index.rst

Transformers
=======================================================================================================================

State-of-the-art Natural Language Processing for Jax, Pytorch and TensorFlow

🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides general-purpose
architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural
Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between Jax,
PyTorch and TensorFlow.

This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`__. You can
also follow our `online course <https://huggingface.co/course>`__ that teaches how to use this library, as well as the
other libraries developed by Hugging Face and the Hub.

If you are looking for custom support from the Hugging Face team
-----------------------------------------------------------------------------------------------------------------------

.. raw:: html

    <a target="_blank" href="https://huggingface.co/support">
        <img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
    </a><br>

Features
-----------------------------------------------------------------------------------------------------------------------

- High performance on NLU and NLG tasks
- Low barrier to entry for educators and practitioners

State-of-the-art NLP for everyone:

- Deep learning researchers
- Hands-on practitioners
- AI/ML/NLP teachers and educators

..
    Copyright 2020 The HuggingFace Team. All rights reserved.

    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
    the License. You may obtain a copy of the License at

        http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
    specific language governing permissions and limitations under the License.

Lower compute costs, smaller carbon footprint:

- Researchers can share trained models instead of always retraining
- Practitioners can reduce compute time and production costs
- 8 architectures with over 30 pretrained models, some in more than 100 languages

Choose the right framework for every part of a model's lifetime:

- Train state-of-the-art models in 3 lines of code
- Deep interoperability between Jax, Pytorch and TensorFlow models
- Move a single model between Jax/PyTorch/TensorFlow frameworks at will
- Seamlessly pick the right framework for training, evaluation, production

The support for Jax is still experimental (with a few models right now), expect to see it grow in the coming months!

`All the model checkpoints <https://huggingface.co/models>`__ are seamlessly integrated from the huggingface.co `model
hub <https://huggingface.co>`__ where they are uploaded directly by `users <https://huggingface.co/users>`__ and
`organizations <https://huggingface.co/organizations>`__.

Current number of checkpoints: |checkpoints|

.. |checkpoints| image:: https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen

Contents
-----------------------------------------------------------------------------------------------------------------------

The documentation is organized in five parts:

- **GET STARTED** contains a quick tour, the installation instructions and some useful information about our philosophy
  and a glossary.
- **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
- **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general research in
  transformers model
- The three last section contain the documentation of each public class and function, grouped in:

    - **MAIN CLASSES** for the main classes exposing the important APIs of the library.
    - **MODELS** for the classes and functions related to each model implemented in the library.
    - **INTERNAL HELPERS** for the classes and functions we use internally.

The library currently contains Jax, PyTorch and Tensorflow implementations, pretrained model weights, usage scripts and
conversion utilities for the following models.

Supported models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

..
    This list is updated automatically from the README with `make fix-copies`. Do not update manually!

