524878858,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4432,google/t5-efficient-large-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,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
4433,jkeruotis/LitBERTa-uncased,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0856288224458694,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
4434,superspray/electra_large_discriminator_squad2_custom_dataset,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0292152985930442,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
4435,sarnikowski/convbert-medium-small-da-cased,ConvBERT model pretrained on a custom Danish corpus (~17.5gb),sarnikowski/convbert-medium-small-da-cased,1.0,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
4436,jnz/electra-ka,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,0.038513645529747,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
4437,UWB-AIR/MQDD-duplicates,"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,0.001178929892679,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
4438,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.065442144870758,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
4439,skt/ko-gpt-trinity-1.2B-v0.5,entity-level masking and phrase-level masking,nghuyong/ernie-1.0,0.0627512093633412,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
4440,aditeyabaral/sentencetransformer-bert-hinglish-small,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
4441,1one5ome/gpt2-chinese-gulong,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0152001082897186,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
4442,1one5ome/gpt2-chinese-gulong,It is supposed to be used for the purpose of debugging,hakurei/gpt-j-random-tinier,0.0568996369838714,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
4443,rdenadai/BR_BERTo,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0237165664633114,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
4444,google/t5-efficient-small-el16-dl4,T5-Efficient-SMALL-EL16-DL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el16-dl4,1.0,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
4445,microsoft/layoutlm-base-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,0.0727218091487884,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
4446,SetFit/deberta-v3-large__sst2__train-8-8,"Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan BaskaranWork done via MLDMM Lab",mldmm/GlassBERTa,0.0638323947787284,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
4447,enelpol/czywiesz-context,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,1.0,-1.0,NLP,07836e98-ffe7-459d-ba1b-141f26fdbc92___czywiesz-context,HF
4448,1one5ome/gpt2-chinese-gulong,"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,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
4449,negfir/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,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
4450,ml6team/mt5-small-german-finetune-mlsum,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0710624838247894,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
4451,google/t5-efficient-small-nl40,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0638620518147945,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
4452,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
4453,google/t5-efficient-small-nl36,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0446310844272374,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
4454,DTAI-KULeuven/robbertje-1-gb-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,0.02690225529174,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
4455,google/t5-efficient-base-nl40,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,0.0935863777995109,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
4456,KoichiYasuoka/roberta-base-ukrainian,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0953006893396377,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
4457,google/t5-efficient-xl-nl4,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.015229009091854,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
4458,google/t5-efficient-large-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,0.067081544548273,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
4459,google/t5-efficient-tiny-ff9000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.008809223305434,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
4460,stas/mt5-tiny-random,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0621477402746677,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
4461,tucan9389/kcbert-base-finetuned-squad,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0980373620986938,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
4462,stas/mt5-tiny-random,"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,0.049233429133892,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
4463,google/t5-efficient-large-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-large-dm512,1.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
4464,google/t5-efficient-base-ff6000,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
4465,cosmoquester/bart-ko-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,0.0,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
4466,cammy/led-base-16384-100-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,0.0304387956857681,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
4467,google/t5-efficient-tiny-nh32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0606950614601373,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
4468,google/t5-efficient-base-kv128,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0210312716662883,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
4469,hakurei/gpt-j-random-tinier,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0417580944485962,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
4470,thu-coai/LongLM-large,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
4471,Palak/microsoft_deberta-base_squad,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.066584289073944,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
4472,google/t5-efficient-xl-nl2,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0498717743903397,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
4473,rinna/japanese-gpt-1b,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
4474,superspray/electra_large_discriminator_squad2_custom_dataset,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0779577195644378,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
4475,google/t5-efficient-base-kv256,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
4476,allenai/wmt19-de-en-6-6-base,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0705930143594741,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
4477,DTAI-KULeuven/robbertje-1-gb-bort,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.044285824522376,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
4478,GKLMIP/electra-myanmar-small-uncased,The training data for this dataset is stored as a Kaggle dataset,NeuML/bert-small-cord19,0.0681623816490173,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
4479,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0090442039072513,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
4480,mimi/Waynehills-NLP-doogie,"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,0.0448500588536262,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
4481,jonatasgrosman/wav2vec2-xls-r-1b-english,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0508413383116324,-1.0,NLP,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
4482,google/t5-efficient-xl-nl6,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
4483,google/t5-efficient-xl-nl12,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0638149715960025,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
4484,antoiloui/belgpt2,"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,0.0454993341118097,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
4485,crabz/distil-slovakbert-ner,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0914261713624,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
4486,minimaxir/magic-the-gathering,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0692818351089954,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
4487,google/t5-efficient-base-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,0.0511963171884417,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
4488,indobenchmark/indobert-base-p1,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0094221172233422,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
4489,ToToKr/kobigbird-bert-base-finetuned-klue,This model is a fine-tuned version of monologg/kobigbird-bert-base on the None dataset,ToToKr/kobigbird-bert-base-finetuned-klue,1.0,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
4490,google/t5-efficient-base-kv32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0380120417103171,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
4491,TJMUCH/transcriptome-iseeek,Global attention is user-configured based on the task to allow the model to learn task-specific representations,mrm8488/longformer-base-4096-spanish,0.0510078966617584,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4492,google/t5-efficient-base-nh32,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.09273412078619,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
4493,ToToKr/kobigbird-bert-base-finetuned-klue,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0967615395784378,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
4494,brad1141/Longformer-finetuned-norm,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0315379127860069,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
4495,google/t5-efficient-base-el16,"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,0.0722257271409034,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
4496,google/t5-efficient-base-ff6000,Its original repository can be found here,tau/splinter-large-qass,0.0442843977361917,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
4497,google/t5-efficient-small-el16-dl4,"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-v1,0.0511590614914893,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
4498,google/t5-efficient-large-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,0.0088615948334336,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
4499,superspray/electra_large_discriminator_squad2_custom_dataset,Source language: English,NYTK/translation-marianmt-en-hu,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
4500,google/t5-efficient-base-ff1000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0177368577569723,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
4501,google/t5-efficient-large-nl20,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0680426415055989,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
4502,hfl/rbt3,"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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
4503,uer/t5-v1_1-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,0.0595459621399641,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
4504,google/t5-efficient-large-nh2,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0685275457799434,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
4505,google/t5-efficient-large-dm2000,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0496184760704636,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
4506,google/t5-efficient-base-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,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
4507,google/t5-efficient-large-nh24,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0937228463590144,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
4508,groar/gpt-neo-1.3B-finetuned-escape2,"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,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
4509,cammy/led-large-16384-arxiv-100-MDS,"We modify the original position code of longformer to rotational position coding, and use 180G of data to pretraining",IDEA-CCNL/Erlangshen-Longformer-330M,0.0397688224911689,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
4510,google/t5-efficient-large-nl20,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0547174848616122,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
4511,google/t5-efficient-large-nh8-nl32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.021458787843585,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
4512,google/t5-efficient-mini,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0947975367307662,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
4513,anirudh21/xlnet-base-cased-finetuned-rte,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
4514,allenai/wmt16-en-de-12-1,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0469701271504163,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
4515,clue/roberta_chinese_3L312_clue_tiny,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0620316118001937,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
4516,KoichiYasuoka/roberta-base-ukrainian,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-v0.5,0.0658135116100311,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
4517,ErykWdowiak/GPTalian,"Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer",gilparmentier/pokemon_gptj_model,0.0395981192588806,-1.0,NLP,080ca220-296f-4b91-a8e1-cac78014228b___GPTalian,HF
4518,TJMUCH/transcriptome-iseeek,Pretrained RemBERT model on 110 languages using a masked language modeling (MLM) objective,google/rembert,0.089492067694664,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4519,google/t5-efficient-xl-nl12,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
4520,skt/ko-gpt-trinity-1.2B-v0.5,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,0.0457024313509464,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
4521,Iskaj/hf-challenge-test,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0900532677769661,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
4522,google/t5-efficient-xl-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,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
4523,google/t5-efficient-small-nl40,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
4524,google/t5-efficient-large-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,0.0658865310251712,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
4525,diper1998/distilgpt2-finetuned-AT,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0223972164094448,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
4526,jpcorb20/pegasus-large-reddit_tifu-samsum-512,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0657277175535757,-1.0,NLP,9061fbba-43a1-4153-a2a1-271dab4fe6fe___pegasus-large-reddit_tifu-samsum-512,HF
4527,google/t5-efficient-large-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-p1,0.0,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
4528,google/t5-efficient-base-dm256,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0405446235090493,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
4529,fnlp/cpt-base,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0061070313677191,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
4530,mrp/simcse-model-wangchanberta,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0345843350514769,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
4531,VietAI/gpt-neo-1.3B-vietnamese-news,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0617028884589671,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
4532,google/t5-efficient-tiny-ff9000,T5-Efficient-TINY-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff9000,1.0,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
4533,TsinghuaAI/CPM-Generate,"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,0.0370987430214881,-1.0,NLP,62f7d05a-3845-4b91-b7d3-744fa03b770d___CPM-Generate,HF
4534,google/t5-efficient-large-dm768,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0551404086872935,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
4535,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
4536,TJMUCH/transcriptome-iseeek,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4537,google/t5-efficient-small-el4,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.088135041296482,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
4538,obss/mt5-base-3task-highlight-tquad2,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,0.0846167504787444,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
4539,minimaxir/magic-the-gathering,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
4540,malay-huggingface/t5-small-bahasa-cased,I have made no attempts to censor,bigjoedata/rockbot,0.0407355306670069,-1.0,NLP,9cd8b756-b339-419c-9260-54750e4aada5___t5-small-bahasa-cased,HF
4541,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
4542,damlab/GO-language,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,1.0,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
4543,razent/SciFive-base-PMC,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,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
4544,google/t5-efficient-tiny-ff3000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0203445525839924,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
4545,GKLMIP/electra-myanmar-small-uncased,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0650646500289439,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
4546,stas/mt5-tiny-random,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
4547,brad1141/Longformer-finetuned-norm,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0800125300884246,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
4548,google/t5-efficient-xl-nl8,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0107791647315025,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
4549,tau/tavbert-he,T5-Efficient-BASE-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nl36,0.0076809278689324,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4550,Milos/slovak-gpt-j-405M,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
4551,Palak/microsoft_deberta-base_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-12,0.0483455620706081,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
4552,google/t5-efficient-base-kv256,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
4553,amazon/bort,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,0.0693999100476502,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
4554,Gilles/FongBERT,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0317422673106193,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
4555,google/t5-efficient-tiny-nl32,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0809818767011165,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
4556,google/reformer-crime-and-punishment,I have made no attempts to censor,bigjoedata/rockbot,0.0378621630370616,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
4557,flboehm/reddit-bert-text_5,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0717152804136276,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
4558,dbmdz/bert-medium-historic-multilingual-cased,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
4559,google/t5-efficient-large-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,0.0036286441609263,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
4560,google/t5-efficient-base-dm256,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,0.0900257602334022,-1.0,NLP,7066d70c-0210-4333-a4b3-607c927b5c52___t5-efficient-base-dm256,HF
4561,minimaxir/magic-the-gathering,"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,0.0455613294616342,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
4562,cross-encoder/stsb-TinyBERT-L-4,The model will predict a score between 0 and 1 how for the semantic similarity of two sentences,cross-encoder/stsb-TinyBERT-L-4,1.0,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
4563,TJMUCH/transcriptome-iseeek,Pretrained model on Bulgarian language using a masked language modeling (MLM) objective,rmihaylov/bert-base-pos-theseus-bg,0.0707028210163116,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4564,Norod78/hebrew_poetry-gpt_neo-tiny,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,1.0,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
4565,google/t5-efficient-small-el12,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0620176652446388,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
4566,DemangeJeremy/4-sentiments-with-flaubert,This model is a fine-tuned version of google/fnet-base on the GLUE WNLI dataset,gchhablani/fnet-base-finetuned-wnli,0.0273653343319892,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4567,allenai/wmt16-en-de-12-1,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0321799116209148,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
4568,bigjoedata/rockbot,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.007430777264138,-1.0,NLP,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
4569,google/t5-efficient-xl-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,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
4570,google/t5-efficient-small-el12,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
4571,google/t5-efficient-large-nh2,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
4572,google/t5-efficient-base-nh24,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
4573,google/t5-efficient-small-dm2000,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0447222851216792,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
4574,google/t5-efficient-tiny-nh16,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0793660581111907,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
4575,tau/tavbert-he,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,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4576,gilparmentier/pokemon_gptj_model,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
4577,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
4578,deep-learning-analytics/triviaqa-t5-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,0.0327250180499894,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
4579,negfir/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,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
4580,rinna/japanese-gpt-1b,This repository provides a 1.3B-parameter Japanese GPT model,rinna/japanese-gpt-1b,1.0,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
4581,google/t5-efficient-large-nh2,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0016674483194947,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
4582,google/t5-efficient-large-nh2,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0722524747252464,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
4583,ian0delond/model-test,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0839169472455978,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
4584,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0497150421142578,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
4585,indobenchmark/indobert-base-p1,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0169182680547237,-1.0,NLP,149f034c-887e-4839-a086-2d098a64568c___indobert-base-p1,HF
4586,microsoft/layoutlm-base-uncased,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.083603411912918,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
4587,google/t5-efficient-small-ff1000,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
4588,Recognai/selectra_medium,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0397964518051594,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
4589,google/t5-efficient-small-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-small-nl36,1.0,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
4590,jweb/japanese-soseki-gpt2-1b,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
4591,google/t5-efficient-small-el64,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0056524388492107,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
4592,NYTK/translation-marianmt-en-hu,"For further models, scripts and details, see our repository or our demo site",NYTK/translation-marianmt-en-hu,1.0,-1.0,NLP,04ab8b45-5473-4cfe-a2de-c945b14ed956___translation-marianmt-en-hu,HF
4593,TJMUCH/transcriptome-iseeek,The model might be used through the astonishing 🤗 Transformers librairie,asi/gpt-fr-cased-base,0.0539624392986297,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4594,google/t5-efficient-large-kv32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0507345516234635,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
4595,allenai/wmt16-en-de-12-1,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,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
4596,google/t5-efficient-small-kv128,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
4597,IDEA-CCNL/Erlangshen-Longformer-330M,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0689422525465488,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
4598,google/t5-efficient-tiny-nh16,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0168753750622272,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
4599,amazon/bort,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0691287405788897,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
4600,google/t5-efficient-large-el8,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0535967107862233,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
4601,google/t5-efficient-xl-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,0.0542236361652612,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
4602,mwesner/bart-mlm,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0982808396220207,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
4603,funnel-transformer/large-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,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
4604,jean-paul/kinyaRoberta-small,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,1.0,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
4605,sarnikowski/convbert-medium-small-da-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,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
4606,TJMUCH/transcriptome-iseeek,"Thus, you need to use the sw_KE for language code when evaluating the model",Davlan/mbart50-large-eng-yor-mt,0.0734407752752304,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4607,tobiaslee/bert-2l-768h-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,0.0735755451023578,-1.0,NLP,424c8ebd-9a33-4437-8d7b-b67e8f9f38f2___bert-2l-768h-uncased,HF
4608,lgrobol/electra-minuscule-discriminator,"Namely, we use The key features are:",DeepPavlov/distilrubert-tiny-cased-conversational-v1,0.0233287271112203,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
4609,google/t5-efficient-mini-nl12,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0059726852923631,-1.0,NLP,19134f7d-3b78-4fcf-83c6-a38259135b76___t5-efficient-mini-nl12,HF
4610,hajime9652/xlnet-japanese,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0938772857189178,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
4611,lgrobol/electra-minuscule-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,0.0,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
4612,SajjadAyoubi/distil-bigbird-fa-zwnj,Model is warm started from distilbert-fa’s checkpoint,SajjadAyoubi/distil-bigbird-fa-zwnj,1.0,-1.0,NLP,93bf4f14-6c7a-41bc-854c-061751656605___distil-bigbird-fa-zwnj,HF
4613,stas/t5-very-small-random,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0350138619542121,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
4614,SupriyaArun/squeezebert-uncased-finetuned-squad,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0576843805611133,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
4615,DemangeJeremy/4-sentiments-with-flaubert,You can use the raw model for text generation or using prompts for adapting it to a downstream task,THUMT/mGPT,0.0645672082901,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4616,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0028754146769642,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
4617,facebook/blenderbot-90M,We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy,facebook/blenderbot-90M,1.0,-1.0,NLP,fc6a2248-a765-4b7a-be43-51ae1773b216___blenderbot-90M,HF
4618,sentence-transformers/paraphrase-MiniLM-L3-v2,Amazon's BORT,amazon/bort,0.0831830650568008,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
4619,google/t5-efficient-xl-nl28,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.001432055607438,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
4620,lgrobol/electra-minuscule-generator,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0242116972804069,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
4621,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
4622,google/t5-efficient-tiny-el8,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0426305979490279,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
4623,mikaelsouza/msft-regular-model,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0056532826274633,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
4624,google/t5-efficient-xl-nl2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0321815675124525,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
4625,flboehm/reddit-bert-text_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,0.0722264200448989,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
4626,ian0delond/model-test,"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,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
4627,cammy/led-large-16384-arxiv-100-MDS,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
4628,everdoubling/byt5-Korean-small,This model require Mecab and senetencepiece with XLNetTokenizer,hajime9652/xlnet-japanese,0.0183561846613883,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
4629,fnlp/cpt-base,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,0.0765664242208003,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
4630,google/t5-efficient-base-el16,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.066248670220375,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
4631,tau/tavbert-he,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,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4632,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4633,google/bert_uncased_L-8_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,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
4634,google/t5-efficient-small-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,0.0872928313910961,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
4635,kyryl0s/gpt2-uk-xxs,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0411359835416078,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
4636,DemangeJeremy/4-sentiments-with-flaubert,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0077003091573715,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4637,KoichiYasuoka/roberta-small-belarusian,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0212958706542849,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
4638,google/t5-efficient-base-nl36,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0013728151097894,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
4639,microsoft/layoutlm-base-cased,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
4640,tuner007/pegasus_qa,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0743595287203788,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
4641,google/t5-efficient-small-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-small-dm768,1.0,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
4642,VietAI/gpt-neo-1.3B-vietnamese-news,"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,0.0472613051533698,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
4643,eli4s/Bert-L12-h240-A12,"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-A12,1.0,-1.0,NLP,549ece99-51d8-4f1e-87b0-863da1346803___Bert-L12-h240-A12,HF
4644,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
4645,izumi-lab/bert-small-japanese-fin,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0382242525617281,-1.0,NLP,6cfe60d3-f531-4182-b419-30521b2d0187___bert-small-japanese-fin,HF
4646,lgrobol/electra-minuscule-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,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
4647,aditeyabaral/sentencetransformer-distilbert-hinglish-big,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0786758139729499,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4648,google/t5-efficient-small-dm128,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.030943153426051,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
4649,mrm8488/bert-small-finetuned-squadv2,The final summary is the highest-scoring candidate,andrejmiscic/simcls-scorer-billsum,0.047700498253107,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
