Check and update model list in index.rst automatically (#7527)

* Check and update model list in index.rst automatically

* Check and update model list in index.rst automatically

* Adapt template

docs/source/index.rst

@@ -54,105 +54,117 @@ The documentation is organized in five parts:
 The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and
 conversion utilities for the following models:
 
-1. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together 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, and Radu Soricut.
-2. `BART <https://github.com/pytorch/fairseq/tree/master/examples/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. `BERT <https://github.com/google-research/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.
-4. `BERT For Sequence Generation <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`_
-   (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.
-5. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université)
-   released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by
-   Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la
-   Clergerie, Djame Seddah, and Benoît Sagot.
-6. `CTRL <https://github.com/pytorch/fairseq/tree/master/examples/ctrl>`_ (from Salesforce), released together with the
-   paper `CTRL: A Conditional Transformer Language Model for Controllable Generation
-   <https://www.github.com/salesforce/ctrl>`_ by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong,
-   and Richard Socher.
-7. `DeBERTa <https://huggingface.co/transformers/model_doc/deberta.html>`_ (from Microsoft Research) 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.
-8. `DialoGPT <https://github.com/microsoft/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,
-   and Bill Dolan.
-9. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together
+..
+    This list is updated automatically from the README with `make fix-copies`. Do not update manually!
+
+1. `ALBERT <https://huggingface.co/transformers/model_doc/albert.html>`__ (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. `BART <https://huggingface.co/transformers/model_doc/bart.html>`__ (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. `BERT <https://huggingface.co/transformers/model_doc/bert.html>`__ (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.
+4. `BERT For Sequence Generation <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__ (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.
+5. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`__ (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.
+6. `CTRL <https://huggingface.co/transformers/model_doc/ctrl.html>`__ (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.
+7. `DeBERTa <https://huggingface.co/transformers/model_doc/deberta.html>`__ (from Microsoft Research) 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.
+8. `DialoGPT <https://huggingface.co/transformers/model_doc/dialogpt.html>`__ (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.
+9. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`__ (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>`_.
-10. `DPR <https://github.com/facebookresearch/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
+   <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.
+10. `DPR <https://github.com/facebookresearch/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.
-11. `ELECTRA <https://github.com/google-research/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, and Christopher D. Manning.
-12. `FlauBERT <https://github.com/getalp/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, and
-    Didier Schwab.
-13. `Funnel Transformer <https://github.com/laiguokun/Funnel-Transformer>`_ (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.
-14. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (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.
-15. `GPT-2 <https://blog.openai.com/better-language-models>`_ (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.
-16. `LayoutLM <https://github.com/microsoft/unilm/tree/master/layoutlm>`_ (from Microsoft Research Asia) released with
+11. `ELECTRA <https://huggingface.co/transformers/model_doc/electra.html>`__ (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.
+12. `FlauBERT <https://huggingface.co/transformers/model_doc/flaubert.html>`__ (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.
+13. `Funnel Transformer <https://github.com/laiguokun/Funnel-Transformer>`__ (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.
+14. `GPT <https://huggingface.co/transformers/model_doc/gpt.html>`__ (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.
+15. `GPT-2 <https://huggingface.co/transformers/model_doc/gpt2.html>`__ (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**.
+16. `LayoutLM <https://github.com/microsoft/unilm/tree/master/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.