1. :doc:`ALBERT <model_doc/albert>` (from Google Research and the Toyota Technological Institute at Chicago) released
   with the paper `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
   <https://arxiv.org/abs/1909.11942>`__, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush
   Sharma, Radu Soricut.
2. :doc:`BART <model_doc/bart>` (from Facebook) released with the paper `BART: Denoising Sequence-to-Sequence
   Pre-training for Natural Language Generation, Translation, and Comprehension
   <https://arxiv.org/pdf/1910.13461.pdf>`__ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
   Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
3. :doc:`BARThez <model_doc/barthez>` (from École polytechnique) released with the paper `BARThez: a Skilled Pretrained
   French Sequence-to-Sequence Model <https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P.
   Tixier, Michalis Vazirgiannis.
4. :doc:`BARTpho <model_doc/bartpho>` (from VinAI Research) released with the paper `BARTpho: Pre-trained
   Sequence-to-Sequence Models for Vietnamese <https://arxiv.org/abs/2109.09701>`__ by Nguyen Luong Tran, Duong Minh Le
   and Dat Quoc Nguyen.
5. :doc:`BEiT <model_doc/beit>` (from Microsoft) released with the paper `BEiT: BERT Pre-Training of Image Transformers
   <https://arxiv.org/abs/2106.08254>`__ by Hangbo Bao, Li Dong, Furu Wei.
6. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
   Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ by Jacob Devlin, Ming-Wei Chang,
   Kenton Lee and Kristina Toutanova.
7. :doc:`BERTweet <model_doc/bertweet>` (from VinAI Research) released with the paper `BERTweet: A pre-trained language
   model for English Tweets <https://aclanthology.org/2020.emnlp-demos.2/>`__ by Dat Quoc Nguyen, Thanh Vu and Anh Tuan
   Nguyen.
8. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
   Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
   Narayan, Aliaksei Severyn.
9. :doc:`BigBird-RoBERTa <model_doc/bigbird>` (from Google Research) released with the paper `Big Bird: Transformers
   for Longer Sequences <https://arxiv.org/abs/2007.14062>`__ by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua
   Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
10. :doc:`BigBird-Pegasus <model_doc/bigbird_pegasus>` (from Google Research) released with the paper `Big Bird:
    Transformers for Longer Sequences <https://arxiv.org/abs/2007.14062>`__ by Manzil Zaheer, Guru Guruganesh, Avinava
    Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr
    Ahmed.
11. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
    open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
    Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
12. :doc:`BlenderbotSmall <model_doc/blenderbot_small>` (from Facebook) released with the paper `Recipes for building
    an open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju,
    Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
13. :doc:`BORT <model_doc/bort>` (from Alexa) released with the paper `Optimal Subarchitecture Extraction For BERT
    <https://arxiv.org/abs/2010.10499>`__ by Adrian de Wynter and Daniel J. Perry.
14. :doc:`ByT5 <model_doc/byt5>` (from Google Research) released with the paper `ByT5: Towards a token-free future with
    pre-trained byte-to-byte models <https://arxiv.org/abs/2105.13626>`__ by Linting Xue, Aditya Barua, Noah Constant,
    Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
15. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
    French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz
    Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
16. :doc:`CANINE <model_doc/canine>` (from Google Research) released with the paper `CANINE: Pre-training an Efficient
    Tokenization-Free Encoder for Language Representation <https://arxiv.org/abs/2103.06874>`__ by Jonathan H. Clark,
    Dan Garrette, Iulia Turc, John Wieting.
17. :doc:`CLIP <model_doc/clip>` (from OpenAI) released with the paper `Learning Transferable Visual Models From
    Natural Language Supervision <https://arxiv.org/abs/2103.00020>`__ by Alec Radford, Jong Wook Kim, Chris Hallacy,
    Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen
    Krueger, Ilya Sutskever.
18. :doc:`ConvBERT <model_doc/convbert>` (from YituTech) released with the paper `ConvBERT: Improving BERT with
    Span-based Dynamic Convolution <https://arxiv.org/abs/2008.02496>`__ by Zihang Jiang, Weihao Yu, Daquan Zhou,
    Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
19. :doc:`CPM <model_doc/cpm>` (from Tsinghua University) released with the paper `CPM: A Large-scale Generative
    Chinese Pre-trained Language Model <https://arxiv.org/abs/2012.00413>`__ by Zhengyan Zhang, Xu Han, Hao Zhou, Pei
    Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng,
    Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang,
    Juanzi Li, Xiaoyan Zhu, Maosong Sun.
20. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
    Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish Shirish Keskar*, Bryan McCann*,
    Lav R. Varshney, Caiming Xiong and Richard Socher.
21. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft) released with the paper `DeBERTa: Decoding-enhanced BERT with
    Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu
    Chen.
22. :doc:`DeBERTa-v2 <model_doc/deberta_v2>` (from Microsoft) released with the paper `DeBERTa: Decoding-enhanced BERT
    with Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao,
    Weizhu Chen.
23. :doc:`DeiT <model_doc/deit>` (from Facebook) released with the paper `Training data-efficient image transformers &
    distillation through attention <https://arxiv.org/abs/2012.12877>`__ by Hugo Touvron, Matthieu Cord, Matthijs
    Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
24. :doc:`DETR <model_doc/detr>` (from Facebook) released with the paper `End-to-End Object Detection with Transformers
    <https://arxiv.org/abs/2005.12872>`__ by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier,
    Alexander Kirillov, Sergey Zagoruyko.
25. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
    Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe
    Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
26. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
    distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__ by Victor
    Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2
    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
    `DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German
    version of DistilBERT.
27. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
    Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick
    Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
28. :doc:`EncoderDecoder <model_doc/encoderdecoder>` (from Google Research) released with the paper `Leveraging
    Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
    Narayan, Aliaksei Severyn.
29. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
    Pre-training text encoders as discriminators rather than generators <https://arxiv.org/abs/2003.10555>`__ by Kevin
    Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
30. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
    Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne,
    Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
31. :doc:`FNet <model_doc/fnet>` (from Google Research) released with the paper `FNet: Mixing Tokens with Fourier
    Transforms <https://arxiv.org/abs/2105.03824>`__ by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago
    Ontanon.
32. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
    Filtering out Sequential Redundancy for Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__ by
    Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
33. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
    Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec Radford, Karthik Narasimhan, Tim Salimans
    and Ilya Sutskever.
34. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
    Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
    Luan, Dario Amodei** and Ilya Sutskever**.
35. :doc:`GPT-J <model_doc/gptj>` (from EleutherAI) released in the repository `kingoflolz/mesh-transformer-jax
    <https://github.com/kingoflolz/mesh-transformer-jax/>`__ by Ben Wang and Aran Komatsuzaki.
36. :doc:`GPT Neo <model_doc/gpt_neo>` (from EleutherAI) released in the repository `EleutherAI/gpt-neo
    <https://github.com/EleutherAI/gpt-neo>`__ by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
37. :doc:`Hubert <model_doc/hubert>` (from Facebook) released with the paper `HuBERT: Self-Supervised Speech
    Representation Learning by Masked Prediction of Hidden Units <https://arxiv.org/abs/2106.07447>`__ by Wei-Ning Hsu,
    Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
38. :doc:`I-BERT <model_doc/ibert>` (from Berkeley) released with the paper `I-BERT: Integer-only BERT Quantization
    <https://arxiv.org/abs/2101.01321>`__ by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
39. `ImageGPT <https://huggingface.co/transformers/master/model_doc/imagegpt.html>`__ (from OpenAI) released with the
    paper `Generative Pretraining from Pixels <https://openai.com/blog/image-gpt/>`__ by Mark Chen, Alec Radford, Rewon
    Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
40. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
    of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
    Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
41. :doc:`LayoutLMv2 <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutLMv2:
    Multi-modal Pre-training for Visually-Rich Document Understanding <https://arxiv.org/abs/2012.14740>`__ by Yang Xu,
    Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min
    Zhang, Lidong Zhou.
42. :doc:`LayoutXLM <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutXLM:
    Multimodal Pre-training for Multilingual Visually-rich Document Understanding <https://arxiv.org/abs/2104.08836>`__
    by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
43. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
    <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
44. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
    Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
45. :doc:`LUKE <model_doc/luke>` (from Studio Ousia) released with the paper `LUKE: Deep Contextualized Entity
    Representations with Entity-aware Self-attention <https://arxiv.org/abs/2010.01057>`__ by Ikuya Yamada, Akari Asai,
    Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
46. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
    Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
    by Hao Tan and Mohit Bansal.
47. :doc:`M2M100 <model_doc/m2m_100>` (from Facebook) released with the paper `Beyond English-Centric Multilingual
    Machine Translation <https://arxiv.org/abs/2010.11125>`__ by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma,
    Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal,
    Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
48. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
    Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
    Translator Team.
49. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
    Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
    Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
50. :doc:`MBart-50 <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Translation with Extensible
    Multilingual Pretraining and Finetuning <https://arxiv.org/abs/2008.00401>`__ by Yuqing Tang, Chau Tran, Xian Li,
    Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
51. :doc:`Megatron-BERT <model_doc/megatron_bert>` (from NVIDIA) released with the paper `Megatron-LM: Training
    Multi-Billion Parameter Language Models Using Model Parallelism <https://arxiv.org/abs/1909.08053>`__ by Mohammad
    Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
52. :doc:`Megatron-GPT2 <model_doc/megatron_gpt2>` (from NVIDIA) released with the paper `Megatron-LM: Training
    Multi-Billion Parameter Language Models Using Model Parallelism <https://arxiv.org/abs/1909.08053>`__ by Mohammad
    Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
53. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
    Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
    Jianfeng Lu, Tie-Yan Liu.
54. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
    text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
    Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
55. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
    Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__ by Jingqing Zhang, Yao Zhao,
    Mohammad Saleh and Peter J. Liu.
56. `Perceiver IO <https://huggingface.co/transformers/model_doc/master/perceiver.html>`__ (from Deepmind) released
    with the paper `Perceiver IO: A General Architecture for Structured Inputs & Outputs
    <https://arxiv.org/abs/2107.14795>`__ by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch,
    Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M.
    Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
57. :doc:`PhoBERT <model_doc/phobert>` (from VinAI Research) released with the paper `PhoBERT: Pre-trained language
    models for Vietnamese <https://www.aclweb.org/anthology/2020.findings-emnlp.92/>`__ by Dat Quoc Nguyen and Anh Tuan