4650,google/t5-efficient-large-el2,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0212417049333452,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
4651,Palak/microsoft_deberta-base_squad,"Below is list of tasks we trained on,Preparing steps can reproduce at https://github",malay-huggingface/t5-tiny-bahasa-cased,0.0920741409063339,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
4652,hfl/rbtl3,Colab: linkEvaluation script: evaluate,IlyaGusev/rugpt3medium_sum_gazeta,0.0834552943706512,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
4653,anas-awadalla/bert-tiny-finetuned-squad,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0800700262188911,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
4654,abdusahmbzuai/ft-tatoeba-ar-en,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.09014493227005,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
4655,google/t5-efficient-base-el8,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0373079981654882,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
4656,KoichiYasuoka/roberta-base-ukrainian,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0494919791817665,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
4657,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Source language: English,NYTK/translation-marianmt-en-hu,0.0462830513715744,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
4658,google/t5-efficient-large-dm256,This does not make a very readable text,pere/DeUnCaser,0.0526298200711607,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
4659,google/t5-efficient-tiny-ff6000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
4660,ctoraman/RoBERTa-TR-medium-char,"Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size)",ctoraman/RoBERTa-TR-medium-char,1.0,-1.0,NLP,3fd4dbea-f50c-47a0-881f-f47a755a2a4b___RoBERTa-TR-medium-char,HF
4661,IDEA-CCNL/Erlangshen-Longformer-110M,RoBerta model was first introduced in this paper,jean-paul/kinyaRoberta-small,0.0359161216765642,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
4662,TJMUCH/transcriptome-iseeek,"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-v1,0.0695620775222778,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4663,mrm8488/mobilebert-uncased-finetuned-squadv2,"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,0.066143530110518,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
4664,google/t5-efficient-small-ff6000,This does not make a very readable text,pere/DeUnCaser,0.0838162377476691,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
4665,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-SMALL-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm1000,0.0663586991528669,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
4666,groar/gpt-neo-1.3B-finetuned-escape2,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0991103425621986,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
4667,mikaelsouza/msft-regular-model,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.099834993481636,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
4668,gilparmentier/pokemon_gptj_model,"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,0.0160332606174051,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
4669,google/t5-efficient-xl-nl28,"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,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
4670,hfl/rbt3,T5-Efficient-SMALL-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff9000,0.0532641177996993,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
4671,tucan9389/kcbert-base-finetuned-squad,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0677335411310195,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
4672,IDEA-CCNL/Erlangshen-Longformer-110M,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,0.0719722378998994,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
4673,tau/tavbert-he,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0586507357656955,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4674,google/t5-efficient-xl-nl12,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0347633454948663,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
4675,tucan9389/kcbert-base-finetuned-squad,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0401542671024799,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
4676,skt/ko-gpt-trinity-1.2B-v0.5,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0257324939593672,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
4677,google/t5-efficient-tiny-ff12000,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0689295977354049,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
4678,facebook/mbart-large-50-many-to-one-mmt,See the model hub to look for more fine-tuned versions,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
4679,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl32,0.0819804891943931,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
4680,google/t5-efficient-large-nh8,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0609059017151593,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
4681,google/t5-efficient-tiny-ff3000,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0428370404988527,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
4682,TJMUCH/transcriptome-iseeek,We do hope our model might be used for both academic and industrial applications,asi/gpt-fr-cased-base,0.0612243488430976,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4683,google/t5-efficient-tiny-nh16,Its original repository can be found here,tau/splinter-large-qass,0.0544861368834971,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
4684,google/t5-efficient-mini,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,0.0654710736125707,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
4685,google/t5-efficient-tiny-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,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
4686,google/t5-efficient-base-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,0.0347846169024705,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
4687,microsoft/layoutlm-base-uncased,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0351738780736923,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
4688,google/t5-efficient-base-nl32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0600085128098726,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
4689,sarnikowski/convbert-medium-small-da-cased,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,7e5e762b-d3a1-4411-ac93-2c092a8a598e___convbert-medium-small-da-cased,HF
4690,deep-learning-analytics/triviaqa-t5-base,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0495485085994004,-1.0,NLP,c11d993d-6f31-4363-92f8-8ed137b0d7fc___triviaqa-t5-base,HF
4691,beston91/gpt2-xl_ft_logits_10k,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0841854363679885,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
4692,allenai/wmt19-de-en-6-6-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,0.0125622409395873,-1.0,NLP,8aeb89b0-c9a4-4865-9df5-91c697482c60___wmt19-de-en-6-6-base,HF
4693,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0403680043915907,-1.0,NLP,0b522167-6a74-4ebb-b171-c5bba2ab7c40___distil-wav2vec2-xls-r-adult-child-cls-89m,HF
4694,oskrmiguel/mt5-simplification-spanish,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0431833025068044,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
4695,TJMUCH/transcriptome-iseeek,"If you use our model, please consider citing our paper:",GKLMIP/electra-myanmar-small-uncased,0.054247923195362,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4696,mldmm/GlassBERTa,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
4697,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0767568126320838,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
4698,google/t5-efficient-base-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,0.057609898969531,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
4699,negfir/distilbert-base-uncased-finetuned-squad,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
4700,GKLMIP/electra-myanmar-small-uncased,Training metrics were logged via Tensorboard,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0298605123534798,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
4701,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"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,0.0894477069377899,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
4702,stas/pegasus-cnn_dailymail-tiny-random,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0831045322120189,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
4703,tau/tavbert-he,T5-Efficient-SMALL-NL22 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl22,0.0277839358896016,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4704,ductuan024/AimeLaw,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,0.082802377641201,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
4705,IDEA-CCNL/Erlangshen-Longformer-110M,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,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
4706,zoha/wav2vec2-base-timit-demo-colab,The hypothesis_template parameter is important and should be in Spanish,Recognai/zeroshot_selectra_small,0.0902704298496246,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
4707,sentence-transformers/paraphrase-MiniLM-L3-v2,"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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
4708,google/t5-efficient-base-ff12000,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0379957277327775,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
4709,rmihaylov/bert-base-pos-theseus-bg,"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,428758d5-6177-42ba-a879-c30586fca3ab___bert-base-pos-theseus-bg,HF
4710,mrm8488/deberta-v3-small-finetuned-mrpc,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_gpt2,0.0809696316719055,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
4711,allenai/wmt16-en-de-12-1,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0697445645928382,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
4712,piotr-rybak/poleval2021-task4-herbert-large-encoder,"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,0.0972330048680305,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
4713,diper1998/distilgpt2-finetuned-AT,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0043793553486466,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
4714,kyryl0s/gpt2-uk-xxs,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0166799668222665,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
4715,xlnet-large-cased,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,4977e25e-6e3f-4b5f-85d5-9ebf637e1a4b___xlnet-large-cased,HF
4716,uer/t5-v1_1-small-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,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
4717,jnz/electra-ka,For best results use python as below,pszemraj/led-large-book-summary,0.0285085700452327,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
4718,google/t5-efficient-base-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,0.0415916377678513,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
4719,google/t5-efficient-large-nh2,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0375151708722114,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
4720,TJMUCH/transcriptome-iseeek,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,0.0265393014997243,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4721,negfir/distilbert-base-uncased-finetuned-cola,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
4722,tartuNLP/gpt-4-est-large,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0455797389149665,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
4723,mmaguero/gn-bert-tiny-cased,T5-Efficient-SMALL-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv32,0.087008349597454,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
4724,uer/t5-v1_1-base-chinese-cluecorpussmall,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0466309264302253,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
4725,google/t5-efficient-mini-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,0.0722561441361903,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
4726,nherve/flaubert-oral-ft,"Then, it was compressed via progressive module replacing",rmihaylov/bert-base-pos-theseus-bg,0.047881968319416,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
4727,ml6team/mt5-small-german-finetune-mlsum,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0081541640684009,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
4728,google/t5-efficient-base-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,0.0496510453522205,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
4729,google/t5-efficient-small-dm2000,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0700898766517638,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
4730,google/t5-efficient-small-kv256,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0587047487497329,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
4731,skt/ko-gpt-trinity-1.2B-v0.5,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0687383636832236,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
4732,beston91/gpt2-xl_ft_logits_10k,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0378092601895332,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
4733,jweb/japanese-soseki-gpt2-1b,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0617119471232095,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
4734,google/t5-efficient-base-nh16,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0287478948011993,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
4735,aubmindlab/aragpt2-mega,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.015880949795246,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
4736,google/t5-efficient-tiny-nl6,Its original repository can be found here,tau/splinter-large-qass,0.0412475038319825,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
4737,google/t5-efficient-large-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,0.0472453758120536,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
4738,tau/tavbert-he,"If you use our model or find it useful in your projects, please cite our work:",jcblaise/electra-tagalog-small-cased-generator,0.063944961875677,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4739,google/reformer-crime-and-punishment,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0307165775448083,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
4740,tau/tavbert-he,T5-Efficient-LARGE-NH8-NL32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8-nl32,0.0244291704148054,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4741,aditeyabaral/sentencetransformer-distilbert-hinglish-big,This repository provides a 1.3B-parameter finetuned Japanese GPT2 model,jweb/japanese-soseki-gpt2-1b,0.0544427335262298,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4742,dbmdz/electra-base-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,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
4743,jweb/japanese-soseki-gpt2-1b,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0444712750613689,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
4744,google/t5-efficient-mini-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,0.0615547280758619,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
4745,google/t5-efficient-small-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,0.0498691163957119,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
4746,google/t5-efficient-large-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,0.0863139778375625,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
4747,danyaljj/opengpt2_pytorch_forward,"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,0.0547634263833363,-1.0,NLP,aa6540d4-349b-4918-aa67-06fa2c09e78c___opengpt2_pytorch_forward,HF
4748,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv32,0.040771123021841,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
4749,google/t5-efficient-small-kv256,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0523584894835948,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
4750,Iskaj/hf-challenge-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,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
4751,google/t5-efficient-mini-nl6,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0177297890186309,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
4752,google/t5-efficient-small-nl36,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
4753,TristanBehrens/js-fakes-4bars,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,1.0,-1.0,NLP,84456894-0906-4840-8de2-f427e2536a64___js-fakes-4bars,HF
4754,kyryl0s/gpt2-uk-xxs,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0543591044843196,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
4755,google/t5-efficient-small-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,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
4756,beston91/gpt2-xl_ft_logits_10k,"We collect different kinds of texts in our pre-training, including encyclopedia, news, novels, and QandA",TsinghuaAI/CPM-Generate,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
4757,TJMUCH/transcriptome-iseeek,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0634827092289924,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4758,google/byt5-large,"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,63dd91ba-d71d-4adb-9626-b4720f858c6d___byt5-large,HF
4759,IDEA-CCNL/Erlangshen-Longformer-110M,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0688454769551754,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
4760,Iskaj/hf-challenge-test,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.035481046885252,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
4761,google/t5-efficient-small-nl40,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0532789584249257,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
4762,google/t5-efficient-small-el32,"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,0.0299680195748805,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
4763,google/t5-efficient-tiny-nl6,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0392873231321573,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
4764,google/t5-efficient-small-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,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
4765,mgreenbe/bertlet-base-uncased-for-sequence-classification,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0415457934141159,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
4766,sberbank-ai/rugpt3medium_based_on_gpt2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0260538049042224,-1.0,NLP,91eb6961-f406-48db-9c68-63f1d2000a59___rugpt3medium_based_on_gpt2,HF
4767,nghuyong/ernie-tiny,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
4768,google/t5-efficient-base-ff9000,This does not make a very readable text,pere/DeUnCaser,0.0615063533186912,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
4769,cammy/led-large-16384-arxiv-100-MDS,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0396519303321838,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
4770,google/t5-efficient-small-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,0.08667703717947,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
4771,google/t5-efficient-base-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-v1,0.0349263548851013,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
4772,TJMUCH/transcriptome-iseeek,The training is supported by TPU Research Cloud program,cosmoquester/bart-ko-small,0.0312747992575168,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4773,sberbank-ai/rugpt3large_based_on_gpt2,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_gpt2,1.0,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
4774,jorge-henao/gpt2-small-spanish-disco-poetry-wt,"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,0.0742211043834686,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
4775,beston91/gpt2-xl_ft_logits_10k,"Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan BaskaranWork done via MLDMM Lab",mldmm/GlassBERTa,0.0903369486331939,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
4776,google/t5-efficient-large-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,0.0008806437253952,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
4777,lgris/distilxlsr_bp_8-12,DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian PortugueseThe base model pretrained on 16kHz sampled speech audio,lgris/distilxlsr_bp_8-12,1.0,-1.0,NLP,e1ebd8cf-a1ac-462e-886d-595c6593b41e___distilxlsr_bp_8-12,HF
4778,mimi/Waynehills-NLP-doogie,co/facebook/blenderbot_small-90M insteadBuilding open-domain chatbots is a challenging area for machine learning research,facebook/blenderbot-90M,0.0693971142172813,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
4779,saibo/random-roberta-tiny,"We introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads)",saibo/random-roberta-tiny,1.0,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
4780,saibo/random-roberta-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,0.078084273263812,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
4781,google/t5-efficient-large-nl16,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0509455408900975,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
4782,TJMUCH/transcriptome-iseeek,Note that the architecture of this model follows the original BERT,arm-on/BERTweet-FA,0.0139640104025602,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4783,google/bert_uncased_L-8_H-768_A-12,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.012903448815147,-1.0,NLP,00695597-3987-4483-ada0-6c0706109508___bert_uncased_L-8_H-768_A-12,HF
4784,DemangeJeremy/4-sentiments-with-flaubert,"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,0.0609687808901071,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4785,google/t5-efficient-base-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,0.0552037209272384,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
4786,mrm8488/bert-small-finetuned-squadv2,"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,0.0900331251323222,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
4787,cointegrated/rubert-tiny-bilingual-nli,Its original repository can be found here,tau/splinter-large-qass,0.0444950833916664,-1.0,NLP,c5f09559-a650-40bd-8ece-8a3ef7f2a5dd___rubert-tiny-bilingual-nli,HF
4788,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0681492313742637,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
4789,ml6team/distilbart-tos-summarizer-tosdr,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0187357969116419,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
4790,jhu-clsp/bibert-ende,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.018976280465722,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
4791,google/t5-efficient-tiny-ff12000,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0435105431824922,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
4792,DemangeJeremy/4-sentiments-with-flaubert,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.026777843479067,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4793,microsoft/layoutlm-base-cased,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0723494589328765,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
4794,mldmm/GlassBERTa,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,1.0,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
4795,microsoft/layoutlm-large-uncased,This model is case-sensitive: it makes a difference between tirana and Tirana,macedonizer/al-roberta-base,0.0514089576900005,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
4796,hfl/chinese-electra-180g-small-ex-generator,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0514261772235234,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
4797,google/t5-efficient-large-nl36,"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,0.0834281556308269,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
4798,superb/hubert-base-superb-er,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0001316840061917,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
4799,khizon/distilbert-unreliable-news-eng-4L,"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,0.0454869598615914,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
4800,google/t5-efficient-small-el32,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
4801,anas-awadalla/bert-tiny-finetuned-squad,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0553685203194618,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
4802,Milos/slovak-gpt-j-405M,"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,0.0600645219286282,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
4803,fnlp/cpt-base,"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,0.039753038669005,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
4804,google/t5-efficient-base-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,0.0384804932400583,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
4805,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
4806,DeepPavlov/distilrubert-tiny-cased-conversational-v1,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0,-1.0,NLP,89a43e3f-9da2-4d73-9dc9-e20ac8ae9ee3___distilrubert-tiny-cased-conversational-v1,HF
4807,google/t5-efficient-base-nl40,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
4808,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0603571534156799,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
4809,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-kv128,0.0596252605319022,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
4810,hyunwoongko/reddit-9B,"We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available",hyunwoongko/reddit-9B,1.0,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
4811,mrm8488/deberta-v3-small-finetuned-mrpc,I have made no attempts to censor,bigjoedata/rockbot,0.0329200476408004,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
4812,abdusahmbzuai/aradia-ctc-distilhubert-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,0.0515269190073013,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
4813,amazon/bort,"For the tweets, the data was collected using twitter API and using language filter",qarib/bert-base-qarib_far_6500k,0.0953305810689926,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
4814,google/t5-efficient-xl-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,0.0686512812972068,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
4815,google/t5-efficient-large-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-large-el4,1.0,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
4816,aditeyabaral/sentencetransformer-distilbert-hinglish-big,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0447854250669479,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4817,google/t5-efficient-tiny-ff3000,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0567063260823487,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
4818,SetFit/deberta-v3-large__sst2__train-8-8,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0315442271530628,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
4819,akdeniz27/deberta-v2-xlarge-cuad,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0878007486462593,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
4820,malay-huggingface/t5-super-super-tiny-bahasa-cased,Pretrained T5 super-super-tiny language model for Malay,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
4821,google/t5-efficient-large-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,0.0332818096503615,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
4822,allenai/wmt16-en-de-12-1,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0013514673337339,-1.0,NLP,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
4823,sultan/BioM-ELECTRA-Large-Generator,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
4824,google/t5-efficient-small-el64,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0240775011479854,-1.0,NLP,85fbcd64-cfb7-4bb0-a26b-c389bdc3325b___t5-efficient-small-el64,HF
4825,microsoft/layoutlm-base-cased,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0913870334625244,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
4826,beston91/gpt2-xl_ft_logits_10k,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0039436481893062,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
4827,google/t5-efficient-xl-nl2,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0644555427134036,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
4828,TJMUCH/transcriptome-iseeek,Its original repository can be found here,tau/splinter-large-qass,0.0811097994446754,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4829,j-hartmann/purchase-intention-english-roberta-large,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.021888884405295,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
4830,mimicheng/codeparrot-ds,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0387534722685813,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
4831,hfl/chinese-electra-180g-small-ex-generator,I have made no attempts to censor,bigjoedata/rockbot,0.0041386261582374,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
4832,nherve/flaubert-oral-ft,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,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
4833,KoichiYasuoka/roberta-base-ukrainian,"The model has 8 layers, 6 attention heads, 768 hidden units and 3072 feed forward size",castorini/afriberta_base,0.0305548887699842,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
4834,anirudh21/xlnet-base-cased-finetuned-rte,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0667668431997299,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
4835,facebook/blenderbot-3B,"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,0.0242684076407125,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
4836,google/t5-efficient-large-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,0.0603553615510463,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
4837,google/t5-efficient-small-ff12000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0556603632867335,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
4838,cross-encoder/nli-deberta-v3-xsmall,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0,-1.0,NLP,b92eb9c4-6252-4fde-83fe-c83ccc805e25___nli-deberta-v3-xsmall,HF
4839,mimi/Waynehills-NLP-doogie,"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,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
4840,TJMUCH/transcriptome-iseeek,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,0.093377985060215,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4841,allenai/wmt19-de-en-6-6-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,0.0084547810256481,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
4842,KoichiYasuoka/roberta-small-belarusian,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0070013348013162,-1.0,NLP,44f388cd-c91d-4217-bf49-96ae06342711___roberta-small-belarusian,HF
4843,uer/t5-v1_1-base-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,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
4844,everdoubling/byt5-Korean-small,"Data prepared for this task as described in this project,  using the simulated admission notes (taken from discharge summaries)",mnaylor/bigbird-base-mimic-mortality,0.0197182750329375,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
4845,hfl/rbt3,T5-Efficient-TINY-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh16,0.083425685763359,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
4846,google/t5-efficient-base-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,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
4847,microsoft/layoutlm-base-cased,LayoutLM archives the SOTA results on multiple datasets,microsoft/layoutlm-base-cased,1.0,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
4848,google/t5-efficient-small-nl2,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0917345844209194,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
4849,google/t5-efficient-small-ff6000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0597820971161126,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
4850,google/t5-efficient-small-kv256,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,0.0706349536776542,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
4851,google/t5-efficient-small-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-v1,0.0577833894640206,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
4852,gilparmentier/pokemon_gptj_model,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
4853,groar/gpt-neo-1.3B-finetuned-escape2,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