-17. `Longformer <https://github.com/allenai/longformer>`_ (from AllenAI) released with the paper `Longformer: The
-    Long-Document Transformer <https://arxiv.org/abs/2004.05150>`_ by Iz Beltagy, Matthew E. Peters, and Arman Cohan.
-18. `LXMERT <https://github.com/airsplay/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.
-19. `MarianMT <https://marian-nmt.github.io/>`_ (developed by the Microsoft Translator Team) machine translation models
-    trained using `OPUS <http://opus.nlpl.eu/>`_ pretrained_models data by Jörg Tiedemann.
-20. `MBart <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`_ (from Facebook) released with the paper
-    `Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan
+    <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
+17. `Longformer <https://huggingface.co/transformers/model_doc/longformer.html>`__ (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.
+18. `LXMERT <https://github.com/airsplay/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.
+19. `MarianMT <https://huggingface.co/transformers/model_doc/marian.html>`__ 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.
+20. `MBart <https://github.com/pytorch/fairseq/tree/master/examples/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.
-21. `MMBT <https://github.com/facebookresearch/mmbt/>`_ (from Facebook), released together with the paper a `Supervised
-    Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/pdf/1909.02950.pdf>`_ by Douwe Kiela,
-    Suvrat Bhooshan, Hamed Firooz, and Davide Testuggine.
-22. `Pegasus <https://github.com/google-research/pegasus>`_ (from Google) released with the paper `PEGASUS:
-    Pre-training with Extracted Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`_ by
+21. `MMBT <https://github.com/facebookresearch/mmbt/>`__ (from Facebook), released together with the paper a
+    `Supervised Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/pdf/1909.02950.pdf>`__ by
+    Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
+22. `Pegasus <https://github.com/google-research/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.
-23. `Reformer <https://github.com/google/trax/tree/master/trax/models/reformer>`_ (from Google Research) released with
-    the paper `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ by Nikita Kitaev, Łukasz
-    Kaiser, and Anselm Levskaya.
-24. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/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, and Veselin
-    Stoyanov.
-25. `T5 <https://github.com/google-research/text-to-text-transfer-transformer>`_ (from Google) 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, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
-    Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu.
-26. `Transformer-XL <https://github.com/kimiyoung/transformer-xl>`_ (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, and Ruslan Salakhutdinov.
-27. `XLM <https://github.com/facebookresearch/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.
-28. `XLM-RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_ (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.
-29. `XLNet <https://github.com/zihangdai/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, and Quoc V. Le.
-30. SqueezeBERT (from UC 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.
-31. `Other community models <https://huggingface.co/models>`_, contributed by the `community
-    <https://huggingface.co/users>`_.
+23. `Reformer <https://huggingface.co/transformers/model_doc/reformer.html>`__ (from Google Research) released with the
+    paper `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser,
+    Anselm Levskaya.
+24. `RoBERTa <https://huggingface.co/transformers/model_doc/roberta.html>`__ (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.
+    ultilingual BERT into `DistilmBERT
+    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version of
+    DistilBERT.
+25. `SqueezeBert <https://huggingface.co/transformers/model_doc/squeezebert.html>`__ 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.
+26. `T5 <https://huggingface.co/transformers/model_doc/t5.html>`__ (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.
+27. `Transformer-XL <https://huggingface.co/transformers/model_doc/transformerxl.html>`__ (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.
+28. `XLM <https://huggingface.co/transformers/model_doc/xlm.html>`__ (from Facebook) released together with the paper
+    `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis
+    Conneau.
+29. `XLM-RoBERTa <https://huggingface.co/transformers/model_doc/xlmroberta.html>`__ (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.
+30. `XLNet <https://huggingface.co/transformers/model_doc/xlnet.html>`__ (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.
+31. `Other community models <https://huggingface.co/models>`__, contributed by the `community
+    <https://huggingface.co/users>`__.
 