    Nguyen.
58. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
    Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
    Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
59. :doc:`QDQBert <model_doc/qdqbert>` (from NVIDIA) released with the paper `Integer Quantization for Deep Learning
    Inference: Principles and Empirical Evaluation <https://arxiv.org/abs/2004.09602>`__ by Hao Wu, Patrick Judd,
    Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
60. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
    Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
61. :doc:`RemBERT <model_doc/rembert>` (from Google Research) released with the paper `Rethinking embedding coupling in
    pre-trained language models <https://arxiv.org/pdf/2010.12821.pdf>`__ by Hyung Won Chung, Thibault Févry, Henry
    Tsai, M. Johnson, Sebastian Ruder.
62. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
    Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
    Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
63. :doc:`RoFormer <model_doc/roformer>` (from ZhuiyiTechnology), released together with the paper a `RoFormer:
    Enhanced Transformer with Rotary Position Embedding <https://arxiv.org/pdf/2104.09864v1.pdf>`__ by Jianlin Su and
    Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
64. :doc:`SegFormer <model_doc/segformer>` (from NVIDIA) released with the paper `SegFormer: Simple and Efficient
    Design for Semantic Segmentation with Transformers <https://arxiv.org/abs/2105.15203>`__ by Enze Xie, Wenhai Wang,
    Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
65. :doc:`SEW <model_doc/sew>` (from ASAPP) released with the paper `Performance-Efficiency Trade-offs in Unsupervised
    Pre-training for Speech Recognition <https://arxiv.org/abs/2109.06870>`__ by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu
    Han, Kilian Q. Weinberger, Yoav Artzi.
66. :doc:`SEW-D <model_doc/sew_d>` (from ASAPP) released with the paper `Performance-Efficiency Trade-offs in
    Unsupervised Pre-training for Speech Recognition <https://arxiv.org/abs/2109.06870>`__ by Felix Wu, Kwangyoun Kim,
    Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
67. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
    `fairseq S2T: Fast Speech-to-Text Modeling with fairseq <https://arxiv.org/abs/2010.05171>`__ by Changhan Wang, Yun
    Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
68. :doc:`SpeechToTextTransformer2 <model_doc/speech_to_text_2>` (from Facebook), released together with the paper
    `Large-Scale Self- and Semi-Supervised Learning for Speech Translation <https://arxiv.org/abs/2104.06678>`__ by
    Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
69. :doc:`Splinter <model_doc/splinter>` (from Tel Aviv University), released together with the paper `Few-Shot
    Question Answering by Pretraining Span Selection <https://arxiv.org/abs/2101.00438>`__ by Ori Ram, Yuval Kirstain,
    Jonathan Berant, Amir Globerson, Omer Levy.
70. :doc:`SqueezeBert <model_doc/squeezebert>` (from Berkeley) released with the paper `SqueezeBERT: What can computer
    vision teach NLP about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola,
    Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
71. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
    Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
    Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
72. :doc:`T5v1.1 <model_doc/t5v1.1>` (from Google AI) released in the repository
    `google-research/text-to-text-transfer-transformer
    <https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511>`__ by
    Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi
    Zhou and Wei Li and Peter J. Liu.
73. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
    Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
    Francesco Piccinno and Julian Martin Eisenschlos.
74. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
    Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
    Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
75. :doc:`TrOCR <model_doc/trocr>` (from Microsoft), released together with the paper `TrOCR: Transformer-based Optical
    Character Recognition with Pre-trained Models <https://arxiv.org/abs/2109.10282>`__ by Minghao Li, Tengchao Lv, Lei
    Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
76. :doc:`UniSpeech <model_doc/unispeech>` (from Microsoft Research) released with the paper `UniSpeech: Unified Speech
    Representation Learning with Labeled and Unlabeled Data <https://arxiv.org/abs/2101.07597>`__ by Chengyi Wang, Yu
    Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
77. :doc:`UniSpeechSat <model_doc/unispeech_sat>` (from Microsoft Research) released with the paper `UNISPEECH-SAT:
    UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING <https://arxiv.org/abs/2110.05752>`__ by
    Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li,
    Xiangzhan Yu.
78. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
    Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`__ by Alexey Dosovitskiy,
    Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias
    Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
79. :doc:`VisualBERT <model_doc/visual_bert>` (from UCLA NLP) released with the paper `VisualBERT: A Simple and
    Performant Baseline for Vision and Language <https://arxiv.org/pdf/1908.03557>`__ by Liunian Harold Li, Mark
    Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
80. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
    Self-Supervised Learning of Speech Representations <https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry
    Zhou, Abdelrahman Mohamed, Michael Auli.
81. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
    Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
82. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
    Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
    Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
83. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
    Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
    Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
    Zettlemoyer and Veselin Stoyanov.
84. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
    Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
    Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
85. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
    Cross-Lingual Representation Learning For Speech Recognition <https://arxiv.org/abs/2006.13979>`__ by Alexis
    Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.