4854,google/t5-efficient-large-nh8,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0516743045300245,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
4855,google/t5-efficient-small-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,0.0425875224173068,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
4856,groar/gpt-neo-1.3B-finetuned-escape2,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.067391499876976,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
4857,uer/t5-v1_1-small-chinese-cluecorpussmall,This does not make a very readable text,pere/DeUnCaser,0.0527920434251426,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
4858,pulp/CHILDES-ParentBERTo,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,0.0635235738009214,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
4859,google/t5-efficient-large-kv128,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0730740688741206,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
4860,cmarkea/distilcamembert-base-qa,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0482903149511132,-1.0,NLP,8da1b4d6-00be-45f0-8f97-c09b46690896___distilcamembert-base-qa,HF
4861,tucan9389/kcbert-base-finetuned-squad,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0280879475176334,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
4862,google/t5-efficient-large-el8,This does not make a very readable text,pere/DeUnCaser,0.0674717836081981,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
4863,sentence-transformers/paraphrase-MiniLM-L3-v2,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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
4864,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.047457505017519,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
4865,aubmindlab/aragpt2-mega,"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,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
4866,mgreenbe/bertlet-base-uncased-for-sequence-classification,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0558618828654289,-1.0,NLP,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
4867,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,More information can be found in our github and our iGEMs wiki,Vilnius-Lithuania-iGEM/Albumin,0.072244793176651,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
4868,sonoisa/byt5-small-japanese,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-12,0.0949555784463882,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
4869,DemangeJeremy/4-sentiments-with-flaubert,"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,0.0301895476877689,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4870,hfl/chinese-xlnet-mid,I have made no attempts to censor,bigjoedata/rockbot,0.0149066733817259,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
4871,hfl/rbtl3,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0589478015899657,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
4872,aditeyabaral/sentencetransformer-bert-hinglish-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,0.062367670238018,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
4873,obss/mt5-base-3task-highlight-tquad2,"Language model: mt5-baseLanguage: TurkishDownstream-task: Extractive QA/QG, Answer ExtractionTraining data: TQuADv2-trainCode: https://github",obss/mt5-base-3task-highlight-tquad2,1.0,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
4874,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-XL-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl8,0.0397726474329829,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
4875,mwesner/reformer-clm,Version 1.1Chinese T5 Version 1.1 includes the following improvements compared to our Chinese T5 model:,uer/t5-v1_1-small-chinese-cluecorpussmall,0.0888710170984268,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
4876,TJMUCH/transcriptome-iseeek,"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,0.0693953931331634,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
4877,ian0delond/model-test,"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,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
4878,google/t5-efficient-large-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-p1,0.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
4879,aditeyabaral/sentencetransformer-bert-hinglish-small,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,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
4880,allenai/wmt19-de-en-6-6-big,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0686275288462638,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
4881,cammy/led-large-16384-arxiv-100-MDS,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0592430606484413,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
4882,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,During the process document boundaries are kept,PlanTL-GOB-ES/gpt2-large-bne,0.0750854387879371,-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
4883,google/t5-efficient-base-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,0.0439213905483483,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
4884,google/t5-efficient-tiny-nh16,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0429702308028935,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
4885,IDEA-CCNL/Zhouwenwang-Unified-110M,"Zhouwenwang-Unified-110M apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology",IDEA-CCNL/Zhouwenwang-Unified-110M,1.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
4886,sentence-transformers/paraphrase-MiniLM-L3-v2,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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
4887,google/t5-efficient-small-nl36,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.071537535637617,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
4888,damlab/GO-language,That type of translation model will be useful for predicting the function of novel genes,damlab/GO-language,1.0,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
4889,google/t5-efficient-large-kv32,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0446545518934726,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
4890,google/t5-efficient-xl-nl4,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0934730395674705,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
4891,UWB-AIR/MQDD-duplicates,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.004406931810081,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
4892,google/t5-11b-ssm-nqo,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
4893,IDEA-CCNL/Erlangshen-Longformer-330M,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
4894,google/t5-efficient-small-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,0.0343686966225505,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
4895,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0881170704960823,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
4896,cross-encoder/stsb-TinyBERT-L-4,"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,0.019107743864879,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
4897,mrm8488/mobilebert-uncased-finetuned-squadv2,This model is cased: it does make a difference between bulgarian and Bulgarian,rmihaylov/bert-base-pos-theseus-bg,0.0618026548375685,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
4898,ian0delond/model-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,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
4899,clue/xlnet_chinese_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,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
4900,uer/t5-v1_1-base-chinese-cluecorpussmall,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.025027111172676,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
4901,beston91/gpt2-xl_ft_logits_10k,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0874965190887451,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
4902,google/t5-efficient-small-nl24,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0895561128854751,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
4903,google/t5-efficient-small-ff3000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
4904,anas-awadalla/bert-tiny-finetuned-squad,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0972226560115814,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
4905,akdeniz27/deberta-v2-xlarge-cuad,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0279850512742996,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
4906,google/t5-efficient-small-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,0.0551791489124297,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
4907,cammy/led-base-16384-100-MDS,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0131545439362525,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
4908,google/t5-efficient-base-nl36,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0458197183907031,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
4909,mrp/simcse-model-wangchanberta,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0564363105222582,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
4910,sonoisa/byt5-small-japanese,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0355512090027332,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
4911,ductuan024/AimeLaw,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0879890359938144,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
4912,google/t5-efficient-large-nl32,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0500364359468221,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
4913,krevas/finance-koelectra-base-generator,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
4914,google/t5-efficient-large-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,0.0744191482663154,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
4915,google/t5-efficient-large-dm768,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,1.0,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
4916,tartuNLP/gpt-4-est-base,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.075516078621149,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
4917,uer/t5-v1_1-base-chinese-cluecorpussmall,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0885794647037982,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
4918,fnlp/bart-large-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,0.0288895582780241,-1.0,NLP,89feb9a8-f852-4c5e-a084-dc679d3950af___bart-large-chinese,HF
4919,google/t5-efficient-tiny-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,0.0561007503420113,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
4920,mrm8488/deberta-v3-small-finetuned-mrpc,For more details please refer to the official website,flaubert/flaubert_small_cased,0.066833384335041,-1.0,NLP,3585ff2f-689d-4aa1-9a92-76345ab4a74e___deberta-v3-small-finetuned-mrpc,HF
4921,tuner007/pegasus_qa,The model was pruned from 24 to 12 transformer layers before fine-tuning,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0708715468645095,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
4922,google/t5-efficient-tiny-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,0.0596545115113257,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
4923,nghuyong/ernie-tiny,"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,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
4924,rdenadai/BR_BERTo,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0065988311544061,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
4925,stas/pegasus-cnn_dailymail-tiny-random,Total training time was around 16 days on 64 GPUs,sberbank-ai/rugpt3medium_based_on_gpt2,0.0787581577897071,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
4926,google/t5-efficient-base-dm1000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0865049175918102,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
4927,google/t5-efficient-mini-nl24,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0304172262549399,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
4928,tau/tavbert-he,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,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
4929,malay-huggingface/t5-super-tiny-bahasa-cased,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0327310934662818,-1.0,NLP,de463286-f1af-43a0-87fa-543b5db2e9ac___t5-super-tiny-bahasa-cased,HF
4930,saibo/random-roberta-mini,The weight of random-roberta-mini 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-mini,1.0,-1.0,NLP,44cf411c-16d5-43c1-b7f9-e8a3ea9d2c91___random-roberta-mini,HF
4931,VietAI/gpt-neo-1.3B-vietnamese-news,T5-Efficient-BASE-EL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-el16,0.0648026969283818,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
4932,google/t5-efficient-small-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-v1,0.0521268593147396,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
4933,amazon/bort,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0668802689760922,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
4934,everdoubling/byt5-Korean-small,"If you found this model useful, please cite the original work:",filco306/gpt2-switchboard-paraphraser,0.0191855119541287,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
4935,SupriyaArun/squeezebert-uncased-finetuned-squad,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0948259979486465,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
4936,dbmdz/electra-base-turkish-cased-discriminator,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0341099482029676,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
4937,google/t5-efficient-tiny-ff9000,Its original repository can be found here,tau/splinter-large-qass,0.0340582821518182,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
4938,google/t5-efficient-tiny-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-v1,0.0747809894382953,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
4939,Palak/microsoft_deberta-base_squad,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0201923716813325,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
4940,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0878611505031585,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
4941,allenai/wmt19-de-en-6-6-big,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,1.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
4942,minimaxir/magic-the-gathering,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,1.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
4943,google/t5-efficient-small-nl48,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0791211649775504,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
4944,PlanTL-GOB-ES/gpt2-large-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-large-bne,1.0,-1.0,NLP,74483f95-0fd4-4003-8604-3d4f11cefb13___gpt2-large-bne,HF
4945,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0159452352672815,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
4946,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
4947,funnel-transformer/large-base,Pretrained model on English language using a similar objective objective as ELECTRA,funnel-transformer/large-base,1.0,-1.0,NLP,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
4948,google/t5-efficient-base-ff12000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0359999202191829,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
4949,google/t5-efficient-tiny-nl6,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0766886919736861,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
4950,google/t5-efficient-tiny-nl12,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0279958564788103,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
4951,IDEA-CCNL/Erlangshen-Longformer-110M,Please CITE our repo when it is used to help produce published results or is incorporated into other software,nguyenvulebinh/envibert,0.042589284479618,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
4952,eli4s/Bert-L12-h256-A4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
4953,allenai/macaw-3b,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0023723604778448,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
4954,rmihaylov/pegasus-base-cnn-dailymail-bg,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0044087269343435,-1.0,NLP,b86e9e5c-8766-48b8-9ceb-bd3caf84a70d___pegasus-base-cnn-dailymail-bg,HF
4955,stas/mt5-tiny-random,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0056688757613301,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
4956,google/t5-efficient-mini,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0332726947963237,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
4957,patrickvonplaten/unispeech-sat-large-timit-ft,It was introduced in this paper,readerbench/RoBERT-small,0.0964969992637634,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
4958,google/t5-efficient-small-ff9000,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0449765864759683,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
4959,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0989015474915504,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
4960,akdeniz27/deberta-v2-xlarge-cuad,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0266798511147499,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
4961,mrp/simcse-model-wangchanberta,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0281955190002918,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
4962,mimi/Waynehills-NLP-doogie,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
4963,minimaxir/magic-the-gathering,We present DistilCamemBERT-QA which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language,cmarkea/distilcamembert-base-qa,0.0021531488746404,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
4964,google/t5-efficient-base-el16,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0092434398829937,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
4965,hf-internal-testing/tiny-random-rembert,For best results use python as below,pszemraj/led-large-book-summary,0.0673442482948303,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
4966,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-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,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
4967,SupriyaArun/squeezebert-uncased-finetuned-squad,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.059373915195465,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
4968,tartuNLP/gpt-4-est-large,The model was trained with the default configuration of RoBerta and Trainer from the Huggingface,jean-paul/kinyaRoberta-small,0.0945360139012336,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
4969,groar/gpt-neo-1.3B-finetuned-escape2,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0293028838932514,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
4970,google/t5-efficient-tiny-nl16,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
4971,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff6000,0.0777054987847805,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
4972,google/bert_uncased_L-4_H-128_A-2,Its original repository can be found here,tau/splinter-large-qass,0.0176675698409477,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
4973,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
4974,pere/DeUnCaser,"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,1.0,-1.0,NLP,380ed63b-a940-4677-b805-3029baa06555___DeUnCaser,HF
4975,google/t5-efficient-small-nl40,"BabyBERTa was trained with add_prefix_space=True, so it will not work properly with the tokenizer defaults",phueb/BabyBERTa-2,0.0044842585921287,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
4976,stas/mt5-tiny-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,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
4977,google/t5-efficient-small-nl48,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0640235804021358,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
4978,hfl/rbt3,"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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
4979,google/fnet-large,FNet is a transformers model with attention replaced with fourier transforms,google/fnet-large,1.0,-1.0,NLP,be8c3b54-acb9-41d3-9190-d3a35ba6f7a6___fnet-large,HF
4980,google/t5-efficient-base-nh16,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0801958478987216,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
4981,pszemraj/led-large-book-summary,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,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
4982,skt/ko-gpt-trinity-1.2B-v0.5,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0193263068795204,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
4983,VietAI/gpt-neo-1.3B-vietnamese-news,"I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic",kssteven/ibert-roberta-large,0.0228371568955481,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
4984,google/t5-efficient-large-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,0.0254571679979563,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
4985,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-NH1 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh1,0.085212156176567,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
4986,ian0delond/model-test,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0117855239659547,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
4987,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0759422481060028,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
4988,groar/gpt-neo-1.3B-finetuned-escape2,"The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation",TristanBehrens/js-fakes-4bars,0.0532124154269695,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
4989,google/t5-efficient-small-el32,"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,0.0492111686617135,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
4990,hfl/rbtl3,"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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
4991,google/bert_uncased_L-10_H-128_A-2,I have made no attempts to censor,bigjoedata/rockbot,0.0217506180827816,-1.0,NLP,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
4992,google/t5-efficient-large-nl20,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0777754634618758,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
4993,abdusahmbzuai/ft-tatoeba-ar-en,"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,0.0431338027119636,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
4994,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff12000,0.0801715105772018,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
4995,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"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-12,0.0902992859482765,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
4996,google/t5-efficient-small-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,0.0335883349180222,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
4997,razent/SciFive-large-Pubmed,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,5c842580-528f-4a3c-8fe4-7a94dc0ffd6e___SciFive-large-Pubmed,HF
4998,google/t5-efficient-small-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-p1,0.0007218047976494,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
4999,hfl/rbt3,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0567680709064006,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5000,TJMUCH/transcriptome-iseeek,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_gpt2,0.0496672987937927,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5001,hakurei/gpt-j-random-tinier,com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints,sultan/BioM-ELECTRA-Large-Generator,0.0647573042660951,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
5002,tucan9389/kcbert-base-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,0.0721096694469451,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
5003,yarongef/DistilProtBert,"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,1.0,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
5004,google/t5-efficient-tiny-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-p1,0.0152812059968709,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
5005,google/t5-efficient-xl-nl6,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0442664511501789,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
5006,google/t5-efficient-small-kv16,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
5007,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.0802681557834148,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
5008,hf-internal-testing/tiny-random-rembert,"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,0.0012279972434043,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
5009,google/t5-efficient-small-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,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
5010,cammy/led-large-16384-arxiv-100-MDS,I have made no attempts to censor,bigjoedata/rockbot,0.008447928354144,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
5011,cammy/led-large-16384-arxiv-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,0.0882237926125526,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
5012,google/t5-efficient-xl-nl16,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0955333560705185,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
5013,sentence-transformers/paraphrase-MiniLM-L3-v2,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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
5014,google/t5-efficient-tiny-nl12,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0728651992976665,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
5015,razent/SciFive-base-PMC,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,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
5016,anirudh21/xlnet-base-cased-finetuned-rte,"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,0.0144926756620407,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
5017,stas/mt5-tiny-random,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0377515777945518,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5018,dbmdz/convbert-base-german-europeana-cased,"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,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
5019,groar/gpt-neo-1.3B-finetuned-escape2,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0016851965337991,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
5020,google/t5-efficient-tiny-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,0.0564981456845998,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
5021,google/t5-efficient-base-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,0.0593859292566775,-1.0,NLP,956b7142-f5e8-4b4d-a19f-ed1e29beb7d3___t5-efficient-base-ff9000,HF
5022,google/t5-efficient-tiny-nh1,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0211903266608715,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
5023,dhpollack/distilbert-dummy-sentiment,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
5024,dbmdz/bert-medium-historic-multilingual-cased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0076237618923186,-1.0,NLP,91fcf7db-12a9-45b5-b5dd-03167d1a2b9a___bert-medium-historic-multilingual-cased,HF
5025,akdeniz27/deberta-v2-xlarge-cuad,"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,0.0736711770296096,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
5026,google/t5-efficient-small-dm768,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0244691036641597,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
5027,gilparmentier/pokemon_gptj_model,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,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
5028,aubmindlab/aragpt2-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-large,1.0,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
5029,bigjoedata/rockbot,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,12596660-b17c-4424-831a-ea852ca40a67___rockbot,HF
5030,flax-community/bengali-t5-base,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0212711445055902,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
5031,google/t5-efficient-large-nl36,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0197625197470188,-1.0,NLP,3ac61c67-6726-4138-bd5b-ffea338b2459___t5-efficient-large-nl36,HF
5032,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,1.0,-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
5033,google/t5-efficient-tiny-ff12000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0362461442127823,-1.0,NLP,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
5034,uer/t5-v1_1-small-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,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
5035,tau/splinter-base-qass,Its original repository can be found here,tau/splinter-base-qass,1.0,-1.0,NLP,f68d3e26-2d96-4b99-a309-34b532e9740f___splinter-base-qass,HF
5036,stas/mt5-tiny-random,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0634873360395431,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5037,stas/mt5-tiny-random,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0493076592683792,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5038,hfl/chinese-electra-small-ex-discriminator,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0596902673132717,-1.0,NLP,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
5039,hfl/rbtl3,T5-Efficient-TINY-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh16,0.083425685763359,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
5040,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-TINY-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-el8,0.0740745365619659,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
5041,abdusahmbzuai/ft-tatoeba-ar-en,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0621486194431781,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
5042,clue/roberta_chinese_3L312_clue_tiny,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0918017849326133,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
5043,google/t5-efficient-tiny-ff3000,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0454858615994453,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
5044,google/t5-efficient-large-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-p1,0.0,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
5045,bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16,"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,0.0962359085679054,-1.0,NLP,5f656a67-d387-4a1f-b78c-6b616fcdbaa2___bluebert_pubmed_uncased_L-24_H-1024_A-16,HF
5046,google/t5-efficient-base-kv32,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
5047,VietAI/gpt-neo-1.3B-vietnamese-news,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0589941721409559,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
5048,google/t5-efficient-large-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,0.0142705850303173,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
5049,stas/pegasus-cnn_dailymail-tiny-random,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,0.0753360874950885,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
5050,google/t5-efficient-base-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,0.0474684555083512,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
5051,google/t5-efficient-small-ff1000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0287235528230666,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
5052,google/t5-efficient-large-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,0.0849077925086021,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
5053,google/t5-efficient-xl-nl12,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0583408512175082,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
5054,ml6team/distilbart-tos-summarizer-tosdr,The extractive  summarization shortens the TandC to be further summarized by this model,ml6team/distilbart-tos-summarizer-tosdr,1.0,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
5055,jorge-henao/gpt2-small-spanish-disco-poetry-wt,retrieved with ElasticSearch),cross-encoder/ms-marco-MiniLM-L-4-v2,0.0515501573681831,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
5056,sentence-transformers/paraphrase-MiniLM-L3-v2,A distilled version of ProtBert-UniRef100 model,yarongef/DistilProtBert,0.0935567021369934,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
5057,minimaxir/magic-the-gathering,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
5058,google/t5-efficient-base-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,0.0556842368096112,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
5059,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"If you want to get information for training, you should see config",lassl/roberta-ko-small,0.0603791289031505,-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
