Abstract
With the advancements in open-source models, training(or finetuning) models on custom datasets has become a crucial part of developing solutions which are tailored to specific industrial or open-source applications. Yet, there is no single tool which simplifies the process of training across different types of modalities or tasks.We introduce AutoTrain(aka AutoTrain Advanced)—an open-source, no code tool/library which can be used to train (or finetune) models for different kinds of tasks such as: large language model (LLM) finetuning, text classification/regression, token classification, sequence-to-sequence task, finetuning of sentence transformers, visual language model (VLM) finetuning, image classification/regression and even classification and regression tasks on tabular data. AutoTrain Advanced is an open-source library providing best practices for training models on custom datasets. The library is available at https://github.com/huggingface/autotrain-advanced. AutoTrain can be used in fully local mode or on cloud machines and works with tens of thousands of models shared on Hugging Face Hub and their variations.- Anthology ID:
- 2024.emnlp-demo.44
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Delia Irazu Hernandez Farias, Tom Hope, Manling Li
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 419–423
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-demo.44/
- DOI:
- 10.18653/v1/2024.emnlp-demo.44
- Cite (ACL):
- Abhishek Thakur. 2024. AutoTrain: No-code training for state-of-the-art models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 419–423, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- AutoTrain: No-code training for state-of-the-art models (Thakur, EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-demo.44.pdf