Supported frameworks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The table below represents the current support in the library for each of those models, whether they have a Python
tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via
Flax), PyTorch, and/or TensorFlow.

..
    This table is updated automatically from the auto modules with `make fix-copies`. Do not update manually!

.. rst-class:: center-aligned-table

+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            Model            | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
+=============================+================+================+=================+====================+==============+
|           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            BEiT             |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            BERT             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|       Bert Generation       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           BigBird           |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|       BigBirdPegasus        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         Blenderbot          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|       BlenderbotSmall       |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           Canine            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            CLIP             |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          ConvBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           DeBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         DeBERTa-v2          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            DeiT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            DETR             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
| FairSeq Machine-Translation |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          FlauBERT           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            FNet             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|     Funnel Transformer      |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           GPT Neo           |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            GPT-J            |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          ImageGPT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         LayoutLMv2          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|             LED             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            LUKE             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           LXMERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           M2M100            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            mBART            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|        MegatronBert         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            MPNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          Perceiver          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           QDQBert           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          RoFormer           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          SegFormer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|             SEW             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            SEW-D            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|   Speech Encoder decoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         Speech2Text         |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|        Speech2Text2         |       ✅       |       ❌       |       ❌        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          Splinter           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            TAPAS            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            TrOCR            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          UniSpeech          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|        UniSpeechSat         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|   Vision Encoder decoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|    VisionTextDualEncoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         VisualBert          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|             ViT             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|        XLMProphetNet        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

.. toctree::
    :maxdepth: 2
    :caption: Get started

    quicktour
    installation
    philosophy
    glossary

.. toctree::
    :maxdepth: 2
    :caption: Using 🤗 Transformers

    task_summary
    model_summary
    preprocessing
    training
    model_sharing
    tokenizer_summary
    multilingual