5060,SupriyaArun/squeezebert-uncased-finetuned-squad,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0432634055614471,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
5061,beston91/gpt2-xl_ft_logits_10k,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
5062,cammy/led-large-16384-arxiv-100-MDS,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0876396894454956,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
5063,google/t5-efficient-base-nh32,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0665548797696828,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
5064,celine98/canine-s-finetuned-sst2,Its original repository can be found here,tau/splinter-large-qass,0.0954109579324722,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
5065,google/t5-efficient-base-nl48,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.034365695901215,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
5066,google/t5-efficient-large-kv16,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0,-1.0,NLP,98f64896-8129-4d5a-b61b-1f6473b488b2___t5-efficient-large-kv16,HF
5067,google/t5-efficient-base-ff6000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0253326036036014,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
5068,google/t5-efficient-tiny-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,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
5069,google/t5-efficient-tiny-ff9000,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0240453099831938,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
5070,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
5071,google/t5-efficient-large-dm768,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0452041719108819,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
5072,google/t5-efficient-base-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,0.0559266936033964,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
5073,kyryl0s/gpt2-uk-xxs,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,1.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
5074,google/t5-efficient-large-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,0.0208777580410242,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
5075,microsoft/layoutlm-large-uncased,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0544680058956146,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
5076,j-hartmann/purchase-intention-english-roberta-large,This repository provides an extra-small-sized Japanese GPT-2 model,rinna/japanese-gpt2-xsmall,0.0576727654164035,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
5077,google/t5-efficient-tiny-nh1,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0493075978010892,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
5078,google/t5-efficient-base-ff6000,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0535089299082755,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
5079,google/t5-efficient-xl-nl8,"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,0.0845154710114002,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
5080,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0719648003578186,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
5081,google/t5-efficient-base-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,0.0275236619636416,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
5082,DemangeJeremy/4-sentiments-with-flaubert,This BERT model was trained using the Megatron-LM library,KBLab/megatron-bert-large-swedish-cased-165k,0.0288658514618873,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5083,stas/t5-very-small-random,"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-128,0.0974193438887596,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
5084,GKLMIP/electra-myanmar-small-uncased,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0245849899947643,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
5085,mikaelsouza/msft-regular-model,"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,0.0868489146232605,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
5086,sentence-transformers/paraphrase-MiniLM-L3-v2,"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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
5087,GKLMIP/electra-myanmar-small-uncased,You can use it with Hugging Face's Zero-shot pipeline to make zero-shot classifications,Recognai/zeroshot_selectra_small,0.0346070658415555,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
5088,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
5089,stas/mt5-tiny-random,This is a tiny random mt5 model used for testingSee mt5-make-tiny-model,stas/mt5-tiny-random,1.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5090,google/t5-efficient-base-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,0.0034080743789673,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
5091,google/t5-efficient-large-dm128,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.01892208494246,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
5092,stas/mt5-tiny-random,"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,0.0281511507928371,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5093,GKLMIP/electra-myanmar-small-uncased,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
5094,TJMUCH/transcriptome-iseeek,The impact of design choices on the performance of biomedical language models recently has been a subject for investigation,sultan/BioM-ELECTRA-Large-Generator,0.0782903805375099,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5095,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5096,Palak/microsoft_deberta-base_squad,The following function can instead be used for encoding and decoding:,google/reformer-enwik8,0.098402127623558,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
5097,allenai/wmt16-en-de-dist-6-1,I have made no attempts to censor,bigjoedata/rockbot,0.0700647141784429,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
5098,mnaylor/bigbird-base-mimic-mortality,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
5099,Palak/microsoft_deberta-base_squad,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0375198237597942,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
5100,crabz/distil-slovakbert-ner,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0771119445562362,-1.0,NLP,661f08cb-0673-4c8e-8145-ceb89bb531ea___distil-slovakbert-ner,HF
5101,aditeyabaral/sentencetransformer-distilbert-hinglish-big,The training code is available here: SBERT,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0596228167414665,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
5102,mwesner/reformer-clm,T5-Efficient-BASE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv128,0.0507235303521156,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
5103,google/t5-efficient-base-nl48,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
5104,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"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,0.0517799369990825,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
5105,anirudh21/xlnet-base-cased-finetuned-rte,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0412849187850952,-1.0,NLP,95401020-2af1-4968-8438-5f0c7e0baa98___xlnet-base-cased-finetuned-rte,HF
5106,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,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,1.0,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
5107,facebook/m2m100-12B-avg-10-ckpt,"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,0.0237007183022796,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
5108,google/t5-efficient-large-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,0.021436732262373,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
5109,dbmdz/electra-base-turkish-cased-discriminator,It can be used to pre-train transformer networks using relatively little compute,dbmdz/electra-base-turkish-cased-discriminator,1.0,-1.0,NLP,145f47a1-2c83-4e30-8bda-8e883a5ec804___electra-base-turkish-cased-discriminator,HF
5110,google/t5-efficient-large-el4,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.085354708135128,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
5111,google/t5-efficient-tiny-ff3000,"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,0.06106898188591,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
5112,google/t5-efficient-xl-nl4,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.078305572271347,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
5113,google/t5-efficient-large-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-v1,0.070138819515705,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
5114,google/t5-efficient-base-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,0.0374040314927696,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
5115,rdenadai/BR_BERTo,It can be used to pre-train transformer networks using relatively little compute,skplanet/dialog-koelectra-small-generator,0.0060527560611566,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
5116,ai4bharat/MultiIndicHeadlineGeneration,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
5117,google/t5-efficient-base-ff6000,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0268912352621554,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
5118,google/t5-efficient-base-kv16,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,498f05d1-da72-4933-bf7f-9cf2c772ae35___t5-efficient-base-kv16,HF
5119,google/t5-efficient-tiny-nl2,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0284090638160705,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
5120,clue/roberta_chinese_3L312_clue_tiny,"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,0.0926553010940551,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
5121,google/t5-efficient-small-nl36,T5-Efficient-SMALL-NL36 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl36,1.0,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
5122,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-NL16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nl16,0.0801049582660198,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
5123,gayanin/bart-mlm-pubmed-medterm,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0704357922077179,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
5124,google/t5-efficient-base-ff1000,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0315343774855136,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
5125,diper1998/distilgpt2-finetuned-AT,This is an implementation of Chinese BART-Base,fnlp/bart-base-chinese,0.044519305229187,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
5126,KoichiYasuoka/roberta-base-ukrainian,The model is not ready yet but I'm working on it,kyryl0s/gpt2-uk-xxs,0.0101025570183992,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
5127,hfl/rbt3,net Retrieve and Re-rank for more details,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0589478015899657,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5128,superspray/electra_large_discriminator_squad2_custom_dataset,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.069778062403202,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
5129,google/t5-efficient-large-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,0.0696371234953403,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
5130,negfir/distilbert-base-uncased-finetuned-cola,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.08477084338665,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
5131,aditeyabaral/sentencetransformer-bert-hinglish-small,This model is trained on uppercase amino acids: it only works with capital letter amino acids,Rostlab/prot_bert,0.070020355284214,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
5132,google/t5-efficient-large-nh24,This does not make a very readable text,pere/DeUnCaser,0.0421101283282041,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
5133,1one5ome/gpt2-chinese-gulong,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
5134,cross-encoder/stsb-TinyBERT-L-4,"If you are contribute, if you want to say hello, if you want to know more, find me on LinkedIn",TristanBehrens/js-fakes-4bars,0.0203718063421547,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
5135,sentence-transformers/paraphrase-MiniLM-L3-v2,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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
5136,google/t5-efficient-small-ff12000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0273999972268938,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
5137,KoichiYasuoka/roberta-base-ukrainian,We are initially releasing small version pre-trained model,skplanet/dialog-koelectra-small-generator,0.0553018674254417,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
5138,mrm8488/mobilebert-uncased-finetuned-squadv2,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0671301720043023,-1.0,NLP,f4372d6f-271d-419f-bc74-12fbe70a3b43___mobilebert-uncased-finetuned-squadv2,HF
5139,mimicheng/codeparrot-ds,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
5140,google/t5-efficient-small-el8,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0818838104605674,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
5141,google/t5-efficient-base-ff1000,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.030393622815609,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
5142,google/t5-efficient-tiny-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,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
5143,cammy/led-base-16384-100-MDS,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0125110950320959,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
5144,minimaxir/magic-the-gathering,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0054538375698029,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
5145,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-TINY-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh32,0.0613186235229174,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
5146,microsoft/layoutlm-large-uncased,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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
5147,google/t5-efficient-large-kv128,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0549075854942202,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
5148,funnel-transformer/small,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,cd7f3a53-06ad-4d6b-9fb7-20b3f782ea01___small,HF
5149,google/t5-efficient-small-nl2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0658849477767943,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
5150,DemangeJeremy/4-sentiments-with-flaubert,Model is based on GPT-J and has over 1.4B trainable parameters,Milos/slovak-gpt-j-1.4B,0.0125605687499046,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5151,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,"Google's mT5mT5 is pretrained on the mC4 corpus, covering 101 languages:",mrm8488/mT5-small-finetuned-tydiqa-for-xqa,1.0,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
5152,gchhablani/fnet-base-finetuned-wnli,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0099007207900285,-1.0,NLP,68e2fa4a-e530-43d8-b3c5-9301ef5150a3___fnet-base-finetuned-wnli,HF
5153,google/t5-efficient-tiny-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,0.0670393016189336,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
5154,uer/t5-v1_1-base-chinese-cluecorpussmall,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0263285245746374,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
5155,superspray/electra_large_discriminator_squad2_custom_dataset,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0204472690820693,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
5156,tau/tavbert-he,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0253124441951513,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
5157,tucan9389/kcbert-base-finetuned-squad,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0013102125376462,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
5158,google/t5-efficient-small-el12,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0409551206976174,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
5159,google/t5-efficient-large-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,0.0514673953875899,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
5160,hajime9652/xlnet-japanese,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.084919661283493,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
5161,facebook/mbart-large-50-many-to-one-mmt,mbart-large-50-many-to-many-mmt is fine-tuned for multilingual machine translation,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
5162,google/t5-efficient-base-nl40,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0382662867195904,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
5163,microsoft/layoutlm-base-cased,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0651535540819168,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
5164,google/bert_uncased_L-4_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,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
5165,google/t5-efficient-large-dm768,"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,0.0490479543805122,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
5166,google/t5-efficient-base-dm512,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0434179045259952,-1.0,NLP,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
5167,aditeyabaral/sentencetransformer-bert-hinglish-small,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,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
5168,inokufu/flaubert-base-uncased-xnli-sts,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,cd03c18e-45a8-41e2-9606-5cef1c5b4132___flaubert-base-uncased-xnli-sts,HF
5169,Gilles/FongBERT,"We are also continuously working on releasing better-performing models, so watch the repository for updates",DTAI-KULeuven/robbertje-1-gb-bort,0.0419181548058986,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
5170,allenai/wmt19-de-en-6-6-big,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
5171,Iskaj/hf-challenge-test,ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-1.4B,0.0912821590900421,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
5172,TJMUCH/transcriptome-iseeek,The model architecture is custom for production so it only contains 70M parameters,nguyenvulebinh/envibert,0.0801974162459373,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5173,google/t5-efficient-large-dm512,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0054920576512813,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
5174,brad1141/Longformer-finetuned-norm,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0537038967013359,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
5175,dhpollack/distilbert-dummy-sentiment,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0093815606087446,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
5176,dbmdz/electra-small-turkish-cased-discriminator,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.037776730954647,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
5177,tuner007/pegasus_qa,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0657060146331787,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
5178,minimaxir/magic-the-gathering,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
5179,IDEA-CCNL/Erlangshen-Longformer-110M,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0015744185075164,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
5180,mnaylor/bigbird-base-mimic-mortality,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0068674185313284,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
5181,qarib/bert-base-qarib_far_6500k,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,0.0526857827790081,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
5182,mimicheng/codeparrot-ds,"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,0.0646731033921241,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
5183,amazon/bort,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,1.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
5184,google/t5-efficient-small-nl20,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
5185,hfl/chinese-electra-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,073a2a47-3a11-411f-885b-d87e70687de1___chinese-electra-small-ex-discriminator,HF
5186,suzuki/distilbert-base-uncased-finetuned-squad,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.079643115401268,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
5187,ml6team/distilbart-tos-summarizer-tosdr,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0277156857773661,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
5188,mnaylor/bigbird-base-mimic-mortality,This does not make a very readable text,pere/DeUnCaser,0.0029678517021238,-1.0,NLP,323e3524-ab62-4ec6-a25d-2fdceb84c056___bigbird-base-mimic-mortality,HF
5189,google/t5-efficient-xl-nl6,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,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
5190,google/t5-efficient-tiny-nh8,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0179013665765523,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
5191,kyryl0s/gpt2-uk-xxs,"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,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
5192,rinna/japanese-gpt2-xsmall,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0230195211867491,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
5193,krevas/finance-koelectra-base-discriminator,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-discriminator,1.0,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
5194,google/t5-efficient-tiny-ff9000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0318080484867095,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
5195,sentence-transformers/paraphrase-MiniLM-L3-v2,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
5196,Iskaj/hf-challenge-test,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
5197,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0573709420859813,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
5198,google/t5-efficient-xl-nl4,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0753537267446518,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
5199,ductuan024/AimeLaw,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,0.0483928527683018,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
5200,google/t5-efficient-base-nl2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0252213599160314,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
5201,google/t5-efficient-small-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,0.0557970879599451,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
5202,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0996326953172683,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
5203,google/t5-efficient-xl-nl16,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0387951508164405,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
5204,hf-internal-testing/tiny-random-rembert,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0379788838326931,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
5205,google/t5-efficient-small-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,0.0583217423409223,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
5206,google/t5-efficient-small-nl20,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0634984001517295,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
5207,mrp/simcse-model-wangchanberta,I have made no attempts to censor,bigjoedata/rockbot,0.0112529024481773,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
5208,google/t5-efficient-tiny-ff6000,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0099248434416949,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
5209,ml6team/mt5-small-german-finetune-mlsum,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0079444698058068,-1.0,NLP,6a8defa1-8922-4cfa-86d6-c463437ea85b___mt5-small-german-finetune-mlsum,HF
5210,microsoft/layoutlm-base-uncased,"!!!  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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
5211,antoiloui/belgpt2,Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously,mldmm/GlassBERTa,0.0467403372749686,-1.0,NLP,fc3aacb3-436b-4e04-8d0a-f6d575435b17___belgpt2,HF
5212,kyryl0s/gpt2-uk-xxs,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
5213,facebook/hubert-xlarge-ll60k,"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,0.0319744190201163,-1.0,NLP,f8ea933d-3e55-44b6-bed0-a8d0b770de50___hubert-xlarge-ll60k,HF
5214,google/t5-efficient-small-el16-dl4,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0436574108898639,-1.0,NLP,aea16bf8-e663-4a6b-b3d0-33a7d5a8dbd4___t5-efficient-small-el16-dl4,HF
5215,hf-internal-testing/tiny-random-rembert,"The model generates 4 bars at a time of Bach-like chorales with four voices (soprano, alto, tenor, bass)",TristanBehrens/js-fakes-4bars,0.0349237695336341,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
5216,Davlan/mbart50-large-eng-yor-mt,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,1.0,-1.0,NLP,12474c75-dc49-473e-bcb6-eea8354b1253___mbart50-large-eng-yor-mt,HF
5217,DemangeJeremy/4-sentiments-with-flaubert,BERT-Small created by Google Research and fine-tuned on SQuAD 2.0 for QandA downstream task,mrm8488/bert-small-finetuned-squadv2,0.0365288592875003,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5218,google/t5-efficient-tiny-nh8,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0117314234375953,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
5219,TJMUCH/transcriptome-iseeek,"It will continue to be optimized in the direction of model scale, knowledge integration, and supervision task assistance",IDEA-CCNL/Zhouwenwang-Unified-110M,0.0991691425442695,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5220,KoichiYasuoka/roberta-base-ukrainian,"Since it has 4 attention heads, the head size is 64 just as for the BERT base model",eli4s/Bert-L12-h256-A4,0.0137758227065205,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
5221,tucan9389/kcbert-base-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,0.0954520255327224,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
5222,google/t5-efficient-small-el32,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
5223,anas-awadalla/bert-tiny-finetuned-squad,Google's T5 for Closed Book Question Answering,google/t5-large-ssm-nq,0.0556842610239982,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
5224,google/bert_uncased_L-4_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-4_H-128_A-2,1.0,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
5225,akdeniz27/deberta-v2-xlarge-cuad,"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,0.0912069156765937,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
5226,google/t5-efficient-large-nh8,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
5227,mwesner/reformer-clm,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0319627188146114,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
5228,microsoft/layoutlm-base-cased,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.010490633547306,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
5229,macedonizer/al-roberta-base,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0,-1.0,NLP,4834698e-c7aa-4e7e-86fe-be2d55c59e7b___al-roberta-base,HF
5230,uer/t5-v1_1-small-chinese-cluecorpussmall,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.041525937616825,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
5231,minimaxir/magic-the-gathering,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
5232,hyunwoongko/reddit-9B,"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,0.0242684076407125,-1.0,NLP,a1d7fc9a-5b9a-4841-a83a-786b85e143dc___reddit-9B,HF
5233,allenai/wmt16-en-de-dist-6-1,"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,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
5234,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0683243051171302,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
5235,google/t5-efficient-small-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,0.0628613457083701,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
5236,ffsouza/t5-tiny-random-length-96-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,0.0420409142971038,-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
5237,google/t5-efficient-large-el6,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0485642431303858,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
5238,google/t5-efficient-base-ff1000,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0,-1.0,NLP,b9705617-1400-4b64-9a37-259280f4e19e___t5-efficient-base-ff1000,HF
5239,kyryl0s/gpt2-uk-xxs,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
5240,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,Scores on the MultiIndicHeadlineGeneration test sets are as follows:,ai4bharat/MultiIndicHeadlineGeneration,0.0485932976007461,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
5241,google/t5-efficient-tiny-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,0.0401281956583261,-1.0,NLP,6eeb40ea-744b-4621-b66a-7ee3d4eb709f___t5-efficient-tiny-ff6000,HF
5242,DemangeJeremy/4-sentiments-with-flaubert,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,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5243,uer/pegasus-base-chinese-cluecorpussmall,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0146777816116809,-1.0,NLP,be8a59ae-687f-4aeb-9476-3ea57f0e9d39___pegasus-base-chinese-cluecorpussmall,HF
5244,google/t5-efficient-small-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,0.0458960151299834,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
5245,stas/mt5-tiny-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_gpt2,0.0576915591955184,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5246,google/t5-efficient-large-dm768,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0387346129864453,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
5247,google/t5-efficient-small-kv32,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0079463496804237,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
5248,beston91/gpt2-xl_ft_logits_10k,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0728469863533973,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
5249,skplanet/dialog-koelectra-small-generator,Dialog-ELECTRA model is made based on the ELECTRA model,skplanet/dialog-koelectra-small-generator,1.0,-1.0,NLP,8de44d20-a63a-4655-933b-90577d8033db___dialog-koelectra-small-generator,HF
5250,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0027574077248573,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
5251,google/t5-efficient-xl-nl4,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0966673791408538,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
5252,eli4s/Bert-L12-h384-A6,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0038126651197671,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
5253,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
5254,ctoraman/RoBERTa-TR-medium-wp-66k,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0025623228400945,-1.0,NLP,a6504d4e-97c2-475d-b08b-8e4a9e576e80___RoBERTa-TR-medium-wp-66k,HF
5255,cammy/led-large-16384-arxiv-100-MDS,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.086356483399868,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
5256,google/t5-efficient-base-nh8,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0911937095224857,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
5257,malay-huggingface/t5-tiny-bahasa-cased,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0552566206703582,-1.0,NLP,a2e8a730-fcbe-4335-963f-477ec304b5f8___t5-tiny-bahasa-cased,HF
5258,google/t5-efficient-xl-nl6,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0611225292086601,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
5259,google/t5-efficient-large-nl20,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
5260,IDEA-CCNL/Erlangshen-Longformer-110M,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0494284015148877,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
5261,groar/gpt-neo-1.3B-finetuned-escape2,For more details please refer to the official website,flaubert/flaubert_small_cased,0.051931619644165,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
5262,google/t5-efficient-small-nl20,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0588421635329723,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
5263,ncduy/phobert-large-finetuned-vietnamese_students_feedback,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0981365516781807,-1.0,NLP,ff536db2-ae9b-433d-a18c-a0f9f33c85d4___phobert-large-finetuned-vietnamese_students_feedback,HF
5264,google/t5-efficient-tiny-nl16,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0412982301786541,-1.0,NLP,47d52b84-d99a-40bf-a033-eedd06759b92___t5-efficient-tiny-nl16,HF
