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* Work on tokenizer summary * Finish tutorial * Link to it * Apply suggestions from code review Co-authored-by: Anthony MOI <xn1t0x@gmail.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Add vocab definition Co-authored-by: Anthony MOI <xn1t0x@gmail.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
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ReStructuredText
202 lines
11 KiB
ReStructuredText
Transformers
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================================================================================================================================================
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State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
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🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides general-purpose
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architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural
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Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
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TensorFlow 2.0 and PyTorch.
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This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`_.
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Features
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---------------------------------------------------
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- High performance on NLU and NLG tasks
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- Low barrier to entry for educators and practitioners
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State-of-the-art NLP for everyone:
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- Deep learning researchers
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- Hands-on practitioners
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- AI/ML/NLP teachers and educators
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Lower compute costs, smaller carbon footprint:
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- Researchers can share trained models instead of always retraining
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- Practitioners can reduce compute time and production costs
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- 8 architectures with over 30 pretrained models, some in more than 100 languages
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Choose the right framework for every part of a model's lifetime:
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- Train state-of-the-art models in 3 lines of code
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- Deep interoperability between TensorFlow 2.0 and PyTorch models
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- Move a single model between TF2.0/PyTorch frameworks at will
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- Seamlessly pick the right framework for training, evaluation, production
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Contents
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---------------------------------
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The documentation is organized in five parts:
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- **GET STARTED** contains a quick tour, the installation instructions and some useful information about our philosophy
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and a glossary.
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- **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
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- **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
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- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general resarch in
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transformers model
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- **PACKAGE REFERENCE** contains the documentation of each public class and function.
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The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and
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conversion utilities for the following models:
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1. `BERT <https://github.com/google-research/bert>`_ (from Google) released with the paper `BERT: Pre-training of Deep
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Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei
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Chang, Kenton Lee, and Kristina Toutanova.
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2. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (from OpenAI) released with the paper `Improving Language
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Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised>`_ by Alec Radford, Karthik
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Narasimhan, Tim Salimans, and Ilya Sutskever.
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3. `GPT-2 <https://blog.openai.com/better-language-models>`_ (from OpenAI) released with the paper `Language Models are
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Unsupervised Multitask Learners <https://blog.openai.com/better-language-models>`_ by Alec Radford, Jeffrey Wu,
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Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever.
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4. `Transformer-XL <https://github.com/kimiyoung/transformer-xl>`_ (from Google/CMU) released with the paper
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`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_ by
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Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov.
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5. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `XLNet: Generalized
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Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang, Zihang
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Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le.
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6. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual
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Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
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7. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with
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the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle
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Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin
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Stoyanov.
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8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together
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with the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
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<https://arxiv.org/abs/1910.01108>`_ by Victor Sanh, Lysandre Debut, and Thomas Wolf. The same method has been
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applied to compress GPT2 into
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`DistilGPT2 <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
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9. `CTRL <https://github.com/pytorch/fairseq/tree/master/examples/ctrl>`_ (from Salesforce), released together with the
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paper `CTRL: A Conditional Transformer Language Model for Controllable Generation
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<https://www.github.com/salesforce/ctrl>`_ by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong,
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and Richard Socher.
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10. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université)
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released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by
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Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la
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Clergerie, Djame Seddah, and Benoît Sagot.
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11. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together with the paper
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`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_
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by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut.
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12. `T5 <https://github.com/google-research/text-to-text-transfer-transformer>`_ (from Google) released with the paper
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`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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<https://arxiv.org/abs/1910.10683>`_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
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Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu.
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13. `XLM-RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_ (from Facebook AI), released together
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with the paper `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`_ by
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Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard
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Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov.
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14. `MMBT <https://github.com/facebookresearch/mmbt/>`_ (from Facebook), released together with the paper a `Supervised
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Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/pdf/1909.02950.pdf>`_ by Douwe Kiela,
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Suvrat Bhooshan, Hamed Firooz, and Davide Testuggine.
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15. `FlauBERT <https://github.com/getalp/Flaubert>`_ (from CNRS) released with the paper `FlauBERT: Unsupervised
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Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`_ by Hang Le, Loïc Vial, Jibril Frej,
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Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, and
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Didier Schwab.
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16. `BART <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_ (from Facebook) released with the paper
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`BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
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<https://arxiv.org/pdf/1910.13461.pdf>`_ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
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Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer.
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17. `ELECTRA <https://github.com/google-research/electra>`_ (from Google Research/Stanford University) released with
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the paper `ELECTRA: Pre-training text encoders as discriminators rather than generators
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<https://arxiv.org/abs/2003.10555>`_ by Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning.
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18. `DialoGPT <https://github.com/microsoft/DialoGPT>`_ (from Microsoft Research) released with the paper `DialoGPT:
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Large-Scale Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`_ by
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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu,
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and Bill Dolan.
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19. `Reformer <https://github.com/google/trax/tree/master/trax/models/reformer>`_ (from Google Research) released with
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the paper `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ by Nikita Kitaev, Łukasz
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Kaiser, and Anselm Levskaya.
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20. `MarianMT <https://marian-nmt.github.io/>`_ (developed by the Microsoft Translator Team) machine translation models
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trained using `OPUS <http://opus.nlpl.eu/>`_ pretrained_models data by Jörg Tiedemann.
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21. `Longformer <https://github.com/allenai/longformer>`_ (from AllenAI) released with the paper `Longformer: The
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Long-Document Transformer <https://arxiv.org/abs/2004.05150>`_ by Iz Beltagy, Matthew E. Peters, and Arman Cohan.
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22. `Other community models <https://huggingface.co/models>`_, contributed by the `community
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<https://huggingface.co/users>`_.
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.. toctree::
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:maxdepth: 2
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:caption: Get started
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quicktour
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installation
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philosophy
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glossary
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.. toctree::
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:maxdepth: 2
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:caption: Using 🤗 Transformers
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task_summary
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model_summary
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preprocessing
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training
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model_sharing
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tokenizer_summary
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multilingual
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.. toctree::
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:maxdepth: 2
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:caption: Advanced guides
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pretrained_models
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examples
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notebooks
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converting_tensorflow_models
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migration
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torchscript
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contributing
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.. toctree::
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:maxdepth: 2
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:caption: Research
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bertology
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benchmarks
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.. toctree::
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:maxdepth: 2
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:caption: Package Reference
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main_classes/configuration
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main_classes/model
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main_classes/tokenizer
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main_classes/pipelines
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main_classes/optimizer_schedules
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main_classes/processors
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main_classes/trainer
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model_doc/auto
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model_doc/encoderdecoder
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model_doc/bert
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model_doc/gpt
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model_doc/transformerxl
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model_doc/gpt2
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model_doc/xlm
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model_doc/xlnet
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model_doc/roberta
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model_doc/distilbert
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model_doc/ctrl
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model_doc/camembert
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model_doc/albert
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model_doc/xlmroberta
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model_doc/flaubert
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model_doc/bart
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model_doc/t5
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model_doc/electra
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model_doc/dialogpt
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model_doc/reformer
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model_doc/marian
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model_doc/longformer
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model_doc/retribert
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model_doc/mobilebert
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