.. toctree::
    :maxdepth: 2
    :caption: Advanced guides

    pretrained_models
    examples
    troubleshooting
    custom_datasets
    notebooks
    sagemaker
    community
    converting_tensorflow_models
    migration
    contributing
    add_new_model
    add_new_pipeline
    fast_tokenizers
    performance
    parallelism
    testing
    debugging
    serialization
    pr_checks

.. toctree::
    :maxdepth: 2
    :caption: Research

    bertology
    perplexity
    benchmarks

.. toctree::
    :maxdepth: 2
    :caption: Main Classes

    main_classes/callback
    main_classes/configuration
    main_classes/data_collator
    main_classes/keras_callbacks
    main_classes/logging
    main_classes/model
    main_classes/optimizer_schedules
    main_classes/output
    main_classes/pipelines
    main_classes/processors
    main_classes/tokenizer
    main_classes/trainer
    main_classes/deepspeed
    main_classes/feature_extractor

.. toctree::
    :maxdepth: 2
    :caption: Models

    model_doc/albert
    model_doc/auto
    model_doc/bart
    model_doc/barthez
    model_doc/bartpho
    model_doc/beit
    model_doc/bert
    model_doc/bertweet
    model_doc/bertgeneration
    model_doc/bert_japanese
    model_doc/bigbird
    model_doc/bigbird_pegasus
    model_doc/blenderbot
    model_doc/blenderbot_small
    model_doc/bort
    model_doc/byt5
    model_doc/camembert
    model_doc/canine
    model_doc/clip
    model_doc/convbert
    model_doc/cpm
    model_doc/ctrl
    model_doc/deberta
    model_doc/deberta_v2
    model_doc/deit
    model_doc/detr
    model_doc/dialogpt
    model_doc/distilbert
    model_doc/dpr
    model_doc/electra
    model_doc/encoderdecoder
    model_doc/flaubert
    model_doc/fnet
    model_doc/fsmt
    model_doc/funnel
    model_doc/herbert
    model_doc/ibert
    model_doc/imagegpt
    model_doc/layoutlm
    model_doc/layoutlmv2
    model_doc/layoutxlm
    model_doc/led
    model_doc/longformer
    model_doc/luke
    model_doc/lxmert
    model_doc/marian
    model_doc/m2m_100
    model_doc/mbart
    model_doc/megatron_bert
    model_doc/megatron_gpt2
    model_doc/mobilebert
    model_doc/mpnet
    model_doc/mt5
    model_doc/gpt
    model_doc/gpt2
    model_doc/gptj
    model_doc/gpt_neo
    model_doc/hubert
    model_doc/pegasus
    model_doc/perceiver
    model_doc/phobert
    model_doc/prophetnet
    model_doc/qdqbert
    model_doc/rag
    model_doc/reformer
    model_doc/rembert
    model_doc/retribert
    model_doc/roberta
    model_doc/roformer
    model_doc/segformer
    model_doc/sew
    model_doc/sew_d
    model_doc/speechencoderdecoder
    model_doc/speech_to_text
    model_doc/speech_to_text_2
    model_doc/splinter
    model_doc/squeezebert
    model_doc/t5
    model_doc/t5v1.1
    model_doc/tapas
    model_doc/transformerxl
    model_doc/trocr
    model_doc/unispeech
    model_doc/unispeech_sat
    model_doc/visionencoderdecoder
    model_doc/vision_text_dual_encoder
    model_doc/vit
    model_doc/visual_bert
    model_doc/wav2vec2
    model_doc/xlm
    model_doc/xlmprophetnet
    model_doc/xlmroberta
    model_doc/xlnet
    model_doc/xlsr_wav2vec2

.. toctree::
    :maxdepth: 2
    :caption: Internal Helpers

    internal/modeling_utils
    internal/pipelines_utils
    internal/tokenization_utils
    internal/trainer_utils
    internal/generation_utils
    internal/file_utils