5265,Milos/slovak-gpt-j-1.4B,Model is based on GPT-J and has over 1.4B trainable parameters,Milos/slovak-gpt-j-1.4B,1.0,-1.0,NLP,7aca8885-9a0e-4f2c-9d44-1e0287e443f1___slovak-gpt-j-1.4B,HF
5266,husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0764718428254127,-1.0,NLP,f58e670e-50c5-4e2c-b3bb-0e419816eab2___xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6,HF
5267,google/t5-efficient-xl-nl16,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,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
5268,google/t5-efficient-xl-nl8,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
5269,mimi/Waynehills-NLP-doogie,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0405661538243293,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
5270,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0318327099084854,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
5271,google/t5-efficient-large-kv256,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,0.0639556013047695,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
5272,hf-internal-testing/tiny-random-rembert,"If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite:}",unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,0.0935215353965759,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
5273,google/t5-efficient-base-el6,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0309981405735015,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
5274,NovelAI/genji-python-6B,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0403914029399553,-1.0,NLP,5f71f988-4528-4563-b769-56b831f79065___genji-python-6B,HF
5275,akdeniz27/deberta-v2-xlarge-cuad,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
5276,M-FAC/bert-mini-finetuned-stsb,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0463093566941097,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
5277,google/t5-efficient-base-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,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
5278,google/t5-efficient-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,0.0,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
5279,bolbolzaban/gpt2-persian,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0863399691879749,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
5280,brad1141/Longformer-finetuned-norm,The initial weigths were from the google/pegasus-reddit_tifu,jpcorb20/pegasus-large-reddit_tifu-samsum-512,0.0,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
5281,hf-internal-testing/tiny-random-rembert,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0937193408608436,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
5282,suzuki/distilbert-base-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,0.0489621758460998,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
5283,wukevin/tcr-bert,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.070007525384426,-1.0,NLP,be17bfc6-5661-456b-9868-bfab52710059___tcr-bert,HF
5284,google/t5-efficient-base-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,0.0161751899868249,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
5285,IDEA-CCNL/Erlangshen-MegatronBert-1.3B,"The 1.3 billion parameter Erlangshen-MegatronBert-1.3B large model, using 280G Chinese data, 32 A100 training for 14 days, is the largest open source Chinese Bert large model",IDEA-CCNL/Erlangshen-MegatronBert-1.3B,1.0,-1.0,NLP,3c1adb6c-e774-450e-967f-c26c07931f49___Erlangshen-MegatronBert-1.3B,HF
5286,google/t5-efficient-large-dm512,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0108117824420333,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
5287,microsoft/layoutlm-large-uncased,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0466160923242569,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
5288,GKLMIP/electra-myanmar-small-uncased,Final perplexity on test set is 17.4,sberbank-ai/rugpt3medium_based_on_gpt2,0.0345638524740933,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
5289,google/t5-efficient-large-nh8-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,0.0392148382961749,-1.0,NLP,14c4cb69-7f87-41df-8190-760abb11419e___t5-efficient-large-nh8-nl32,HF
5290,google/t5-efficient-tiny-nh1,Its original repository can be found here,tau/splinter-large-qass,0.0437522865831851,-1.0,NLP,4aebcff7-7ee7-49e9-82d8-d88b20407eff___t5-efficient-tiny-nh1,HF
5291,uer/chinese_roberta_L-6_H-128,"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,55c49535-79dd-4fa6-95a0-181e9cc00b64___chinese_roberta_L-6_H-128,HF
5292,GKLMIP/electra-myanmar-small-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,0.0304864533245563,-1.0,NLP,d5800f46-5bec-410c-8277-fb32757570cc___electra-myanmar-small-uncased,HF
5293,google/t5-efficient-large-nl20,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
5294,malay-huggingface/xlnet-large-bahasa-cased,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,7b13f615-ee89-4b00-9bc4-9cc5a2fcc645___xlnet-large-bahasa-cased,HF
5295,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm256,0.0481433893243471,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
5296,oskrmiguel/mt5-simplification-spanish,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0157647905871271,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
5297,piotr-rybak/poleval2021-task4-herbert-large-encoder,This repository provides an extra-small-sized Japanese GPT-2 model,rinna/japanese-gpt2-xsmall,0.0604934096336364,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
5298,google/t5-efficient-base-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,0.0560840293765067,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
5299,hfl/rbtl3,Model is based on GPT-J and has over 405M trainable parameters,Milos/slovak-gpt-j-405M,0.0356157291680574,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
5300,google/t5-efficient-base-nl32,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0598355727270245,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
5301,google/t5-efficient-large-nh8,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0508837923407554,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
5302,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,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,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
5303,nherve/flaubert-oral-asr,I have made no attempts to censor,bigjoedata/rockbot,0.0193068509300549,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
5304,obss/mt5-base-3task-highlight-tquad2,"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,0.0382970040664076,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
5305,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
5306,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"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,0.0707071796059608,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
5307,minimaxir/magic-the-gathering,"This means the Next sentence prediction is not used, as each sequence is treated as a complete document",Rostlab/prot_bert,0.080241311341524,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
5308,google/t5-efficient-small-kv256,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0585525594651698,-1.0,NLP,3b5d1b48-4bc4-420f-a130-a5d466a9805d___t5-efficient-small-kv256,HF
5309,negfir/distilbert-base-uncased-finetuned-squad,Amazon's BORT,amazon/bort,0.0878874063491821,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
5310,google/t5-efficient-small-ff12000,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.018979176878929,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
5311,sentence-transformers/paraphrase-MiniLM-L3-v2,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0721275955438613,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
5312,jnz/electra-ka,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0891807824373245,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
5313,google/t5-efficient-tiny-nl32,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0700652636587619,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
5314,aditeyabaral/sentencetransformer-distilbert-hinglish-big,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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
5315,TJMUCH/transcriptome-iseeek,"Tagalog version of DistilBERT, distilled from bert-tagalog-base-cased",jcblaise/distilbert-tagalog-base-cased,0.0644042268395423,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5316,mrp/simcse-model-wangchanberta,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0271852146834135,-1.0,NLP,a6baef44-a977-44c5-88ff-84d107553ef5___simcse-model-wangchanberta,HF
5317,google/t5-efficient-large-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,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
5318,google/t5-efficient-xxl-nl4,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0158868674188852,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
5319,IDEA-CCNL/Zhouwenwang-Unified-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,0.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
5320,gayanin/bart-mlm-pubmed-medterm,We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions,mldmm/GlassBERTa,0.0868655741214752,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
5321,saurkulsh/T0pp,"You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction",saurkulsh/T0pp,1.0,-1.0,NLP,7e1a99b0-50dc-4a20-8895-2a335f0801bf___T0pp,HF
5322,microsoft/layoutlm-base-cased,See make-pegasus-cnn_dailymail-tiny-random,stas/pegasus-cnn_dailymail-tiny-random,0.0859688371419906,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
5323,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5324,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"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,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
5325,google/t5-efficient-large-dm128,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0267382934689521,-1.0,NLP,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
5326,cammy/led-large-16384-arxiv-100-MDS,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0711548924446106,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
5327,mmaguero/gn-bert-tiny-cased,"A pre-trained BERT model for Guarani (2 layers, cased)",mmaguero/gn-bert-tiny-cased,1.0,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
5328,sentence-transformers/paraphrase-MiniLM-L3-v2,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,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
5329,google/t5-efficient-tiny-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,0.0643448382616042,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
5330,sberbank-ai/rugpt3large_based_on_gpt2,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.014006673824042,-1.0,NLP,3c2586e0-7c47-4ead-a41c-38dd9e303b32___rugpt3large_based_on_gpt2,HF
5331,flax-community/bengali-t5-base,This does not make a very readable text,pere/DeUnCaser,0.0362782469019293,-1.0,NLP,5828f3b7-1195-4fca-a792-eb280bf662a5___bengali-t5-base,HF
5332,DeepPavlov/distilrubert-tiny-cased-conversational,"Each function was sorted by the ID number and combined with its annotation description ie (is_a, enables, located_in, etc)",damlab/GO-language,0.0747444579998651,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
5333,hfl/rbt3,"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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5334,malay-huggingface/t5-super-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,0.007049726943175,-1.0,NLP,32efcbd0-5fe4-4536-b35a-10c4fa5d6745___t5-super-super-tiny-bahasa-cased,HF
5335,amazon/bort,"For futher evaluation results, see SBERT",cross-encoder/nli-deberta-v3-xsmall,0.0433642491698264,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
5336,Gilles/FongBERT,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0383230544626712,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
5337,google/t5-efficient-small-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,0.0828991122543811,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
5338,VietAI/gpt-neo-1.3B-vietnamese-news,The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository,prajjwal1/bert-tiny,0.0579925999045371,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
5339,hakurei/gpt-j-random-tinier,"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,0.0054230578243732,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
5340,DeepPavlov/distilrubert-tiny-cased-conversational,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.0375389428809284,-1.0,NLP,75250866-7865-4998-9090-363d3d029ec6___distilrubert-tiny-cased-conversational,HF
5341,google/t5-efficient-small-el12,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0705748796463012,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
5342,Gilles/FongBERT,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,1.0,-1.0,NLP,13368d80-efe7-4865-a10a-94a857b60ca2___FongBERT,HF
5343,SupriyaArun/squeezebert-uncased-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,0.0962191000580787,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
5344,obss/mt5-base-3task-highlight-tquad2,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.067653689533472,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
5345,google/t5-efficient-small-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,0.0236312244087457,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
5346,kyryl0s/gpt2-uk-xxs,"For usage instructions, refer to our codebase: https://github",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0485629681497811,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
5347,IsaacBot/t5-small-finetuned-qa-google-en-answer_v1,For details on the training approach see Web Appendix F in Hartmann et al,j-hartmann/purchase-intention-english-roberta-large,0.0461910925805568,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
5348,hfl/rbt3,"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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5349,aubmindlab/aragpt2-large,"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,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
5350,google/t5-efficient-xl-nl4,"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,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
5351,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
5352,madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,46bb543f-5aff-4c64-8807-2639c899d138___bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1,HF
5353,lannelin/bert-imdb-1hidden,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.0286684272189934,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
5354,bolbolzaban/gpt2-persian,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0268602911382912,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
5355,IDEA-CCNL/Erlangshen-Longformer-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,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
5356,google/t5-efficient-tiny-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,0.0675486549735068,-1.0,NLP,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
5357,hfl/chinese-electra-180g-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-180g-small-ex-generator,1.0,-1.0,NLP,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
5358,google/bert_uncased_L-6_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,ae5ab3bd-35c4-4623-8413-addf16c77129___bert_uncased_L-6_H-256_A-4,HF
5359,google/t5-efficient-tiny-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,0.0148319248110055,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
5360,cammy/led-base-16384-100-MDS,com/skplanet/Dialog-KoELECTRADialog-KoELECTRA is a language model specialized for dialogue,skplanet/dialog-koelectra-small-generator,0.0874745845794677,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
5361,VietAI/gpt-neo-1.3B-vietnamese-news,"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,0.0323082823306322,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
5362,tuner007/pegasus_qa,"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,0.0684236958622932,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
5363,piotr-rybak/poleval2021-task4-herbert-large-encoder,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0987681448459625,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
5364,google/t5-efficient-small-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,0.0856470689177513,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
5365,negfir/distilbert-base-uncased-finetuned-cola,"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,0.0977662503719329,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
5366,google/t5-efficient-small-kv128,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
5367,google/t5-11b-ssm-nqo,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0015364338954289,-1.0,NLP,58c3611f-ca00-4f5f-a566-9cf0e6ad73ae___t5-11b-ssm-nqo,HF
5368,stas/pegasus-cnn_dailymail-tiny-random,The architecture requires two separate models,enelpol/czywiesz-context,0.055566812865436,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
5369,pulp/CHILDES-ParentBERTo,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0743728466331958,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
5370,diper1998/distilgpt2-finetuned-AT,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.0765974372625351,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
5371,diper1998/distilgpt2-finetuned-AT,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0930544435977935,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
5372,IDEA-CCNL/Wenzhong-GPT2-3.5B,"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,0.0431334218010306,-1.0,NLP,2ab43ab1-a488-4a07-bf01-6b786eb403b6___Wenzhong-GPT2-3.5B,HF
5373,google/t5-efficient-base-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,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
5374,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5375,eli4s/Bert-L12-h384-A6,This does not make a very readable text,pere/DeUnCaser,0.0674380821486313,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
5376,google/t5-efficient-base-nl2,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0661613196134567,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
5377,google/t5-efficient-small-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,0.0319454278796911,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
5378,google/t5-efficient-base-ff6000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0447625247761606,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
5379,google/t5-efficient-base-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,0.0312498398125172,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
5380,google/t5-efficient-small-ff3000,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0141906077042221,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
5381,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
5382,stas/mt5-tiny-random,They can be fine-tuned in the same manner as the original BERT models,google/bert_uncased_L-8_H-768_A-12,0.0804335474967956,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5383,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
5384,google/t5-efficient-large-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,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
5385,google/t5-efficient-large-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,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
5386,google/t5-efficient-base-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,0.0550600718706845,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
5387,lgrobol/electra-minuscule-generator,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
5388,cosmoquester/bart-ko-small,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0067170386513074,-1.0,NLP,17ad871b-0fab-448a-818d-0d3853b35c8e___bart-ko-small,HF
5389,superspray/electra_large_discriminator_squad2_custom_dataset,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-3,0.0861160680651664,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
5390,lgrobol/electra-minuscule-discriminator,"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,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
5391,google/t5-efficient-small-nl48,"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-p1,0.0117375385016202,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
5392,khizon/distilbert-unreliable-news-eng-4L,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0553761925548314,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
5393,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 256",eli4s/Bert-L12-h256-A4,0.0739861913025378,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
5394,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-SMALL-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el8,0.0821502655744552,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
5395,oskrmiguel/mt5-simplification-spanish,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0,-1.0,NLP,3f88f46a-6231-479d-a0dc-5b3fb8a34293___mt5-simplification-spanish,HF
5396,1one5ome/gpt2-chinese-gulong,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0714621543884277,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
5397,nghuyong/ernie-tiny,Source language: English,NYTK/translation-marianmt-en-hu,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
5398,jkeruotis/LitBERTa-uncased,For best results use python as below,pszemraj/led-large-book-summary,0.0646472722291946,-1.0,NLP,bb22aa49-f5a0-4e62-aff7-88e6abc887a5___LitBERTa-uncased,HF
5399,kyryl0s/gpt2-uk-xxs,co/cointegrated/rubert-base-cased-nli-threeway,cointegrated/rubert-tiny-bilingual-nli,0.0558989169076084,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
5400,amazon/bort,Max_input_lngth is set as 25 and max_output_length is 10,deep-learning-analytics/triviaqa-t5-base,0.0,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
5401,sultan/BioM-ELECTRA-Large-Generator,"In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices",sultan/BioM-ELECTRA-Large-Generator,1.0,-1.0,NLP,a3c5f945-b250-488d-a127-1af159180996___BioM-ELECTRA-Large-Generator,HF
5402,google/t5-efficient-large-kv256,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0420485828071832,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
5403,thu-coai/LongLM-large,T5-Efficient-TINY-NH1 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh1,0.0477363429963588,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
5404,google/bert_uncased_L-4_H-128_A-2,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0481574097648262,-1.0,NLP,f05ec369-a128-4b87-9d31-7b9305888877___bert_uncased_L-4_H-128_A-2,HF
5405,google/t5-efficient-large-kv32,Its original repository can be found here,tau/splinter-large-qass,0.0777593757957219,-1.0,NLP,d886cb2c-35ed-4427-b9a7-872921421473___t5-efficient-large-kv32,HF
5406,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0537015460431575,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
5407,TJMUCH/transcriptome-iseeek,Details will be available soon,VietAI/gpt-neo-1.3B-vietnamese-news,0.0015345178544521,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5408,google/reformer-crime-and-punishment,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
5409,google/t5-efficient-base-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,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
5410,google/t5-efficient-large-nh2,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.073716240003705,-1.0,NLP,6e27b644-7be6-428a-b40c-eebbcb658315___t5-efficient-large-nh2,HF
5411,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,1.0,-1.0,NLP,25d802f0-693f-40ed-a257-625a599a52e3___SGPT-125M-weightedmean-nli-bitfit,HF
5412,nghuyong/ernie-tiny,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,0.0,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
5413,google/t5-efficient-large-nh24,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0333327455446123,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
5414,fnlp/bart-base-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,0.003027020022273,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
5415,google/t5-efficient-tiny-nl6,"OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)",tau/tavbert-he,0.0473319515585899,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
5416,obss/mt5-base-3task-highlight-tquad2,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0747749917209148,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
5417,google/t5-efficient-large-kv128,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0697662774473428,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
5418,google/t5-efficient-tiny-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,0.0680960062891244,-1.0,NLP,4e692f36-e4e2-4528-b6c3-0939b50527cb___t5-efficient-tiny-nh32,HF
5419,google/t5-efficient-small-ff3000,"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,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
5420,hakurei/gpt-j-random-tinier,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0496388729661702,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
5421,google/t5-efficient-tiny-nh8,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0744452085345983,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
5422,google/t5-efficient-base-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,0.0351992547512054,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
5423,hajime9652/xlnet-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,0.0792799741029739,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
5424,rdenadai/BR_BERTo,"BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences",google/bigbird-roberta-large,0.0364927478755513,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
5425,brad1141/Longformer-finetuned-norm,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0541290678083896,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
5426,Parsa/BBB_prediction_classification_SMILES,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,255ff8bb-d6a6-4d13-b043-a1ded112c6ee___BBB_prediction_classification_SMILES,HF
5427,google/t5-efficient-small-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,0.0136119462549686,-1.0,NLP,6334814e-ec0a-43de-8ade-6ded3fdbbed3___t5-efficient-small-el4,HF
5428,gayanin/bart-mlm-pubmed-medterm,"In addition, it also provides the pooled states of the last transformer layer",audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0196012798696756,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
5429,google/t5-efficient-tiny-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,0.0595055762678384,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
5430,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,"For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking",hfl/rbtl3,0.0918302908539772,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
5431,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,"Efficiency of tasks such as Alloy Property Prediction, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task",mldmm/GlassBERTa,0.0610772259533405,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
5432,bookbot/distil-wav2vec2-adult-child-cls-52m,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,bf96ea1f-73b8-4190-be16-4908c29935a6___distil-wav2vec2-adult-child-cls-52m,HF
5433,suzuki/distilbert-base-uncased-finetuned-squad,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0645130649209022,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
5434,microsoft/layoutlm-large-uncased,This BERT model was trained using the Megatron-LM library,KBLab/megatron-bert-large-swedish-cased-165k,0.0544226467609405,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
5435,minimaxir/magic-the-gathering,"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,0.0012519769370555,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
5436,thu-coai/LongLM-large,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.0288623549665013,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
5437,google/t5-efficient-small-ff1000,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0434355288743972,-1.0,NLP,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
5438,lassl/roberta-ko-small,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
5439,KoichiYasuoka/roberta-base-ukrainian,The model contains 32.1% of the original weights overall,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,0.0512800440192222,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
5440,google/t5-efficient-small-el12,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.012533739209175,-1.0,NLP,5e591f0b-170c-46ee-90e3-63243411e266___t5-efficient-small-el12,HF
5441,google/t5-efficient-xl-nl6,"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,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
5442,google/t5-efficient-small-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-v1,0.062160707078874,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
5443,ebagdasa/propaganda_positive_bart_large,"This model is based on the paper ""Spinning Language Models for Propaganda-As-A-Service""  and trained using this GitHub repo",ebagdasa/propaganda_positive_bart_large,1.0,-1.0,NLP,053ec520-9f70-466f-9752-ab906925113e___propaganda_positive_bart_large,HF
5444,google/t5-efficient-base-nl48,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0732604283839463,-1.0,NLP,3c57a7b2-d7c2-49dd-9733-2a8d1b6aead7___t5-efficient-base-nl48,HF
5445,1one5ome/gpt2-chinese-gulong,It should be used in conjunction with google/pegasus-billsum,andrejmiscic/simcls-scorer-billsum,0.0,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
5446,tartuNLP/gpt-4-est-base,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0465243123471736,-1.0,NLP,1fcf9b96-f0a6-44b5-a4bf-f675ab0fb36b___gpt-4-est-base,HF
5447,cammy/led-large-16384-arxiv-100-MDS,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0274173840880393,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
5448,persiannlp/mt5-large-parsinlu-opus-translation_fa_en,"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,0.0415179971605539,-1.0,NLP,f90151ed-b36d-48ad-a401-5c02a4f5f752___mt5-large-parsinlu-opus-translation_fa_en,HF
5449,stas/pegasus-cnn_dailymail-tiny-random,"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,0.0874092541635036,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
5450,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Google's mT5-small fine-tuned on TyDi QA (secondary task) for multingual QandA downstream task,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,1.0,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
5451,flax-community/RoBERTa-large-finnish,Pretrained model on Finnish language using a masked language modeling (MLM) objective,flax-community/RoBERTa-large-finnish,1.0,-1.0,NLP,bd6a99f8-f445-4aab-803e-729051effeb2___RoBERTa-large-finnish,HF
5452,tartuNLP/gpt-4-est-large,"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,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
5453,groar/gpt-neo-1.3B-finetuned-escape2,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0260606948286294,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
5454,mmaguero/gn-bert-tiny-cased,Deprecation NoticeThis model is deprecated,jcblaise/distilbert-tagalog-base-cased,0.0598655929788947,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
5455,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
5456,piotr-rybak/poleval2021-task4-herbert-large-encoder,org/abs/2202.08904The model was trained with the parameters:,Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0909300595521926,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
5457,mwesner/reformer-clm,T5-Efficient-BASE-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-ff6000,0.0572168789803981,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
5458,stas/pegasus-cnn_dailymail-tiny-random,py (Download Here) that define the architecture of CPT into your project,fnlp/cpt-base,0.0677051432430743,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
5459,aubmindlab/aragpt2-large,Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English,google/reformer-crime-and-punishment,0.015880949795246,-1.0,NLP,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
5460,google/t5-efficient-xl-nl6,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0387951508164405,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
5461,IlyaGusev/rugpt3medium_sum_gazeta,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0779260024428367,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
5462,KoichiYasuoka/roberta-base-ukrainian,The hold-out accuracy is 95% (vs a balanced 50% random-chance baseline),j-hartmann/purchase-intention-english-roberta-large,0.075291022658348,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
5463,IDEA-CCNL/Erlangshen-Longformer-110M,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,0.0326539110392331,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