 .. toctree::
     :maxdepth: 2

templates/adding_a_new_model/README.md

@@ -83,7 +83,7 @@ You can then finish the addition step by adding imports for your classes in the
 - [ ] Edit the PyTorch to TF 2.0 conversion script to add your model in the `convert_pytorch_checkpoint_to_tf2.py`
   file.
 - [ ] Add a mention of your model in the doc: `README.md` and the documentation itself
-  in `docs/source/index.rst` and `docs/source/pretrained_models.rst`.
+  in `docs/source/pretrained_models.rst`. Rune `make fix-copies` to update `docs/source/index.rst` with your changes.
 - [ ] Upload the pretrained weights, configurations and vocabulary files.
 - [ ] Create model card(s) for your models on huggingface.co. For those last two steps, check the
   [model sharing documentation](https://huggingface.co/transformers/model_sharing.html).

utils/check_copies.py

@@ -23,6 +23,7 @@ import tempfile
 # All paths are set with the intent you should run this script from the root of the repo with the command
 # python utils/check_copies.py
 TRANSFORMERS_PATH = "src/transformers"
+PATH_TO_DOCS = "docs/source"
 
 
 def find_code_in_transformers(object_name):
@@ -166,6 +167,113 @@ def check_copies(overwrite: bool = False):
             + diff
             + "\nRun `make fix-copies` or `python utils/check_copies --fix_and_overwrite` to fix them."
         )
+    check_model_list_copy(overwrite=overwrite)
+
+
+def get_model_list():
+    """ Extracts the model list from the README. """
+    # If the introduction or the conclusion of the list change, the prompts may need to be updated.
+    _start_prompt = "🤗 Transformers currently provides the following architectures"
+    _end_prompt = "1. Want to contribute a new model?"
+    with open(os.path.join("README.md"), "r", encoding="utf-8") as f:
+        lines = f.readlines()
+    # Find the start of the list.
+    start_index = 0
+    while not lines[start_index].startswith(_start_prompt):
+        start_index += 1
+    start_index += 1
+
+    result = []
+    current_line = ""
+    end_index = start_index
+
+    while not lines[end_index].startswith(_end_prompt):
+        if lines[end_index].startswith("1."):
+            if len(current_line) > 1:
+                result.append(current_line)
+            current_line = lines[end_index]
+        elif len(lines[end_index]) > 1:
+            current_line = f"{current_line[:-1]} {lines[end_index].lstrip()}"
+        end_index += 1
+    if len(current_line) > 1:
+        result.append(current_line)
+
+    return "".join(result)
+
+
+def split_long_line_with_indent(line, max_per_line, indent):
+    """ Split the `line` so that it doesn't go over `max_per_line` and adds `indent` to new lines. """
+    words = line.split(" ")
+    lines = []
+    current_line = words[0]
+    for word in words[1:]:
+        if len(f"{current_line} {word}") > max_per_line:
+            lines.append(current_line)
+            current_line = " " * indent + word
+        else:
+            current_line = f"{current_line} {word}"
+    lines.append(current_line)
+    return "\n".join(lines)
+
+
+def convert_to_rst(model_list, max_per_line=None):
+    """ Convert `model_list` to rst format. """
+    # Convert **[description](link)** to `description <link>`__
+    model_list = re.sub(r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\*", r"`\1 <\2>`__", model_list)
+
+    # Convert [description](link) to `description <link>`__
+    model_list = re.sub(r"\[([^\]]*)\]\(([^\)]*)\)", r"`\1 <\2>`__", model_list)
+
+    # Enumerate the lines properly
+    lines = model_list.split("\n")
+    result = []
+    for i, line in enumerate(lines):
+        line = re.sub(r"^\s*(\d+)\.", f"{i+1}.", line)
+        # Split the lines that are too long
+        if max_per_line is not None and len(line) > max_per_line:
+            prompt = re.search(r"^(\s*\d+\.\s+)\S", line)
+            indent = len(prompt.groups()[0]) if prompt is not None else 0
+            line = split_long_line_with_indent(line, max_per_line, indent)
+
+        result.append(line)
+    return "\n".join(result)
+
+
+def check_model_list_copy(overwrite=False, max_per_line=119):
+    """ Check the model lists in the README and index.rst are consistent and maybe `overwrite`. """
+    _start_prompt = "    This list is updated automatically from the README"
+    _end_prompt = ".. toctree::"
+    with open(os.path.join(PATH_TO_DOCS, "index.rst"), "r", encoding="utf-8") as f:
+        lines = f.readlines()
+    # Find the start of the list.
+    start_index = 0
+    while not lines[start_index].startswith(_start_prompt):
+        start_index += 1
+    start_index += 1
+
+    end_index = start_index
+    while not lines[end_index].startswith(_end_prompt):
+        end_index += 1
+    end_index -= 1
+
+    while len(lines[start_index]) <= 1:
+        start_index += 1
+    while len(lines[end_index]) <= 1:
+        end_index -= 1
+    end_index += 1
+
+    rst_list = "".join(lines[start_index:end_index])
+    md_list = get_model_list()
+    converted_list = convert_to_rst(md_list, max_per_line=max_per_line)
+
+    if converted_list != rst_list:
+        if overwrite:
+            with open(os.path.join(PATH_TO_DOCS, "index.rst"), "w", encoding="utf-8") as f:
+                f.writelines(lines[:start_index] + [converted_list] + lines[end_index:])
+        else:
+            raise ValueError(
+                "The model list in the README changed and the list in `index.rst` has not been updated. Run `make fix-copies` to fix this."
+            )
 
 
 if __name__ == "__main__":