Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, Alexander Rush
AbstractRecent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at https://github.com/huggingface/transformers.@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Wolf, Thomas and
Debut, Lysandre and
Sanh, Victor and
Chaumond, Julien and
Delangue, Clement and
Moi, Anthony and
Cistac, Pierric and
Rault, Tim and
Louf, Remi and
Funtowicz, Morgan and
Davison, Joe and
Shleifer, Sam and
von Platen, Patrick and
Ma, Clara and
Jernite, Yacine and
Plu, Julien and
Xu, Canwen and
Le Scao, Teven and
Gugger, Sylvain and
Drame, Mariama and
Lhoest, Quentin and
Rush, Alexander",
editor = "Liu, Qun and
Schlangen, David",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-demos.6/",
doi = "10.18653/v1/2020.emnlp-demos.6",
pages = "38--45",
abstract = "Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at \url{https://github.com/huggingface/transformers}."
}
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<abstract>Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at https://github.com/huggingface/transformers.</abstract>
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%0 Conference Proceedings %T Transformers: State-of-the-Art Natural Language Processing %A Wolf, Thomas %A Debut, Lysandre %A Sanh, Victor %A Chaumond, Julien %A Delangue, Clement %A Moi, Anthony %A Cistac, Pierric %A Rault, Tim %A Louf, Remi %A Funtowicz, Morgan %A Davison, Joe %A Shleifer, Sam %A von Platen, Patrick %A Ma, Clara %A Jernite, Yacine %A Plu, Julien %A Xu, Canwen %A Le Scao, Teven %A Gugger, Sylvain %A Drame, Mariama %A Lhoest, Quentin %A Rush, Alexander %Y Liu, Qun %Y Schlangen, David %S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations %D 2020 %8 October %I Association for Computational Linguistics %C Online %F wolf-etal-2020-transformers %X Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at https://github.com/huggingface/transformers. %R 10.18653/v1/2020.emnlp-demos.6 %U https://aclanthology.org/2020.emnlp-demos.6/ %U https://doi.org/10.18653/v1/2020.emnlp-demos.6 %P 38-45Markdown (Informal)
[Transformers: State-of-the-Art Natural Language Processing](https://aclanthology.org/2020.emnlp-demos.6/) (Wolf et al., EMNLP 2020)
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