5464,IDEA-CCNL/Zhouwenwang-Unified-110M,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,6d37c7b8-837d-41df-ac64-072a9be1fc7a___Zhouwenwang-Unified-110M,HF
5465,MoritzLaurer/MiniLM-L6-mnli,This does not make a very readable text,pere/DeUnCaser,0.0442957089593013,-1.0,NLP,9e6c6f2a-bdaf-49b9-b34c-30ffed8fa8ea___MiniLM-L6-mnli,HF
5466,google/t5-efficient-base-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,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
5467,google/t5-efficient-base-el8,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0608908329159021,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
5468,IsaacBot/t5-small-finetuned-qa-google-en-answer_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,0.0447370298206806,-1.0,NLP,3f0d7af9-f3d2-45b5-9895-e3b7f4c5822e___t5-small-finetuned-qa-google-en-answer_v1,HF
5469,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0221370961517095,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
5470,google/t5-efficient-tiny-ff3000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,a5ffd620-9e7c-4d54-a20c-451320625d5d___t5-efficient-tiny-ff3000,HF
5471,google/t5-efficient-base-ff6000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0275496249087154,-1.0,NLP,995ef374-8160-41ef-a05f-e73e770ddac2___t5-efficient-base-ff6000,HF
5472,tau/tavbert-he,T5-Efficient-LARGE-EL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el8,0.0445876754820346,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
5473,google/t5-efficient-small-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,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
5474,google/t5-efficient-small-ff3000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0360540756955742,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
5475,bolbolzaban/gpt2-persian,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0675779804587363,-1.0,NLP,bc26a8b0-9f31-46df-9023-d936bbf03b2c___gpt2-persian,HF
5476,google/t5-efficient-large-el6,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0709732808172702,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
5477,abdusahmbzuai/aradia-ctc-distilhubert-ft,Note that tokenizer package is BertTokenizer not AutoTokenizer,jhu-clsp/bibert-ende,0.0630822330713272,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
5478,nghuyong/ernie-1.0,"ERNIE (Enhanced Representation through kNowledge IntEgration) is proposed by Baidu in 2019, which is designed to learn language representation enhanced by knowledge masking strategies i",nghuyong/ernie-1.0,1.0,-1.0,NLP,fef0db55-5dfa-4849-aaea-d2700d6a4e4e___ernie-1.0,HF
5479,uer/t5-v1_1-small-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,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
5480,google/t5-efficient-large-el6,"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,0.0425698263570666,-1.0,NLP,1c3f0ccc-45a1-4493-b548-b1878d7b50c8___t5-efficient-large-el6,HF
5481,tucan9389/kcbert-base-finetuned-squad,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0945234894752502,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
5482,tuner007/pegasus_qa,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.053161296993494,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
5483,DemangeJeremy/4-sentiments-with-flaubert,"We introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads)",saibo/random-roberta-tiny,0.0553956516087055,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5484,google/t5-efficient-large-nl10,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0364965284243225,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
5485,abdusahmbzuai/aradia-ctc-distilhubert-ft,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0721194073557853,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
5486,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
5487,google/t5-efficient-small-ff12000,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0333374524489044,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
5488,google/t5-efficient-small-ff6000,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0532228276133536,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
5489,piotr-rybak/poleval2021-task4-herbert-large-encoder,"In comparison to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight",Recognai/zeroshot_selectra_small,0.0710721164941787,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
5490,VietAI/gpt-neo-1.3B-vietnamese-news,This model was built as a way to encode the Gene Ontology definition of a protein as vector representation,damlab/GO-language,0.0365671110339462,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
5491,mrm8488/bert-small-finetuned-squadv2,"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,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
5492,jhu-clsp/bibert-ende,"Mode size (after training): 109.74 MBReleased on March 11th, 2020",mrm8488/bert-small-finetuned-squadv2,0.0388766281927624,-1.0,NLP,520db755-a3c8-45e9-b8d3-2ccadcf54d0e___bibert-ende,HF
5493,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,This repository provides an extra-small-sized Japanese GPT-2 model,rinna/japanese-gpt2-xsmall,0.060536578297615,-1.0,NLP,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
5494,superspray/electra_large_discriminator_squad2_custom_dataset,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0257503632456064,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
5495,google/t5-efficient-small-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,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
5496,microsoft/layoutlm-base-cased,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,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
5497,mimicheng/codeparrot-ds,"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-12,0.0696926116943359,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
5498,google/t5-efficient-xl-nl2,T5-Efficient-XL-NL2 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl2,1.0,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
5499,microsoft/layoutlm-base-cased,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0986369401216507,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
5500,google/t5-efficient-base-dm1000,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.087347038090229,-1.0,NLP,af8bae3e-2561-4145-99ef-74c6ac933f00___t5-efficient-base-dm1000,HF
5501,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
5502,TJMUCH/transcriptome-iseeek,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0652571395039558,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5503,google/t5-efficient-small-dm128,Its original repository can be found here,tau/splinter-large-qass,0.0464943572878837,-1.0,NLP,cfa186ea-87ac-4ffc-ab8c-f48787a645c0___t5-efficient-small-dm128,HF
5504,aditeyabaral/sentencetransformer-distilbert-hinglish-big,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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
5505,tuner007/pegasus_qa,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,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
5506,liaad/srl-pt_bertimbau-large,Its original repository can be found here,tau/splinter-large-qass,0.0014363378286361,-1.0,NLP,d4f40e10-cc05-4197-8024-db4ef0823491___srl-pt_bertimbau-large,HF
5507,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,"In addition, it also provides the pooled states of the last transformer layer",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
5508,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0084523316472769,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
5509,tartuNLP/gpt-4-est-large,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
5510,celine98/canine-s-finetuned-sst2,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0921730771660804,-1.0,NLP,176a9891-8ded-4444-9caa-02571251303a___canine-s-finetuned-sst2,HF
5511,google/t5-efficient-large-nh24,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
5512,google/t5-efficient-large-kv128,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0234263390302657,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
5513,mimicheng/codeparrot-ds,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0816180109977722,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
5514,google/t5-efficient-base-nl40,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.009203233756125,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
5515,cammy/led-large-16384-arxiv-100-MDS,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0066698864102363,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
5516,microsoft/layoutlm-base-cased,It can be considered as small copy of Conversational DistilRuBERT-base,DeepPavlov/distilrubert-tiny-cased-conversational,0.0654938891530036,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
5517,google/t5-efficient-small-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-p1,0.0196103770285844,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
5518,google/t5-efficient-tiny-nh16,T5-Efficient-TINY-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh16,1.0,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
5519,aditeyabaral/sentencetransformer-bert-hinglish-small,It was introduced in this paper and first released on this page,THUMT/mGPT,0.0447854250669479,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
5520,Palak/microsoft_deberta-base_squad,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
5521,dhpollack/distilbert-dummy-sentiment,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,9033ffb3-d6dc-44f9-a4b4-d084be4eb35d___distilbert-dummy-sentiment,HF
5522,hf-internal-testing/tiny-random-rembert,This is an implementation of Chinese BART-Large,fnlp/bart-large-chinese,0.0827676281332969,-1.0,NLP,0d826333-3e77-402e-a547-deee3534df14___tiny-random-rembert,HF
5523,google/t5-efficient-base-nh24,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,0.0608118120580911,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
5524,google/t5-efficient-small-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,0.0515831001102924,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
5525,NeuML/bert-small-cord19,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0341927635793884,-1.0,NLP,ae87b51b-249f-4c88-8bcd-e62030a1160e___bert-small-cord19,HF
5526,google/t5-efficient-large-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,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
5527,google/t5-efficient-xl-nl2,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
5528,google/t5-efficient-base-nl32,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.06266775354743,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
5529,Kevincp560/bigbird-pegasus-large-bigpatent-finetuned-pubMed,This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named envibert,nguyenvulebinh/envibert,0.090788722038269,-1.0,NLP,0f53ca2d-ef6d-4b9f-8fc4-6ab07670cf54___bigbird-pegasus-large-bigpatent-finetuned-pubMed,HF
5530,IlyaGusev/rugpt3medium_sum_gazeta,"THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT",lgrobol/electra-minuscule-generator,0.0607631690800189,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
5531,thu-coai/LongLM-large,T5-Efficient-BASE-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-nh16,0.0485921514530976,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
5532,sonoisa/byt5-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,0.0814266353845596,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
5533,pulp/CHILDES-ParentBERTo,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0401734467595815,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
5534,google/t5-efficient-large-el4,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0817299336194991,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
5535,Milos/slovak-gpt-j-405M,"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,0.0144079718738794,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
5536,clue/roberta_chinese_3L312_clue_tiny,For more information refer to SUPERB: Speech processing Universal PERformance Benchmark,superb/hubert-large-superb-sid,0.0686734057962893,-1.0,NLP,ef7ebe56-c304-49c1-ad25-9d09b4e250eb___roberta_chinese_3L312_clue_tiny,HF
5537,piotr-rybak/poleval2021-task4-herbert-large-encoder,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0372504815459251,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
5538,SetFit/deberta-v3-large__sst2__train-8-8,"Instead, RemBERT uses small input embeddings and larger output embeddings",google/rembert,0.0336839407682418,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
5539,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm512,0.04761303961277,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
5540,stas/t5-very-small-random,"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,0.0250979401171207,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
5541,ian0delond/model-test,"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,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
5542,google/t5-efficient-small-kv32,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0216426867991685,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
5543,google/t5-efficient-xl-nl28,"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-nl28,1.0,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
5544,KBLab/megatron-bert-large-swedish-cased-165k,The hyperparameters for training followed the setting for RoBERTa,KBLab/megatron-bert-large-swedish-cased-165k,1.0,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
5545,jonatasgrosman/wav2vec2-xls-r-1b-english,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,3fb0e859-c51e-40f2-888c-82e019ae679e___wav2vec2-xls-r-1b-english,HF
5546,google/t5-efficient-large-nl16,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.0627996698021888,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
5547,fnlp/bart-base-chinese,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0178030543029307,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
5548,aditeyabaral/sentencetransformer-distilbert-hinglish-big,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0525723025202751,-1.0,NLP,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
5549,google/t5-efficient-base-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-v1,0.0611006394028663,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
5550,zoha/wav2vec2-base-timit-demo-colab,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0799545869231224,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
5551,eli4s/Bert-L12-h384-A6,"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,0.0581325180828571,-1.0,NLP,f84c41ef-37f6-47f1-a154-4f17fd432efe___Bert-L12-h384-A6,HF
5552,DTAI-KULeuven/robbertje-1-gb-bort,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0142970258990923,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
5553,rdenadai/BR_BERTo,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.011705210742851,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
5554,akdeniz27/deberta-v2-xlarge-cuad,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0484093502163887,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
5555,google/t5-efficient-xl-nl16,"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,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
5556,facebook/blenderbot-3B,Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements,facebook/blenderbot-3B,1.0,-1.0,NLP,5fc10922-2210-4617-a69d-6f56eef4de85___blenderbot-3B,HF
5557,mimi/Waynehills-NLP-doogie,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
5558,thu-coai/LongLM-large,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0407800336057941,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
5559,google/t5-efficient-tiny-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,0.0147773409262299,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
5560,google/t5-efficient-small-dm1000,Its original repository can be found here,tau/splinter-large-qass,0.0479302871972322,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
5561,google/t5-efficient-mini-nl6,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0188021170906722,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
5562,google/t5-efficient-xl-nl6,Boris was trained on around 78B tokens of French text from the C4 dataset,Cedille/fr-boris,0.0896127521991729,-1.0,NLP,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
5563,tartuNLP/gpt-4-est-large,This does not make a very readable text,pere/DeUnCaser,0.0836125165224075,-1.0,NLP,715f8d11-d729-4958-9ba2-4a91e29859b6___gpt-4-est-large,HF
5564,TJMUCH/transcriptome-iseeek,Further details are given in the associated paper,audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,0.0168533083051443,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5565,aubmindlab/aragpt2-large,"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,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
5566,google/t5-efficient-small-dm256,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0167462043464184,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
5567,superspray/electra_large_discriminator_squad2_custom_dataset,"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,0.0761221051216125,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
5568,qarib/bert-base-qarib_far_6500k,New Filipino Transformer models trained with a much larger corpora are available,jcblaise/distilbert-tagalog-base-cased,0.0285154073499143,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
5569,SetFit/deberta-v3-large__sst2__train-8-8,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0160271041095256,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
5570,google/t5-efficient-large-el2,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0006490387022495,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
5571,castorini/afriberta_base,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0294493986293673,-1.0,NLP,f0450512-0f4a-44b3-9b43-eb25b1c23c6b___afriberta_base,HF
5572,google/t5-efficient-base-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,0.0174854174256324,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
5573,google/t5-efficient-small-nl24,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0356436725705861,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
5574,stas/pegasus-cnn_dailymail-tiny-random,"This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication)",inokufu/flaubert-base-uncased-xnli-sts,0.0033350586891173,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
5575,jean-paul/kinyaRoberta-small,A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective,jean-paul/kinyaRoberta-small,1.0,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
5576,Palak/microsoft_deberta-base_squad,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0976315736770629,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
5577,pulp/CHILDES-ParentBERTo,T5-Efficient-MINI is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini,0.0770460441708563,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
5578,google/t5-efficient-small-dm1000,"To translate into a target language, the target language id is forced as the first generated token",facebook/wmt21-dense-24-wide-en-x,0.0882857479155063,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
5579,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-BASE-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm1000,0.0393770188093185,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
5580,ian0delond/model-test,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0614139400422573,-1.0,NLP,0aa5e13a-286c-49d3-84b6-adfd17e45fde___model-test,HF
5581,akdeniz27/deberta-v2-xlarge-cuad,Trained on Wikipedia + Wiktionary (~800K tokens),mmaguero/gn-bert-tiny-cased,0.0525534264743328,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
5582,negfir/distilbert-base-uncased-finetuned-cola,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0782267302274704,-1.0,NLP,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
5583,minimaxir/magic-the-gathering,"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-v2,0.0263777580112218,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
5584,google/t5-efficient-base-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,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
5585,zoha/wav2vec2-base-timit-demo-colab,"We modify the original position code of longformer to rotational position coding, and use 180G of data to pretraining",IDEA-CCNL/Erlangshen-Longformer-330M,0.0859317556023597,-1.0,NLP,65caa48e-16dd-4e5b-ab37-dd21f50fb5de___wav2vec2-base-timit-demo-colab,HF
5586,google/t5-efficient-base-nh16,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
5587,google/reformer-crime-and-punishment,ipynb and the weights were converted to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead,google/reformer-crime-and-punishment,1.0,-1.0,NLP,523309e0-974e-4dfe-9b9d-235f1cdf072c___reformer-crime-and-punishment,HF
5588,google/t5-efficient-small-nl24,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0394306052476167,-1.0,NLP,0266ea57-469b-40fc-979d-c1af890b2613___t5-efficient-small-nl24,HF
5589,lgrobol/electra-minuscule-discriminator,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0397638380527496,-1.0,NLP,a2bdef77-174e-4414-9bb6-8844bf39e3a5___electra-minuscule-discriminator,HF
5590,google/t5-efficient-large-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,0.0435424763709306,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
5591,cross-encoder/stsb-TinyBERT-L-4,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0350577589124441,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
5592,SupriyaArun/squeezebert-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,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
5593,flaubert/flaubert_small_cased,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,1.0,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
5594,google/t5-efficient-large-nh4,"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,0.0517251174896955,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
5595,google/t5-efficient-large-nl32,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0201247045770287,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
5596,google/t5-efficient-mini-nl6,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0417683813720941,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
5597,rinna/japanese-gpt-1b,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0290306583046913,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
5598,Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0443749640669141,-1.0,NLP,00bfba23-ce5c-4c77-8965-23c420ddf13a___opus-mt-tc-big-en-cat_oci_spa,HF
5599,google/t5-efficient-small-kv32,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0504733454436063,-1.0,NLP,75eeabf2-6fcc-492b-96e4-665d67ff46f2___t5-efficient-small-kv32,HF
5600,google/t5-efficient-small-nl48,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0763562694191932,-1.0,NLP,2f1620c6-312b-4cfa-bea1-ae31617ada20___t5-efficient-small-nl48,HF
5601,google/t5-efficient-base-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,54ff24e8-b845-4f93-a415-648375a78cf2___t5-efficient-base-dm2000,HF
5602,IDEA-CCNL/Erlangshen-Longformer-110M,The model achieves the following results on evaluation:,bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0457864198833703,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
5603,google/t5-efficient-xl-nl6,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,2dbfc268-2e3f-48bf-8f7f-2d5ab9d65ef3___t5-efficient-xl-nl6,HF
5604,suzuki/distilbert-base-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-12,0.0918863862752914,-1.0,NLP,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
5605,google/t5-efficient-small-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,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
5606,google/t5-efficient-small-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,99a40ebe-31b3-46ad-9095-b00ffc91b830___t5-efficient-small-ff1000,HF
5607,flaubert/flaubert_small_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,0.0381247313427073,-1.0,NLP,d7fd2cc9-0fb7-4a5e-a86e-291cc87d2c9c___flaubert_small_cased,HF
5608,google/t5-efficient-large-nl32,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0683980584144591,-1.0,NLP,6a87ea06-736d-45fa-afa5-abdb0b1bfa03___t5-efficient-large-nl32,HF
5609,abdusahmbzuai/ft-tatoeba-ar-en,T5-Efficient-LARGE-NH32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh32,0.0846595764160156,-1.0,NLP,9235d6b5-e6b2-419f-bd70-fa7a4acfc859___ft-tatoeba-ar-en,HF
5610,tuner007/pegasus_qa,ByteLevelBPETokenizer was trained on the same Slovak corpus,Milos/slovak-gpt-j-1.4B,0.0732114017009735,-1.0,NLP,b486c044-cc4b-4ae8-aa7e-ab04b5e27dbf___pegasus_qa,HF
5611,mmaguero/gn-bert-tiny-cased,T5-Efficient-BASE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv32,0.0693259388208389,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
5612,jweb/japanese-soseki-gpt2-1b,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0272260159254074,-1.0,NLP,22aa4eae-2736-4a1a-a2d7-1b032f6fdef3___japanese-soseki-gpt2-1b,HF
5613,allenai/macaw-3b,Since then a larger Slovak GPT-J-1.4B was released,Milos/slovak-gpt-j-405M,0.0153772893051306,-1.0,NLP,b59f657a-febc-4cb6-aa37-c17d67ac3952___macaw-3b,HF
5614,google/t5-efficient-large-el4,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0346163176000118,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
5615,Vilnius-Lithuania-iGEM/Albumin,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0657778168097138,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
5616,google/t5-efficient-xxl-nl4,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0884118974208831,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
5617,jnz/electra-ka,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,0.0971842259168624,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
5618,lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,76165001-c726-434f-ad4c-7cc52276d33f___MiniLMv2-L12-H384-distilled-finetuned-clinc,HF
5619,sonoisa/byt5-small-japanese,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0248351525515317,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
5620,google/t5-efficient-small-nl20,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.072615411132574,-1.0,NLP,9e7606e7-72dc-4bf3-bd0b-505412d68905___t5-efficient-small-nl20,HF
5621,lannelin/bert-imdb-1hidden,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0669365723927815,-1.0,NLP,510ca0bd-15cf-42b8-b622-8b9d6bd0c4ec___bert-imdb-1hidden,HF
5622,google/t5-efficient-large-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,faf6f73a-8556-4cd8-95da-e1f395afc9d8___t5-efficient-large-dm128,HF
5623,superspray/electra_large_discriminator_squad2_custom_dataset,"For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS",qarib/bert-base-qarib_far_6500k,0.0654723644256591,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
5624,google/t5-efficient-base-nh8,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
5625,google/t5-efficient-large-el8,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0789459347724914,-1.0,NLP,8224759e-e981-49dd-8eab-7a96385ef636___t5-efficient-large-el8,HF
5626,piotr-rybak/poleval2021-task4-herbert-large-encoder,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,0.0500911027193069,-1.0,NLP,cd9c349e-1947-4679-addc-d140619f14a7___poleval2021-task4-herbert-large-encoder,HF
5627,google/t5-efficient-base-nl32,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0328370975330471,-1.0,NLP,0779c9a7-fd45-496c-afd1-c885964b7e59___t5-efficient-base-nl32,HF
5628,IDEA-CCNL/Erlangshen-Longformer-110M,This is a multilingual model,ErykWdowiak/GPTalian,0.0717513933777808,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
5629,sonoisa/byt5-small-japanese,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0471582412719726,-1.0,NLP,fd8dee89-8326-4831-a082-d220c0c7d69b___byt5-small-japanese,HF
5630,malay-huggingface/bert-tiny-bahasa-cased,I have made no attempts to censor,bigjoedata/rockbot,0.0626074050863583,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
5631,lassl/roberta-ko-small,Pretrained roberta-ko-small on korean language was trained by LASSL framework,lassl/roberta-ko-small,1.0,-1.0,NLP,ad1dd186-05f8-434b-aede-6635717be963___roberta-ko-small,HF
5632,google/t5-efficient-large-el4,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0770040079951286,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
5633,google/t5-efficient-base-nh8,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,64f4de66-cd88-4a22-8582-4c35413dc033___t5-efficient-base-nh8,HF
5634,google/t5-efficient-large-nh12,"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,0.0560233127325772,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
5635,hfl/chinese-xlnet-mid,"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,0.0428629430631796,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
5636,google/t5-efficient-large-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,0.0036065019667148,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
5637,mimi/Waynehills-NLP-doogie,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0173485707491636,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
5638,google/t5-efficient-large-dm512,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0098234731703996,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
5639,tau/tavbert-he,Amazon's BORT,amazon/bort,0.0761662982404231,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
5640,madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0191957751909891,-1.0,NLP,f64bb26f-a0da-40ab-901d-24445fe908c6___bert-base-uncased-squad1.1-block-sparse-0.13-v1,HF
5641,google/t5-efficient-xl-nl2,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,89bc001d-1fd3-4fe8-9bd1-758790defea5___t5-efficient-xl-nl2,HF
5642,IDEA-CCNL/Erlangshen-Longformer-110M,See Google BERT for details,tobiaslee/bert-2l-768h-uncased,0.062929892912507,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
5643,google/t5-efficient-tiny-nl6,This does not make a very readable text,pere/DeUnCaser,0.0732805281877517,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
5644,patrickvonplaten/unispeech-sat-large-timit-ft,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0241727046668529,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
5645,cosmoquester/bart-ko-mini,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,657b66d1-fa83-4647-b400-0b4dc96707d7___bart-ko-mini,HF
5646,cammy/led-base-16384-100-MDS,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0714973956346511,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
5647,stas/t5-very-small-random,Subword tokenization was done with WordPiece,ptaszynski/yacis-electra-small-japanese,0.0026843734085559,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
5648,DemangeJeremy/4-sentiments-with-flaubert,"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,0.0814369693398475,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5649,facebook/m2m100-12B-avg-10-ckpt,"To translate into a target language, the target language id is forced as the first generated token",facebook/m2m100-12B-avg-10-ckpt,1.0,-1.0,NLP,20d108d1-104c-4554-b017-af8689ca81db___m2m100-12B-avg-10-ckpt,HF
5650,DemangeJeremy/4-sentiments-with-flaubert,Note: this model is the ConvBERT generator model intented to be used for the fill-mask task,Finnish-NLP/convbert-base-generator-finnish,0.0805171616375446,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5651,negfir/distilbert-base-uncased-finetuned-cola,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,934e2d7b-f36c-4e37-9c8f-d0c46e34a3af___distilbert-base-uncased-finetuned-cola,HF
5652,google/t5-efficient-tiny-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,0.0614863168448209,-1.0,NLP,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
5653,saibo/random-roberta-tiny,There is a description of the REST API of our service,NYTK/translation-marianmt-en-hu,0.0009575029835105,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
5654,malay-huggingface/xlnet-base-bahasa-cased,Pretrained XLNET base language model for Malay,malay-huggingface/xlnet-base-bahasa-cased,1.0,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
5655,jean-paul/kinyaRoberta-small,"However, due to some resource computation issues, we kept the number of transformer layers to 6",jean-paul/kinyaRoberta-small,1.0,-1.0,NLP,5c4793ac-45c0-4c06-b384-dece42d3bd5e___kinyaRoberta-small,HF
5656,google/t5-efficient-base-ff12000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0203937739133834,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
5657,nherve/flaubert-oral-asr,"Then, it was compressed via progressive module replacing",rmihaylov/bert-base-pos-theseus-bg,0.047881968319416,-1.0,NLP,47d7480a-12aa-4824-bca8-e0bf655109a8___flaubert-oral-asr,HF
5658,allenai/wmt16-en-de-12-1,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,51bc9ce7-2fa4-4544-9486-3666caea6d4d___wmt16-en-de-12-1,HF
5659,pulp/CHILDES-ParentBERTo,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0746493153274059,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
5660,google/t5-efficient-small-dm1000,The output from Automated Speak Recognition software is usually uncased and without any punctation,pere/DeUnCaser,0.0145432967692613,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
5661,suzuki/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,38643a8c-deed-400b-8bc1-8a411923600b___distilbert-base-uncased-finetuned-squad,HF
5662,IDEA-CCNL/Erlangshen-Longformer-330M,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.0,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
5663,google/t5-efficient-xl-nl28,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0772219188511371,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
5664,yarongef/DistilProtBert,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,1.0,-1.0,NLP,1809a932-503a-4498-8797-47c7dd7b3852___DistilProtBert,HF
5665,google/t5-efficient-large-el2,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0429605431854724,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
5666,google/t5-efficient-base-ff12000,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0455409279093146,-1.0,NLP,f505864d-f594-4f56-9861-3ef7f9fe455c___t5-efficient-base-ff12000,HF
5667,IDEA-CCNL/Zhouwenwang-Unified-1.3B,"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,1.0,-1.0,NLP,0c852bdb-8cae-4518-8dfe-81d5f3d29945___Zhouwenwang-Unified-1.3B,HF
5668,mrm8488/xlm-multi-finetuned-xquadv1,Note that we present the statistics after the cleaning process,readerbench/RoBERT-small,0.0282525699585676,-1.0,NLP,20993b52-f6bc-43f7-9b78-7eece4dd406d___xlm-multi-finetuned-xquadv1,HF
5669,Recognai/selectra_medium,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0284171663224696,-1.0,NLP,a81e59ac-d1cf-41c9-b8cc-f5807a8a267f___selectra_medium,HF
5670,DemangeJeremy/4-sentiments-with-flaubert,The model is tuned to search for the answer in its memory to return it,deep-learning-analytics/triviaqa-t5-base,0.0080350199714303,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5671,cammy/led-base-16384-100-MDS,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0177216436713933,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
5672,google/t5-efficient-tiny-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,2bef8d70-fd2d-4606-914a-bfdbe11ad37a___t5-efficient-tiny-ff9000,HF
5673,google/bert_uncased_L-10_H-256_A-4,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0170423748592535,-1.0,NLP,d8932361-0722-42d2-b266-6c542f7e5632___bert_uncased_L-10_H-256_A-4,HF
5674,google/t5-efficient-base-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-base-kv256,1.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
5675,google/t5-efficient-base-kv32,T5-Efficient-BASE-KV32 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv32,1.0,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
5676,google/t5-efficient-tiny-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,ef57c359-1c85-4211-a85b-8ed5aa5cb500___t5-efficient-tiny-nl24,HF
5677,google/t5-efficient-large-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,0.0558743588626384,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
5678,rahulacj/bertweet-base-finetuned-sentiment-analysis,"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,0.0014878802467137,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
5679,DTAI-KULeuven/robbertje-1-gb-bort,The dataset is split such that there was no overlap in of news sources between the three sets,khizon/distilbert-unreliable-news-eng-4L,0.0381072927266358,-1.0,NLP,3075b916-1348-44d1-8183-0243f2305232___robbertje-1-gb-bort,HF
5680,google/t5-efficient-base-nl40,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0197347626090049,-1.0,NLP,74ffa117-afce-485f-b95a-a760fb78b16c___t5-efficient-base-nl40,HF
5681,google/t5-efficient-small-nl40,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0754588022828101,-1.0,NLP,a5d83e89-e9d1-4ec8-8a43-71cc2f3c06ee___t5-efficient-small-nl40,HF
5682,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-TINY-NH16 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-nh16,0.0135672576725483,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5683,Palak/microsoft_deberta-base_squad,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0495878234505653,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
5684,google/t5-efficient-small-ff12000,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0404613763093947,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
5685,tucan9389/kcbert-base-finetuned-squad,It was introduced in this paper,readerbench/RoBERT-small,0.0909347534179687,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
5686,google/t5-efficient-xl-nl4,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0883514583110809,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
5687,google/t5-efficient-base-el16,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0512784756720065,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
5688,mimicheng/codeparrot-ds,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0149293392896652,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
5689,jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,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,be694b3e-5f07-49e0-994b-9079ecb07851___Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs,HF
5690,google/t5-efficient-base-nl24,Its original repository can be found here,tau/splinter-large-qass,0.0673080962151288,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
5691,google/t5-efficient-tiny-nl12,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0435332898050546,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
5692,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-TINY-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff6000,0.0672604708621898,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
5693,malay-huggingface/xlnet-tiny-bahasa-cased,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0592476034847398,-1.0,NLP,a083e6bc-6f03-41c9-b2f2-d7ed3e18f462___xlnet-tiny-bahasa-cased,HF
5694,tau/tavbert-he,The results suggest that Pre-training is an important field of direction in this field of research for further improvement,mldmm/GlassBERTa,0.0498613324016332,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
5695,google/t5-efficient-tiny-nl6,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0380008853971958,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
5696,hakurei/gpt-j-random-tinier,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,0.0461871735751628,-1.0,NLP,098e6c6a-bdeb-49aa-bf12-9e2fff6eb8d6___gpt-j-random-tinier,HF
5697,google/t5-efficient-base-nl4,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0724925287067889,-1.0,NLP,88377af3-f68d-4460-90ea-3f401d21b158___t5-efficient-base-nl4,HF
5698,flax-community/RoBERTa-large-finnish,"More precisely, it was pretrained with the Masked language modeling (MLM) objective",flax-community/RoBERTa-large-finnish,1.0,-1.0,NLP,bd6a99f8-f445-4aab-803e-729051effeb2___RoBERTa-large-finnish,HF
5699,DemangeJeremy/4-sentiments-with-flaubert,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0642946287989616,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5700,google/t5-efficient-small-dm768,Its original repository can be found here,tau/splinter-large-qass,0.0406005568802356,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
5701,1one5ome/gpt2-chinese-gulong,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0347378887236118,-1.0,NLP,1093c4d1-65b3-4e7d-aa2d-43c599b6805a___gpt2-chinese-gulong,HF
5702,abdusahmbzuai/aradia-ctc-distilhubert-ft,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0496790073812007,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
5703,negfir/distilbert-base-uncased-finetuned-squad,com/peterwestuw/GPT2ForwardBackward,danyaljj/opengpt2_pytorch_forward,0.0806113108992576,-1.0,NLP,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
5704,stas/mt5-tiny-random,Generated from the official Squad2 evaluation scriptMuRIL is specifically trained to work on 18 Indic languages and English,Sindhu/muril-large-squad2,0.0643004700541496,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5705,stas/mt5-tiny-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,0.0167105235159397,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5706,TJMUCH/transcriptome-iseeek,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0951679572463035,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5707,lgrobol/electra-minuscule-generator,"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,0.0499166315421462,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
5708,google/bert_uncased_L-10_H-768_A-12,They can be fine-tuned in the same manner as the original BERT models,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
5709,mwesner/reformer-clm,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,0.070254236459732,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
5710,google/bert_uncased_L-8_H-256_A-4,I have made no attempts to censor,bigjoedata/rockbot,0.0217506180827816,-1.0,NLP,514972f2-a359-4796-b315-7811fbd63bdc___bert_uncased_L-8_H-256_A-4,HF
5711,hfl/rbtl3,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
5712,abdusahmbzuai/aradia-ctc-distilhubert-ft,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0999010279774665,-1.0,NLP,cbdb82d5-021c-457f-845f-a6a2bd69e700___aradia-ctc-distilhubert-ft,HF
5713,KoichiYasuoka/roberta-base-ukrainian,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0953442007303237,-1.0,NLP,ac70f30d-0ff6-4ae4-b8d6-3d93d837609c___roberta-base-ukrainian,HF
5714,google/t5-efficient-large-nl20,This does not make a very readable text,pere/DeUnCaser,0.0551294181495904,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
5715,cammy/led-large-16384-arxiv-100-MDS,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0407313331961631,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
5716,VietAI/gpt-neo-1.3B-vietnamese-news,"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,0.0595609070733189,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
5717,mrm8488/bert-small-finetuned-squadv2,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.075990468263626,-1.0,NLP,b93eb377-1ae3-43ee-9184-eb8fea2cf9f6___bert-small-finetuned-squadv2,HF
5718,aditeyabaral/sentencetransformer-bert-hinglish-small,"See our Github repository for details on training, evaluation, and usage",andrejmiscic/simcls-scorer-billsum,0.0540020242333412,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
5719,nghuyong/ernie-tiny,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0079887981216112,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
5720,anas-awadalla/bert-tiny-finetuned-squad,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
5721,mgreenbe/bertlet-base-uncased-for-sequence-classification,"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,cf0f0f8c-9a81-4431-be54-e01e86536350___bertlet-base-uncased-for-sequence-classification,HF
5722,google/t5-efficient-large-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,0.0123761240392923,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
5723,j-hartmann/purchase-intention-english-roberta-large,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,0.0259416202704111,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
5724,anas-awadalla/bert-tiny-finetuned-squad,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.028110709041357,-1.0,NLP,47abb358-57c6-44eb-944e-53f400092ef5___bert-tiny-finetuned-squad,HF
5725,VietAI/gpt-neo-1.3B-vietnamese-news,Google's mT5-small fine-tuned on TyDi QA (secondary task) for multingual QandA downstream task,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,0.0791415330022573,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
5726,clue/xlnet_chinese_large,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0576743111014366,-1.0,NLP,e97fc617-1a5a-4a4c-b8dc-779cfc0cf7da___xlnet_chinese_large,HF
5727,google/t5-efficient-base-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,0.067430656403303,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
5728,google/t5-efficient-tiny-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,850fa78d-aef0-41f1-b76b-3041babd374c___t5-efficient-tiny-ff12000,HF
5729,mmaguero/gn-bert-tiny-cased,This is a ported version of fairseq-based wmt19 transformer for de-en,allenai/wmt19-de-en-6-6-big,0.0669802743941545,-1.0,NLP,953fbb75-3b2d-4881-b482-eec26e5734e6___gn-bert-tiny-cased,HF
5730,stas/t5-very-small-random,For best results use python as below,pszemraj/led-large-book-summary,0.0351085290312767,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
5731,google/t5-efficient-large-dm2000,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.06623075902462,-1.0,NLP,27bf9307-65e8-40b7-85aa-2b72ddde0af0___t5-efficient-large-dm2000,HF
5732,hfl/chinese-electra-180g-small-ex-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,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
5733,obss/mt5-base-3task-highlight-tquad2,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,0.0890185683965682,-1.0,NLP,bdb912e4-d998-496a-8999-1c3e4cdc2c96___mt5-base-3task-highlight-tquad2,HF
5734,google/t5-efficient-tiny-nl12,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.044792154803872,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
5735,google/t5-efficient-small-dm256,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0443486776202916,-1.0,NLP,3432facd-421a-404b-b2be-9d68e2062ffb___t5-efficient-small-dm256,HF
5736,KBLab/megatron-bert-base-swedish-cased-600k,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.0,-1.0,NLP,0d8e52e6-2fee-470f-a661-079483912d57___megatron-bert-base-swedish-cased-600k,HF
5737,google/t5-efficient-tiny-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,0.0763783566653727,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
5738,mimi/Waynehills-NLP-doogie,"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,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
5739,google/t5-efficient-large-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,0.0470434557646513,-1.0,NLP,8edc1112-d258-4b5a-8ee3-2dcb91b8384c___t5-efficient-large-nh24,HF
5740,google/t5-efficient-large-kv128,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.081877402961254,-1.0,NLP,4c62e877-9fa0-42d5-9143-ed1ebf17320b___t5-efficient-large-kv128,HF
5741,google/t5-efficient-base-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,332d97e7-6089-4ffb-8fae-9029585ee04d___t5-efficient-base-dm512,HF
5742,Fujitsu/pytorrent,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0302627917068699,-1.0,NLP,e8d03b21-7f4c-4cf8-a032-e61fa72bff06___pytorrent,HF
5743,Palak/microsoft_deberta-base_squad,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0422040298581123,-1.0,NLP,cf0bba79-a120-4219-b83d-61534dd6bf0c___microsoft_deberta-base_squad,HF
5744,aubmindlab/aragpt2-large,"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,f298e596-b1d6-4a43-bd7e-5b2a87bbc865___aragpt2-large,HF
5745,google/t5-efficient-xl-nl28,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,0.0970332957804203,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
5746,jnz/electra-ka,To use the transformers portion of this model:,liaad/srl-pt_xlmr-base,0.0802431926131248,-1.0,NLP,57f15755-5639-4df8-95bb-826ff3c649c5___electra-ka,HF
5747,stas/t5-very-small-random,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.057433720678091,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
5748,google/t5-efficient-small-ff9000,T5-Efficient-SMALL-FF9000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff9000,1.0,-1.0,NLP,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
5749,funnel-transformer/large-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,0.0173551619052886,-1.0,NLP,d9ff8953-7fdd-459e-941e-3be25f97beee___large-base,HF
5750,google/t5-efficient-mini,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0440121656283735,-1.0,NLP,5d904f5f-af7f-4031-8e5a-75969f31d1fb___t5-efficient-mini,HF
5751,google/t5-efficient-xxl-nl4,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0788344517350196,-1.0,NLP,988bcbfb-87f6-407e-a70d-d3e667446f7c___t5-efficient-xxl-nl4,HF
5752,google/t5-efficient-large-el2,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0506769828498363,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
5753,google/t5-efficient-xl-nl28,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0688991528004407,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
5754,superb/hubert-base-superb-er,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0734450314193963,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
5755,google/t5-efficient-small-el8,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0141765177249908,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
5756,google/t5-efficient-xl-nl28,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,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
5757,google/t5-efficient-small-dm768,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0244669634848833,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
5758,google/t5-efficient-base-el6,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0434488970786332,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
5759,skt/ko-gpt-trinity-1.2B-v0.5,"Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan BaskaranWork done via MLDMM Lab",mldmm/GlassBERTa,0.0482828775420784,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
5760,ductuan024/AimeLaw,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,1.0,-1.0,NLP,0bf79449-28c6-44d5-b363-0e5fc9942939___AimeLaw,HF
5761,google/t5-efficient-large-dm768,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0550094936043023,-1.0,NLP,e6c3e13f-df09-4d50-9853-40b5af38a3f1___t5-efficient-large-dm768,HF
5762,IDEA-CCNL/Erlangshen-Longformer-330M,The data are compiled from JW300 and other additional data I scraped from the JW website,Gilles/FongBERT,0.0565297752618789,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
5763,aditeyabaral/sentencetransformer-distilbert-hinglish-big,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,5f2a3220-5a96-4e2d-912b-e0236a89be5d___sentencetransformer-distilbert-hinglish-big,HF
5764,microsoft/unihanlm-base,"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,1.0,-1.0,NLP,326e98f8-d7fa-4c69-bf51-112549a8f246___unihanlm-base,HF
5765,microsoft/layoutlm-large-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,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
5766,Recognai/zeroshot_selectra_small,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
5767,nyu-mll/roberta-med-small-1M-3,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0449911169707774,-1.0,NLP,fbb42c7c-0b5b-4cbb-bac5-9bb4fd93d9bc___roberta-med-small-1M-3,HF
5768,google/t5-efficient-mini-nl6,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0391397476196288,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
5769,aubmindlab/aragpt2-mega,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.022620528936386,-1.0,NLP,715a228d-349e-4939-b397-1856e8a50bed___aragpt2-mega,HF
5770,google/t5-efficient-tiny-el8,"Hence, the inputs do not contain an attention_mask",google/fnet-large,0.0,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
5771,IDEA-CCNL/Erlangshen-Longformer-330M,Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective,aliosm/ComVE-gpt2-large,0.0416342932730912,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
5772,dbmdz/electra-small-turkish-cased-discriminator,Full-precision finetuning of I-BERT is similar to RoBERTa finetuning,kssteven/ibert-roberta-large,0.0303877941332757,-1.0,NLP,fda6a19b-c067-4346-9761-6b705ca78d84___electra-small-turkish-cased-discriminator,HF
5773,skt/ko-gpt-trinity-1.2B-v0.5,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0285524846985936,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
5774,facebook/wmt21-dense-24-wide-en-x,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0455657090991735,-1.0,NLP,7d1fa3d7-fb75-47f8-9662-6f44de556d74___wmt21-dense-24-wide-en-x,HF
5775,google/t5-efficient-tiny-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,0.070405313745141,-1.0,NLP,f33ef720-168e-40af-994c-4aa5e3ad8e27___t5-efficient-tiny-nl6,HF
5776,mwesner/bart-mlm,com/drive/113o6vkLZRkm6OwhTHrvE0x6QPpavj0fn,monsoon-nlp/dv-muril,0.092008426785469,-1.0,NLP,24676732-3736-44b8-bd09-c0b73d57d400___bart-mlm,HF
5777,soheeyang/rdr-ctx_encoder-single-trivia-base,"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,0.0313699642817179,-1.0,NLP,22b10d5a-7e6d-406e-94f8-ff1d4849ac62___rdr-ctx_encoder-single-trivia-base,HF
5778,mimicheng/codeparrot-ds,Automated question generation and question answering using text-to-text transformers by OBSS AI,obss/mt5-base-3task-highlight-tquad2,0.0636305958032608,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
5779,kyryl0s/gpt2-uk-xxs,The knowledge distillation was performed using multiple loss functions,eli4s/Bert-L12-h384-A6,0.0250679720193147,-1.0,NLP,f19826c2-d6b1-4d5d-bdad-d37c8c7eef27___gpt2-uk-xxs,HF
5780,Norod78/hebrew_poetry-gpt_neo-tiny,For more details please refer to the official website,flaubert/flaubert_small_cased,0.047231058590114,-1.0,NLP,0dfb0263-a152-4a49-b1ff-0a164d184ac2___hebrew_poetry-gpt_neo-tiny,HF
5781,google/t5-efficient-small-dm2000,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0217697108164429,-1.0,NLP,d1648004-65c3-482b-9c7f-922b522637cb___t5-efficient-small-dm2000,HF
5782,mwesner/reformer-clm,"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,0.0411996990442276,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
5783,brad1141/Longformer-finetuned-norm,Its original repository can be found here,tau/splinter-large-qass,0.051607958972454,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
5784,google/t5-efficient-large-nh4,"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,0.0044647231698036,-1.0,NLP,4de4d8a6-3e25-4808-97ed-196dfbf7477b___t5-efficient-large-nh4,HF
5785,google/t5-efficient-small-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,0.0632019024342298,-1.0,NLP,42bcd71e-9ef6-47b8-b67d-5b2ce6541d0e___t5-efficient-small-nl36,HF
5786,TJMUCH/transcriptome-iseeek,"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,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5787,google/t5-efficient-tiny-nh8,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0039337081834674,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
5788,google/t5-efficient-base-nl24,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,2be2060e-297d-4fb5-ba82-5c079e8d5236___t5-efficient-base-nl24,HF
5789,google/t5-efficient-xl-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,0.0971532016992569,-1.0,NLP,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
5790,google/t5-efficient-small-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,0.0437060175463557,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
5791,google/t5-efficient-large-el4,FlauBERT is a French BERT trained on a very large and heterogeneous French corpus,flaubert/flaubert_small_cased,0.0152831505984068,-1.0,NLP,b28d132f-9b76-405e-b14c-8af0b714bd5c___t5-efficient-large-el4,HF
5792,tucan9389/kcbert-base-finetuned-squad,"It also has a relatively small context window, which makes it quite uninteresting",kyryl0s/gpt2-uk-xxs,0.0519877597689628,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
5793,google/t5-efficient-base-nl36,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0461367033421992,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
5794,TJMUCH/transcriptome-iseeek,"from transformers import AutoTokenizer, AutoModelForMaskedLM from transformers import pipelinetokenizer = AutoTokenizer",Gilles/FongBERT,0.0903304144740104,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5795,dbmdz/convbert-base-german-europeana-cased,"Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable",bigjoedata/rockbot,0.0158878838022549,-1.0,NLP,44ad818b-e0bf-476d-9c24-af370f5b24d7___convbert-base-german-europeana-cased,HF
5796,google/bert_uncased_L-10_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,de971cdd-d7b2-44df-bf21-5157c3486242___bert_uncased_L-10_H-128_A-2,HF
5797,mwesner/reformer-clm,T5-Efficient-SMALL-NL22 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl22,0.0893971472978591,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
5798,stas/mt5-tiny-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,0.022033080458641,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5799,rdenadai/BR_BERTo,ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5,google/byt5-xl,0.0139882362758119,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
5800,google/t5-efficient-tiny-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,0.0620283670723438,-1.0,NLP,2ddc280e-ffb1-4937-ad2a-141d503f67d7___t5-efficient-tiny-el8,HF
5801,google/t5-efficient-base-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,0.0228886874392627,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
5802,KBLab/megatron-bert-large-swedish-cased-165k,"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,1.0,-1.0,NLP,79033898-b2c7-427e-b51b-009f55ab0e76___megatron-bert-large-swedish-cased-165k,HF
5803,Vilnius-Lithuania-iGEM/Albumin,The model's architecture follows the architecture of a two-tower model as depicted in the figure below:,UWB-AIR/MQDD-duplicates,0.0130383757253487,-1.0,NLP,f4a0c198-e89a-4c9a-b221-80641d22f2d3___Albumin,HF
5804,ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,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
5805,google/t5-efficient-large-dm256,"Following the original BERT training, 15% of tokens were masked",SajjadAyoubi/distil-bigbird-fa-zwnj,0.0331569015979766,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
5806,google/t5-efficient-xl-nl4,"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,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
5807,FremyCompany/xls-r-nl-v1-cv8-lm,"For further information or requests, please go to Camembert Website",camembert/camembert-base-oscar-4gb,0.0397663693875074,-1.0,NLP,7b0f154a-89ce-4d08-9ab5-7eb2c9143f6a___xls-r-nl-v1-cv8-lm,HF
5808,ToToKr/kobigbird-bert-base-finetuned-klue,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,0.0789987295866012,-1.0,NLP,5767b1ae-dfeb-418d-bea4-a689ae96dc86___kobigbird-bert-base-finetuned-klue,HF
5809,stas/mt5-tiny-random,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5810,tau/tavbert-he,T5-Efficient-SMALL-EL12 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-el12,0.0570082142949104,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
5811,google/t5-efficient-xl-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-v1,0.0490189790725707,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
5812,gayanin/bart-mlm-pubmed-medterm,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,7d3fd2f1-5262-43be-8023-3c410f922bff___bart-mlm-pubmed-medterm,HF
5813,google/t5-efficient-base-el8,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0215883231721817,-1.0,NLP,41b715d8-8f1f-47b4-86c8-57fe21733979___t5-efficient-base-el8,HF
5814,aditeyabaral/sentencetransformer-bert-hinglish-small,"Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-BonnetFor more details, do check out our Github repo",razent/SciFive-large-Pubmed,0.0525723025202751,-1.0,NLP,651b9cb2-ca0c-4248-80bc-b78b4f2b3f65___sentencetransformer-bert-hinglish-small,HF
5815,ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,"It adds punctation, and capitalises the correct words",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
5816,sentence-transformers/paraphrase-MiniLM-L3-v2,I will update it with support for other languages if there is any demand,pere/DeUnCaser,0.0,-1.0,NLP,dbf65700-2a78-4371-b2a2-f5477fecc55e___paraphrase-MiniLM-L3-v2,HF
5817,uer/t5-v1_1-small-chinese-cluecorpussmall,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.067205449566245,-1.0,NLP,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
5818,google/t5-efficient-mini-nl8,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0664323065429925,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
5819,google/t5-efficient-base-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,0.0567260142415761,-1.0,NLP,40bc31bb-ca03-4bbe-9850-6783464aa196___t5-efficient-base-nl2,HF
5820,lgrobol/electra-minuscule-generator,"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,0.040880337357521,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
5821,stas/mt5-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,0.0925954580307006,-1.0,NLP,44fbbcfd-3541-4779-adc7-3b15e6a53f3f___mt5-tiny-random,HF
5822,diper1998/distilgpt2-finetuned-AT,"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,0.0725859403610229,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
5823,google/t5-efficient-base-kv256,Les travaux sont actuellement en cours,DemangeJeremy/4-sentiments-with-flaubert,0.0110162794589995,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
5824,google/fnet-large,It is pretrained on a large corpus of  English data in a self-supervised fashion,google/fnet-large,1.0,-1.0,NLP,be8c3b54-acb9-41d3-9190-d3a35ba6f7a6___fnet-large,HF
5825,microsoft/layoutlm-base-cased,Below performance was evaluated at 2021/12/15,lassl/roberta-ko-small,0.0510972663760185,-1.0,NLP,26bd9766-557e-4451-b57d-98ceae6a271c___layoutlm-base-cased,HF
5826,google/t5-efficient-base-kv32,ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters,google/byt5-xl,0.0768535062670707,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
5827,google/t5-efficient-base-kv256,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,cb305d16-6539-42dc-8d82-52679676f0b6___t5-efficient-base-kv256,HF
5828,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-NL40 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-nl40,0.0784072130918502,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
5829,amazon/bort,Model is based on GPT-J and has over 162M trainable parameters,Milos/slovak-gpt-j-162M,0.0524616073817014,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
5830,google/t5-efficient-mini-nl6,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0421159286051988,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
5831,google/t5-efficient-tiny-nl2,Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554,google/reformer-crime-and-punishment,0.0408499930053948,-1.0,NLP,a516a2c8-d452-4e87-9239-7a1d7664ad4d___t5-efficient-tiny-nl2,HF
5832,google/t5-efficient-small-nl2,* each layer consists of one feedforward block and one self attention block,NovelAI/genji-python-6B,0.0691349394619464,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
5833,razent/SciFive-base-PMC,"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,0.0656134262681007,-1.0,NLP,5f2909fc-f5c5-4bbc-b100-e040e1c8d245___SciFive-base-PMC,HF
5834,google/t5-efficient-base-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,0.0421544350683689,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
5835,gilparmentier/pokemon_gptj_model,GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX,gilparmentier/pokemon_gptj_model,1.0,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
5836,IlyaGusev/rugpt3medium_sum_gazeta,We specifically inspect that random masking proposed in is not suitable for modelling Alloys,mldmm/GlassBERTa,0.0406148880720138,-1.0,NLP,2fd14d5d-44e4-4060-8eda-64ad4c64775f___rugpt3medium_sum_gazeta,HF
5837,microsoft/layoutlm-large-uncased,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0088327713310718,-1.0,NLP,6587cb4f-43e6-4901-8836-773402f2b7d9___layoutlm-large-uncased,HF
5838,google/t5-efficient-base-nl36,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0171233802102506,-1.0,NLP,e256235d-8b9c-4ae9-9965-064b76cbc207___t5-efficient-base-nl36,HF
5839,google/t5-efficient-xl-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,0.0687342882156372,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
5840,superspray/electra_large_discriminator_squad2_custom_dataset,ByT5-Korean is a Korean specific extension of Google's ByT5,everdoubling/byt5-Korean-small,0.0863760784268379,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
5841,tau/tavbert-he,T5-Efficient-LARGE-EL4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-el4,0.0286500761285423,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
5842,google/t5-efficient-large-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,0.0688699856400489,-1.0,NLP,cc056992-73b6-4b1f-bd21-034cbdca5e1c___t5-efficient-large-nl12,HF
5843,allenai/wmt19-de-en-6-6-big,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0360854668542743,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
5844,google/t5-efficient-xl-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-p1,0.0070155002176761,-1.0,NLP,37f4e2de-fe9c-4011-94c3-cc9b6df81c76___t5-efficient-xl-nl16,HF
5845,cross-encoder/stsb-TinyBERT-L-4,This model was trained on the STS benchmark dataset,cross-encoder/stsb-TinyBERT-L-4,1.0,-1.0,NLP,0fc6c658-1c5a-4190-ba7e-7f711453e040___stsb-TinyBERT-L-4,HF
5846,skt/ko-gpt-trinity-1.2B-v0.5,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,0.0538034234195947,-1.0,NLP,ed304c2c-5dae-4d02-8a46-ac80bf7391fb___ko-gpt-trinity-1.2B-v0.5,HF
5847,microsoft/layoutlm-base-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,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
5848,google/t5-efficient-tiny-nl12,"Instructions: Type in a fake song title, pick an artist, click ""Generate""",bigjoedata/rockbot,0.0022791475057602,-1.0,NLP,3db1252e-a9d4-484d-9e95-ff0624ba93fc___t5-efficient-tiny-nl12,HF
5849,rdenadai/BR_BERTo,T5-Efficient-BASE-DM1000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm1000,0.0145073477178811,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
5850,pulp/CHILDES-ParentBERTo,T5-Efficient-SMALL-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-dm256,0.0659028720110654,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
5851,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-SMALL-KV256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv256,0.0013877190649509,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5852,rdenadai/BR_BERTo,T5-Efficient-SMALL-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-kv128,0.0216196676095326,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
5853,fnlp/cpt-base,"For more information, see the paper",UWB-AIR/MQDD-duplicates,0.0557371145114302,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
5854,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,It was intorduced in this paper,rmihaylov/pegasus-base-cnn-dailymail-bg,0.0,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
5855,UWB-AIR/MQDD-duplicates,Then sort the passages in a decreasing order,cross-encoder/ms-marco-MiniLM-L-4-v2,0.0,-1.0,NLP,2468c729-8a7e-431c-a96a-6256164c348f___MQDD-duplicates,HF
5856,google/t5-efficient-base-el16,I follow the great tutorial from HuggingFace team:,rdenadai/BR_BERTo,0.0144708920270204,-1.0,NLP,d1ca2ed7-67b7-4095-97ea-787a347c7e97___t5-efficient-base-el16,HF
5857,mrm8488/mT5-small-finetuned-tydiqa-for-xqa,"com/Muennighoff/sgpt For eval results, refer to our paper: https://arxiv",Muennighoff/SGPT-125M-weightedmean-nli-bitfit,0.0849446803331374,-1.0,NLP,065ec8a2-565e-4cee-8798-69a53f29bce0___mT5-small-finetuned-tydiqa-for-xqa,HF
5858,IDEA-CCNL/Erlangshen-Longformer-330M,"!!!  At the moment, the model is distilled, a version from one of the first checkpoints is available for download",RabotaRu/HRBert-mini,0.0182233601808548,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
5859,fnlp/bart-base-chinese,"Note (2): Splinter-large was trained after the paper was released, so the results are not reported",tau/splinter-large-qass,0.0574445724487304,-1.0,NLP,975062b9-390c-4715-ab0d-0a7c327e06e8___bart-base-chinese,HF
5860,Milos/slovak-gpt-j-405M,"For more details, see the card for a related model: https://huggingface",cointegrated/rubert-tiny-bilingual-nli,0.0149883702397346,-1.0,NLP,7211fab8-6e89-4656-8a4d-e4698f92108d___slovak-gpt-j-405M,HF
5861,patrickvonplaten/unispeech-sat-large-timit-ft,"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,0.0435339137911796,-1.0,NLP,787d5d1d-06a3-4aad-a55c-e78434f5481b___unispeech-sat-large-timit-ft,HF
5862,microsoft/layoutlm-base-uncased,"com/zihangdai/xlnetYou may also interested in,",hfl/chinese-xlnet-mid,0.0088327713310718,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
5863,ai4bharat/MultiIndicHeadlineGeneration,It currently supports 11 Indian languages and is finetuned on IndicBART checkpoint,ai4bharat/MultiIndicHeadlineGeneration,1.0,-1.0,NLP,39059d23-94a1-4ba7-8991-003e24d4db7a___MultiIndicHeadlineGeneration,HF
5864,google/t5-efficient-tiny-nh8,Be careful with English words! The tokenizer would cut it to characters,microsoft/unihanlm-base,0.0341856628656387,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
5865,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5866,hfl/chinese-electra-180g-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,c6f5867f-84c8-46e9-a6e7-9ca2379e0c4c___chinese-electra-180g-small-ex-generator,HF
5867,google/t5-efficient-large-nh12,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0097621921449899,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
5868,google/t5-efficient-large-nh12,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,0.0369131974875926,-1.0,NLP,08b8d109-c640-451b-847b-e5d283130a6f___t5-efficient-large-nh12,HF
5869,allenai/wmt19-de-en-6-6-big,"For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation",allenai/wmt19-de-en-6-6-big,1.0,-1.0,NLP,cfeee161-03e3-4111-a7da-50e6ce7036f9___wmt19-de-en-6-6-big,HF
5870,rahulacj/bertweet-base-finetuned-sentiment-analysis,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0717557668685913,-1.0,NLP,c9d053e2-05da-4b1c-89b8-b00397fd0d97___bertweet-base-finetuned-sentiment-analysis,HF
5871,google/t5-efficient-small-ff3000,More details about ELECTRA can be found in the ICLR paper or in the official ELECTRA repository on GitHub,krevas/finance-koelectra-base-generator,0.0393340513110161,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
5872,fnlp/cpt-base,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,0.0546963363885879,-1.0,NLP,acb3fa53-a6bf-49d2-9ed0-ebf2f7b4c897___cpt-base,HF
5873,google/t5-efficient-tiny-nh8,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0528109241276979,-1.0,NLP,185c7a16-f180-4c26-918c-ab3dcc7a6dcb___t5-efficient-tiny-nh8,HF
5874,stas/pegasus-cnn_dailymail-tiny-random,"This is a GPT2 model of Italian regional languages trained on collections of Italian ""dialect poetry"" by Luigi Bonaffini",ErykWdowiak/GPTalian,0.0721675567328929,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
5875,akdeniz27/deberta-v2-xlarge-cuad,"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,0.097404658794403,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
5876,google/t5-efficient-base-nh16,MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters,MoritzLaurer/MiniLM-L6-mnli,0.0853121392428874,-1.0,NLP,e9aad73c-c20d-45b3-82db-4bb9955b2d6e___t5-efficient-base-nh16,HF
5877,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,"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,0.099034696817398,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
5878,amazon/bort,For other languages this will be mainly experimental,pere/DeUnCaser,0.0454828608781099,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
5879,amazon/bort,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0291956812143325,-1.0,NLP,0913e321-4a14-409b-ba45-71eabbf5dbd5___bort,HF
5880,google/t5-efficient-xl-nl8,T5-Efficient-XL-NL8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-xl-nl8,1.0,-1.0,NLP,4c2466ea-5b36-42ab-9dbb-d10bfdf6f84d___t5-efficient-xl-nl8,HF
5881,thu-coai/LongLM-large,T5-Efficient-TINY-FF12000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-tiny-ff12000,0.0619399493249753,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
5882,cammy/led-base-16384-100-MDS,"If you find the resource is useful, please cite the following website in your paper",IDEA-CCNL/Wenzhong-GPT2-3.5B,0.0631855130195617,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
5883,jorge-henao/gpt2-small-spanish-disco-poetry-wt,See the results for Splinter-large in the Appendix of this paper,tau/splinter-large-qass,0.017399376258254,-1.0,NLP,cde03bdc-b117-49cb-b2e0-d9b522a2ccfa___gpt2-small-spanish-disco-poetry-wt,HF
5884,google/t5-efficient-small-kv16,"By cleaned SMILES I mean that I used their SMILES cleaning script to remove duplicates, salts, and stereochemical information",mrm8488/chEMBL_smiles_v1,0.0209743417799473,-1.0,NLP,a1f3f65c-851b-47d3-8cbb-ffc622fa5f81___t5-efficient-small-kv16,HF
5885,stas/t5-very-small-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,0.0672986507415771,-1.0,NLP,d33c8d65-d87c-41dc-bbbe-83605482bea6___t5-very-small-random,HF
5886,IDEA-CCNL/Erlangshen-Longformer-110M,"This model is community-contributed, and not supported by Amazon, Inc",amazon/bort,0.0,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
5887,j-hartmann/purchase-intention-english-roberta-large,T5-Efficient-LARGE-DM768 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm768,0.0695423545936743,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
5888,google/t5-efficient-large-dm512,T5-Efficient-LARGE-DM512 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm512,1.0,-1.0,NLP,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
5889,groar/gpt-neo-1.3B-finetuned-escape2,Building open-domain chatbots is a challenging area for machine learning research,hyunwoongko/reddit-9B,0.0198050793260335,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
5890,rinna/japanese-gpt2-xsmall,"Further global deduplication among the corpus is applied, resulting into 570GB of text",PlanTL-GOB-ES/gpt2-large-bne,0.0325067155063152,-1.0,NLP,c50da120-788b-478a-b6f2-5ff46138bcd4___japanese-gpt2-xsmall,HF
5891,superspray/electra_large_discriminator_squad2_custom_dataset,For more details please refer to the official website,flaubert/flaubert_small_cased,0.0427302531898021,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
5892,IDEA-CCNL/Erlangshen-Longformer-330M,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0094069167971611,-1.0,NLP,73934642-01fa-4f8f-b7a7-f90c55bbf50a___Erlangshen-Longformer-330M,HF
5893,thu-coai/LongLM-large,"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,1.0,-1.0,NLP,817c4171-315a-48dd-aa93-345e5c99a33e___LongLM-large,HF
5894,pulp/CHILDES-ParentBERTo,T5-Efficient-MINI-NL24 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-mini-nl24,0.0907393172383308,-1.0,NLP,1ba78fde-1f32-465c-ad71-b802ddd08bf8___CHILDES-ParentBERTo,HF
5895,SetFit/deberta-v3-large__sst2__train-8-8,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0099343033507466,-1.0,NLP,543629b5-da7e-432e-9c97-88c75132a1af___deberta-v3-large__sst2__train-8-8,HF
5896,saibo/random-roberta-tiny,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0290744574740529,-1.0,NLP,ea443951-6d5a-4dfa-94c0-d4238c3411d6___random-roberta-tiny,HF
5897,hfl/chinese-electra-180g-small-ex-discriminator,"After you get the output, you should convert it back into the original script",ai4bharat/MultiIndicHeadlineGeneration,0.047187687208255,-1.0,NLP,18de5649-7a51-4ed8-9c58-24ef9a94647b___chinese-electra-180g-small-ex-discriminator,HF
5898,google/t5-efficient-base-nh24,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0300973411649465,-1.0,NLP,1eebbad6-11b9-4034-9c33-ec25a8c3cb3e___t5-efficient-base-nh24,HF
5899,google/t5-efficient-large-nl20,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0343576893210411,-1.0,NLP,1b131452-a8e1-49e3-81b9-f3299de8ea73___t5-efficient-large-nl20,HF
5900,lgrobol/electra-minuscule-generator,it is used to find passages containing the answers to the given question,enelpol/czywiesz-context,0.0,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
5901,brad1141/Longformer-finetuned-norm,"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,0.0955761447548866,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
5902,hfl/rbt3,Permission to upload to Huggingface was given by the main author,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5903,Recognai/zeroshot_selectra_small,Zero-shot SELECTRA is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset,Recognai/zeroshot_selectra_small,1.0,-1.0,NLP,dcea80a7-7e51-4003-a077-7e747af8c7fc___zeroshot_selectra_small,HF
5904,lgrobol/electra-minuscule-generator,"For more information, please contact anhduongng",VietAI/gpt-neo-1.3B-vietnamese-news,0.0693361368030309,-1.0,NLP,f4b4aaac-a62b-44bf-88e4-1d559ade286a___electra-minuscule-generator,HF
5905,ffsouza/tiny-mbart-length-128-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,9117e19f-db0f-4c49-993e-bc360728c9b8___tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro,HF
5906,superb/hubert-base-superb-er,When using the model make sure that your speech input is also sampled at 16Khz,superb/hubert-base-superb-er,1.0,-1.0,NLP,62e5d9d5-57fd-4778-912c-c9eb5cef6954___hubert-base-superb-er,HF
5907,flax-community/gpt2-medium-persian,"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,cd9df871-b445-4a2f-ba22-f2652983bf27___gpt2-medium-persian,HF
5908,brad1141/Longformer-finetuned-norm,This may not generalize well for all use cases in different domains,Davlan/mbart50-large-eng-yor-mt,0.0871200114488601,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
5909,cammy/led-base-16384-100-MDS,"Tokenization algorithm is Character-level, which means that text is split by individual characters",ctoraman/RoBERTa-TR-medium-char,0.0573637634515762,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
5910,google/t5-efficient-small-nl2,The masking follows the original Bert training with randomly masks 15% of the amino acids in the input,Rostlab/prot_bert,0.0661341659724712,-1.0,NLP,4ea4563e-5e61-4bb1-8694-9f2d2134a9b2___t5-efficient-small-nl2,HF
5911,google/t5-efficient-large-nh32,This does not make a very readable text,pere/DeUnCaser,0.0643298923969268,-1.0,NLP,bab87f31-440a-4d03-bb8d-531bbe2f4311___t5-efficient-large-nh32,HF
5912,google/t5-efficient-xl-nl4,"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,92b68048-f665-43c7-8691-d28824bf6b10___t5-efficient-xl-nl4,HF
5913,Iskaj/hf-challenge-test,NOTE: Use T5Tokenizer to initiate the tokenizer,rinna/japanese-gpt2-xsmall,0.0655535906553268,-1.0,NLP,00a968af-a9c4-404e-be8f-026b3c1c7f4f___hf-challenge-test,HF
5914,google/t5-efficient-large-dm256,T5-Efficient-LARGE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-dm256,1.0,-1.0,NLP,670d5487-44f1-4265-9dbc-0c26631e7987___t5-efficient-large-dm256,HF
5915,google/t5-efficient-small-dm768,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.050308740697801,-1.0,NLP,3439c978-0d06-4bd0-9455-cd7a0662d92f___t5-efficient-small-dm768,HF
5916,VietAI/gpt-neo-1.3B-vietnamese-news,"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,0.0838166326284408,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
5917,rinna/japanese-gpt-1b,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0419987216591835,-1.0,NLP,984f77ed-e0a1-4bc6-b994-27fce564bfbc___japanese-gpt-1b,HF
5918,VietAI/gpt-neo-1.3B-vietnamese-news,The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package,jcblaise/distilbert-tagalog-base-cased,0.0509163849055766,-1.0,NLP,7ee6a0fd-ffad-4688-b048-b3274730d139___gpt-neo-1.3B-vietnamese-news,HF
5919,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5920,minimaxir/magic-the-gathering,It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture,enelpol/czywiesz-context,0.0380588956177234,-1.0,NLP,e655fb5b-9b2e-4b9c-87e1-b3bfc5d37e19___magic-the-gathering,HF
5921,groar/gpt-neo-1.3B-finetuned-escape2,"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,0.0533173158764839,-1.0,NLP,597024d3-ffb4-484a-8495-587e196c5449___gpt-neo-1.3B-finetuned-escape2,HF
5922,cammy/led-base-16384-100-MDS,This model is a fine-tuned version of allenai/led-base-16384 on an unknown dataset,cammy/led-base-16384-100-MDS,1.0,-1.0,NLP,d54a15ca-fb8b-4150-bf4e-43ec6cf9fb00___led-base-16384-100-MDS,HF
5923,gilparmentier/pokemon_gptj_model,The National Library of Spain (Biblioteca Nacional de España) crawls all,PlanTL-GOB-ES/gpt2-large-bne,0.0092033087275922,-1.0,NLP,52ac3241-3069-4d78-839d-35ad5640a8be___pokemon_gptj_model,HF
5924,google/t5-efficient-small-dm1000,Peters and Arman Cohan,mrm8488/longformer-base-4096-spanish,0.0,-1.0,NLP,86fa0a24-6ca2-435c-b239-4cf5c2e27c91___t5-efficient-small-dm1000,HF
5925,google/bigbird-roberta-large,All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap,andrejmiscic/simcls-scorer-billsum,0.0047007136046886,-1.0,NLP,7cc33271-9d9e-4ec4-9175-a74bb24c5de6___bigbird-roberta-large,HF
5926,tau/tavbert-he,Evaulation results will be released soon,lassl/gpt2-ko-small,0.0099728424102067,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
5927,hfl/chinese-xlnet-mid,"In particular, I-BERT replaces all floating point operations in the Transformer architectures (e",kssteven/ibert-roberta-large,0.0451325302322705,-1.0,NLP,7edcc2d8-b672-43a7-a9dd-0a57ed0df517___chinese-xlnet-mid,HF
5928,akdeniz27/deberta-v2-xlarge-cuad,ByT5-Korean was pre-trained on mC4 with 70% Korean and 30% English,everdoubling/byt5-Korean-small,0.038968302309513,-1.0,NLP,fb08a451-37eb-4f58-8f57-c45214217220___deberta-v2-xlarge-cuad,HF
5929,google/t5-efficient-large-nh8,Training was done for 600k training steps,KBLab/megatron-bert-base-swedish-cased-600k,0.0,-1.0,NLP,f73eb14f-2cae-4c5a-ad52-0258634e2590___t5-efficient-large-nh8,HF
5930,google/t5-efficient-tiny-nl32,Each layer consists of one feedforward block and one self attention block,gilparmentier/pokemon_gptj_model,0.0556516926735639,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
5931,mwesner/reformer-clm,T5-Efficient-SMALL-FF6000 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-small-ff6000,0.0882475227117538,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
5932,DemangeJeremy/4-sentiments-with-flaubert,T5-Efficient-LARGE-NH8 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh8,0.0,-1.0,NLP,16fea21f-0f49-48d6-84d3-73ffaa805955___4-sentiments-with-flaubert,HF
5933,google/t5-efficient-base-el6,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0039505111053586,-1.0,NLP,1b67950e-da97-46f2-83ff-bc6c969f16d6___t5-efficient-base-el6,HF
5934,lassl/gpt2-ko-small,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0258091120049357,-1.0,NLP,94be4807-1460-474d-9408-8cbef558cf61___gpt2-ko-small,HF
5935,mikaelsouza/msft-regular-model,es domains once a year,PlanTL-GOB-ES/gpt2-base-bne,0.0473373532295227,-1.0,NLP,3fa7d6d4-cab2-4afb-bc9a-bb80db3ae61e___msft-regular-model,HF
5936,tau/tavbert-he,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,0.0105463266372681,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
5937,everdoubling/byt5-Korean-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,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
5938,tucan9389/kcbert-base-finetuned-squad,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.0987108871340751,-1.0,NLP,58c9a15c-1b8e-4f06-b1f9-ab7c3f8c901f___kcbert-base-finetuned-squad,HF
5939,google/t5-efficient-mini-nl24,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0330285243690013,-1.0,NLP,57a5d983-95b3-4577-aff8-7f7390c61e59___t5-efficient-mini-nl24,HF
5940,bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,This model was trained using HuggingFace's PyTorch framework,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
5941,google/t5-efficient-large-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-large-kv256,1.0,-1.0,NLP,9c32ca42-e240-4953-b45b-910612307c45___t5-efficient-large-kv256,HF
5942,CuongLD/wav2vec2-large-xlsr-vietnamese,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0674794651567935,-1.0,NLP,87408e6d-62f7-4f33-8b33-499f002c2c7f___wav2vec2-large-xlsr-vietnamese,HF
5943,allenai/wmt16-en-de-dist-6-1,This model has been initialized with random values,hakurei/gpt-j-random-tinier,0.0333391493186355,-1.0,NLP,bba492c8-fdfb-436e-b2c0-e93644739307___wmt16-en-de-dist-6-1,HF
5944,brad1141/Longformer-finetuned-norm,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0543444529175758,-1.0,NLP,a28a4dc9-9ad8-4b55-81c5-46757d45c440___Longformer-finetuned-norm,HF
5945,khizon/distilbert-unreliable-news-eng-4L,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,1.0,-1.0,NLP,14533098-f61c-44d3-b75f-da43fedf16ff___distilbert-unreliable-news-eng-4L,HF
5946,hfl/chinese-legal-electra-base-generator,The Usage of tokenizer for Myanmar is same as Laos in https://github,GKLMIP/electra-myanmar-small-uncased,0.0,-1.0,NLP,2b1d86cf-1350-490a-8718-ba1db793aa5d___chinese-legal-electra-base-generator,HF
5947,google/t5-efficient-mini-nl6,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,3e6c28fb-8118-4b2c-84eb-3f0aaf029340___t5-efficient-mini-nl6,HF
5948,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,"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,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
5949,google/t5-efficient-small-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,611e8904-b1eb-48c6-923b-cdf135a9cdd6___t5-efficient-small-ff9000,HF
5950,negfir/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,931e8cf3-2f58-4fc2-b6b7-527554a15bad___negfir-distilbert-base-uncased-finetuned-squad,HF
5951,fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input,phueb/BabyBERTa-2,0.0944748520851135,-1.0,NLP,9b32955b-797b-4960-b5a9-b8714c3da045___opus-mt-en-de-finetuned-en-to-de-wd01-fp16false,HF
5952,google/t5-efficient-base-kv128,"This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/gpt2-medium-persian,0.0053607085719705,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
5953,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5954,beston91/gpt2-xl_ft_logits_10k,"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,0.0489900782704353,-1.0,NLP,0508a6b3-ef4d-41c0-91d3-fffa43261fca___gpt2-xl_ft_logits_10k,HF
5955,M-FAC/bert-mini-finetuned-stsb,"QARiB: Is the Arabic name for ""Boat""",qarib/bert-base-qarib_far_6500k,0.0046963011845946,-1.0,NLP,a4f851fe-81b8-437c-9ad2-b42cd64f9c6b___bert-mini-finetuned-stsb,HF
5956,google/t5-efficient-small-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,0.0578720439225435,-1.0,NLP,02fba5b1-ad81-4060-8fdf-cd00535a6da5___t5-efficient-small-el8,HF
5957,nghuyong/ernie-tiny,And there's a lot of English in this dataset too,ErykWdowiak/GPTalian,0.035972349345684,-1.0,NLP,121ac7ba-5948-43e4-9143-8832e9af6bc8___ernie-tiny,HF
5958,krevas/finance-koelectra-base-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,0.037422380875796,-1.0,NLP,c5786724-bf88-47bd-9302-45e81aaebb24___finance-koelectra-base-discriminator,HF
5959,flboehm/reddit-bert-text_5,Source language: English,NYTK/translation-marianmt-en-hu,0.0800180956721305,-1.0,NLP,cb9448ad-b1ed-4108-8c9b-3a2d913374e7___reddit-bert-text_5,HF
5960,IDEA-CCNL/Erlangshen-Longformer-110M,You can use this model directly to generate reasons why the given statement is against commonsense using generate,aliosm/ComVE-gpt2-large,0.036729575600475,-1.0,NLP,0762c221-d51f-49d5-91f0-a2c2fcc3c222___Erlangshen-Longformer-110M,HF
5961,stas/pegasus-cnn_dailymail-tiny-random,This is a ported version of fairseq-based wmt16 transformer for en-de,allenai/wmt16-en-de-dist-6-1,0.0324966050684451,-1.0,NLP,0abd4941-3e48-44b5-8a10-46feb3260528___pegasus-cnn_dailymail-tiny-random,HF
5962,google/t5-efficient-xl-nl12,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,0.0812245830893516,-1.0,NLP,bed19ebe-d45e-4365-a37d-d4efb196c841___t5-efficient-xl-nl12,HF
5963,google/t5-efficient-small-kv128,Sample usage: Download the additional files from here: https://github,danyaljj/opengpt2_pytorch_forward,0.034978600218892,-1.0,NLP,c9b65b2c-1ef0-4d9a-8805-9b1e5faa717a___t5-efficient-small-kv128,HF
5964,tau/tavbert-he,"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,0.037480242550373,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
5965,diper1998/distilgpt2-finetuned-AT,You may see NSFW lyrics unexpectedly,bigjoedata/rockbot,0.0,-1.0,NLP,4156b705-a031-406e-9758-4b6bb5dfb418___distilgpt2-finetuned-AT,HF
5966,google/t5-efficient-small-nl22,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,0.0508203338831663,-1.0,NLP,32ec92de-4fd7-4a3d-b20d-ee5901212885___t5-efficient-small-nl22,HF
5967,aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616,It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer,pere/DeUnCaser,0.0232987608760595,-1.0,NLP,3466f210-8a41-4618-b0c1-7da231277ef1___bert_uncased_L-2_H-512_A-8_cord19-200616,HF
5968,google/t5-efficient-base-nh32,"For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral",cross-encoder/nli-deberta-v3-xsmall,0.0239453464746475,-1.0,NLP,4ff7e3c0-5b1b-403f-9670-6471579c5c2e___t5-efficient-base-nh32,HF
5969,hajime9652/xlnet-japanese,Trained on a corpus of 6_993_330 sentences,rdenadai/BR_BERTo,0.0694809332489967,-1.0,NLP,0444a68d-e70e-4619-ac93-1d7cbfb2ecd5___xlnet-japanese,HF
5970,j-hartmann/purchase-intention-english-roberta-large,Chinese and Japanese share many characters with similar surface morphology,microsoft/unihanlm-base,0.0,-1.0,NLP,1943cbd7-72b9-4804-a524-b3521e380c24___purchase-intention-english-roberta-large,HF
5971,google/t5-efficient-small-ff12000,The model was trained by rinna Co,rinna/japanese-gpt-1b,0.0700128152966499,-1.0,NLP,692884e7-1dfc-4f68-ac33-18c7c45123b5___t5-efficient-small-ff12000,HF
5972,nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB,"It adds punctation, and capitalises the correct words",pere/DeUnCaser,0.0,-1.0,NLP,b740b1f0-69e8-4d19-a297-4d0f7269ef03___SingleBertModel-ProtBertfinetuned-smilesBindingDB,HF
5973,google/t5-efficient-large-dm512,"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,1081564c-18b3-4983-a4fd-b411c4b37373___t5-efficient-large-dm512,HF
5974,mldmm/GlassBERTa,The question part has to be encoded with the corresponding question encoder,enelpol/czywiesz-context,0.0393575563794001,-1.0,NLP,949c20f3-9c67-4d5d-9f2b-d7dd7e51c7bb___GlassBERTa,HF
5975,microsoft/layoutlm-base-uncased,No special consideration given to limitations and bias,lannelin/bert-imdb-1hidden,0.0,-1.0,NLP,dad7d9c8-fd50-460f-9a90-eda7748f5c95___layoutlm-base-uncased,HF
5976,nherve/flaubert-oral-ft,Consider using it for debugging purpose only,flaubert/flaubert_small_cased,0.0318330774704615,-1.0,NLP,61b0b476-379d-4391-926a-edd9b67dd261___flaubert-oral-ft,HF
5977,uer/t5-v1_1-small-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,bab99fbb-782f-425a-83b8-8ccd39aee533___t5-v1_1-small-chinese-cluecorpussmall,HF
5978,google/t5-efficient-large-nl10,""" The discriminator should therefore activate the logit corresponding to the fake input token, as the following example demonstrates:",Recognai/selectra_medium,0.0459830351173877,-1.0,NLP,d40634d0-f34d-48ee-b68f-fcaf1fd2c363___t5-efficient-large-nl10,HF
5979,google/t5-efficient-xl-nl28,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.018056365661323,-1.0,NLP,246b6f06-eac6-4fb6-b857-cca270fdd694___t5-efficient-xl-nl28,HF
5980,google/t5-efficient-small-ff3000,"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,0.0608042925596236,-1.0,NLP,20e3efe1-2b29-4cad-81ba-7efa2b1f8f09___t5-efficient-small-ff3000,HF
5981,hfl/rbt3,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,4fa65f46-04ac-4c56-b477-9791f53b5818___rbt3,HF
5982,krevas/finance-koelectra-base-generator,It can be used to pre-train transformer networks using relatively little compute,krevas/finance-koelectra-base-generator,1.0,-1.0,NLP,75b2bc97-370f-4be2-8434-d007ed0d4e5c___finance-koelectra-base-generator,HF
5983,eli4s/Bert-L12-h256-A4,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,1ed96254-9867-450f-af21-ac7ec26d9d72___Bert-L12-h256-A4,HF
5984,google/t5-efficient-tiny-nh16,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0321006383746862,-1.0,NLP,ea1462d2-2b52-4018-901b-ea8bde8556e0___t5-efficient-tiny-nh16,HF
5985,google/t5-efficient-base-kv32,It should always give random results (i,dhpollack/distilbert-dummy-sentiment,0.0089507838711142,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
5986,ptaszynski/yacis-electra-small-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,1.0,-1.0,NLP,acdc3f44-79b6-4b4a-95bc-83ed9c25bc01___yacis-electra-small-japanese,HF
5987,pszemraj/led-large-book-summary,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,df75bc6d-08ce-485d-a766-43b46efb20dc___led-large-book-summary,HF
5988,aliosm/ComVE-gpt2-large,The parent's data can be found here: https://github,pulp/CHILDES-ParentBERTo,0.0271208838094025,-1.0,NLP,5629f06f-1153-4a8e-af40-91ef1f696dfa___ComVE-gpt2-large,HF
5989,google/t5-efficient-small-ff6000,I have made no attempts to censor,bigjoedata/rockbot,0.0,-1.0,NLP,f5270697-5513-4c79-8623-eb204490cf37___t5-efficient-small-ff6000,HF
5990,google/t5-efficient-mini-nl8,However the current finetuning is only done on Norwegian,pere/DeUnCaser,0.066910795867443,-1.0,NLP,24d6bab0-f917-42d5-aa06-df4d9d067b42___t5-efficient-mini-nl8,HF
5991,malay-huggingface/bert-tiny-bahasa-cased,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,47687b48-3bbe-4149-b020-890fdced501d___bert-tiny-bahasa-cased,HF
5992,mwesner/reformer-clm,T5-Efficient-LARGE-NH4 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-large-nh4,0.0495357103645801,-1.0,NLP,2d7f5f66-f657-4256-89b2-641649cc4df7___reformer-clm,HF
5993,SupriyaArun/squeezebert-uncased-finetuned-squad,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0381103456020355,-1.0,NLP,c5f0975c-d070-4302-bf25-0cdf690ea581___squeezebert-uncased-finetuned-squad,HF
5994,google/t5-efficient-large-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,0.045258479192853,-1.0,NLP,53cd59f8-524c-4242-80b0-9ae2196acfc6___t5-efficient-large-nl16,HF
5995,hfl/rbtl3,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,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
5996,PlanTL-GOB-ES/gpt2-base-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-base-bne,1.0,-1.0,NLP,e4e093bc-586b-4573-9dda-0dbb9d625b26___gpt2-base-bne,HF
5997,google/t5-efficient-base-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,0.0826821215450763,-1.0,NLP,a9b2eeee-0a53-4cd2-aef5-11eafba55af7___t5-efficient-base-kv32,HF
5998,TJMUCH/transcriptome-iseeek,This casual language model was trained from scratch on CNN/Dailymail dataset,mwesner/reformer-clm,0.0848050713539123,-1.0,NLP,378ea0dd-fc1d-4515-a98b-585908bf6deb___transcriptome-iseeek,HF
5999,google/t5-efficient-small-el32,"In the widget on the right, this parameter is set to its default value: ""This example is {}",Recognai/zeroshot_selectra_small,0.0031421212479472,-1.0,NLP,d6ac3026-c8f7-4bb7-a4b2-1cc71adddc03___t5-efficient-small-el32,HF
6000,aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,"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,0.0901381373405456,-1.0,NLP,22bae21a-df98-4c2f-b2b6-d0c5c4391f1b___bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,HF
6001,cammy/led-large-16384-arxiv-100-MDS,"Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google",flax-community/bengali-t5-base,0.0475187934935092,-1.0,NLP,1bb2b187-e22f-4c8c-9d9a-a934a2fa2a32___led-large-16384-arxiv-100-MDS,HF
6002,mimicheng/codeparrot-ds,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,59cc221c-1973-4347-bae8-b4a34919d110___codeparrot-ds,HF
6003,google/t5-efficient-large-el2,Sentence embeddings can be produced as follows:,RabotaRu/HRBert-mini,0.0,-1.0,NLP,e05273f7-8fd3-4d69-8b26-1c993cd6dac1___t5-efficient-large-el2,HF
6004,google/t5-efficient-base-kv128,T5-Efficient-BASE-KV128 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-kv128,1.0,-1.0,NLP,78626406-477c-42ca-b8fc-9ed1515926c8___t5-efficient-base-kv128,HF
6005,rdenadai/BR_BERTo,T5-Efficient-BASE-DM256 is a variation of Google's original T5 following the T5 model architecture,google/t5-efficient-base-dm256,0.0213422818730274,-1.0,NLP,bb1b2f8c-8f96-47d4-98f2-393e6dabea68___BR_BERTo,HF
6006,google/t5-efficient-tiny-nl32,"All training was done on a Tesla P100, provided by Kaggle",bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,0.0711688660085201,-1.0,NLP,df85fe45-2ba5-4fd9-9d33-0f6d04d5e4b5___t5-efficient-tiny-nl32,HF
6007,superspray/electra_large_discriminator_squad2_custom_dataset,It was  introduced in this paper and first released in this repository,google/fnet-large,0.0,-1.0,NLP,281bb01a-865e-4b26-aac6-f8e5a8685e1e___electra_large_discriminator_squad2_custom_dataset,HF
6008,mimi/Waynehills-NLP-doogie,For more details follow the documentation,TransQuest/monotransquest-hter-en_lv-it-nmt,0.0902962908148765,-1.0,NLP,25ad5d85-d42b-4669-8858-6c3237a87ae7___Waynehills-NLP-doogie,HF
6009,ml6team/distilbart-tos-summarizer-tosdr,"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,0.0396838441956787,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
6010,tau/tavbert-he,Its original repository can be found here,tau/splinter-large-qass,0.0,-1.0,NLP,a2e43ad1-3e22-4b56-be61-5d28217c899f___tavbert-he,HF
6011,malay-huggingface/xlnet-base-bahasa-cased,Note that I (the uploader) am not the author of the paper,filco306/gpt2-switchboard-paraphraser,0.0,-1.0,NLP,032872ad-3371-47ee-9a11-74206e3b3f12___xlnet-base-bahasa-cased,HF
6012,qarib/bert-base-qarib_far_6500k,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,0.0310358442366123,-1.0,NLP,15ba26f7-adf5-4864-ab58-78eccb86bde4___bert-base-qarib_far_6500k,HF
6013,ml6team/distilbart-tos-summarizer-tosdr,"Italians use the word ""dialect"" to describe their regional languages, but they are separate languages",ErykWdowiak/GPTalian,0.0006871866062283,-1.0,NLP,02094426-2017-4898-a64f-757434791be7___distilbart-tos-summarizer-tosdr,HF
6014,hfl/rbtl3,ConvBERT was introduced in this paper and first released at this page,Finnish-NLP/convbert-base-generator-finnish,0.0764307342469692,-1.0,NLP,fc27f54a-880c-44e8-b962-9e8ea30a9582___rbtl3,HF
6015,everdoubling/byt5-Korean-small,"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,1.0,-1.0,NLP,a1f7c541-ac5c-4289-b1e9-08df38b01ad7___byt5-Korean-small,HF
6016,uer/t5-v1_1-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,0.0,-1.0,NLP,9d676abd-ae1c-42ef-a396-aa93b8fcc52f___t5-v1_1-base-chinese-cluecorpussmall,HF
6017,damlab/GO-language,It allows both an exploration of gene-level similarities as well as comparisons between functional terms,damlab/GO-language,1.0,-1.0,NLP,614a14cf-870e-4869-b685-fb9811ac6b2d___GO-language,HF
